CN108919008B  Online power quality disturbance identification method and system based on timefrequency database  Google Patents
Online power quality disturbance identification method and system based on timefrequency database Download PDFInfo
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 CN108919008B CN108919008B CN201810776012.1A CN201810776012A CN108919008B CN 108919008 B CN108919008 B CN 108919008B CN 201810776012 A CN201810776012 A CN 201810776012A CN 108919008 B CN108919008 B CN 108919008B
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Abstract
The invention discloses an online power quality disturbance identification method and system based on a timefrequency database. The method comprises the following steps: establishing a timefrequency database of a tree structure according to various disturbance signal characteristic curves; collecting a quality disturbance signal of electric energy to be identified; performing multiresolution hyperbolic S transformation on a disturbance signal to be identified; determining a characteristic curve of the disturbance signal to be identified according to the disturbance signal to be identified after multiresolution hyperbolic S transformation; and comparing the characteristic curve of the disturbance signal to be identified with the characteristic curve in the characteristic curve database, and determining the category of the characteristic curve of the disturbance signal to be identified. The invention aims to provide an online power quality disturbance identification method and system based on a timefrequency database, provides a scientific and reasonable method, is high in accuracy, abandons complex threshold setting and has good application value.
Description
Technical Field
The invention relates to the field of power quality disturbance identification, in particular to an online power quality disturbance identification method and system based on a timefrequency database.
Background
Today, the application of various power electronic devices brings huge challenges to the power grid. Meanwhile, the use of various precise instruments and highprecision control devices also puts higher requirements on the power supply quality of the power grid. As the basis of power quality assessment and treatment, the identification and classification of the power quality composite disturbance are particularly important.
The power quality disturbance identification generally comprises two steps of signal processing and identification and classification. The commonly used power quality disturbance signal processing mainly comprises Fourier transform, wavelet transform, dq transform, S transform, HilbertHuang transform and the like, and has the defects of large amount of threshold calculation, high design requirement on a classifier and low calculation speed.
Disclosure of Invention
The invention aims to provide an online power quality disturbance identification method and system based on a timefrequency database, provides a scientific and reasonable method, is high in accuracy, abandons complex threshold setting and has good application value.
In order to achieve the purpose, the invention provides the following scheme:
an online electric energy quality disturbance identification method based on a timefrequency database is characterized by comprising the following steps:
establishing a timefrequency database of a tree structure according to various disturbance signal characteristic curves, wherein a father node of the tree structure is a frequency domain database, a child node of the tree structure is a time domain database, a frequency amplitude partial curve of the disturbance signal characteristic curve is stored in the frequency domain database, and a fundamental frequency amplitude curve of the disturbance signal characteristic curve is stored in the time domain database;
collecting a quality disturbance signal of electric energy to be identified;
performing multiresolution hyperbolic S transformation on a disturbance signal to be identified;
determining a characteristic curve of the disturbance signal to be identified according to the disturbance signal to be identified after multiresolution hyperbolic S transformation;
and comparing the characteristic curve of the disturbance signal to be identified with the characteristic curve in the characteristic curve database, and determining the category of the characteristic curve of the disturbance signal to be identified.
Optionally, the frequency domain database includes a first frequency domain database, a second frequency domain database, a third frequency domain database, and a fourth frequency domain database, the first frequency domain database includes a plurality of frequency amplitude partial curves generated by the disturbance signals S1, S2, S3, S4, the second frequency domain database includes a plurality of frequency amplitude partial curves generated by the disturbance signals S5, S6, S7, S8, the third frequency domain database includes a plurality of frequency amplitude partial curves generated by the disturbance signals S9, S10, S11, S12, the fourth frequency domain database includes a plurality of frequency amplitude partial curves generated by the disturbance signals S13, S14, S15, S16, the time domain database includes a first time domain database, a second time domain database, a third time domain database, and a fourth time domain database, the first time domain database includes a plurality of disturbance signals S1, S2, S3, The S4 generates a plurality of fundamental frequency amplitude curves respectively, the second time domain database comprises a plurality of fundamental frequency amplitude curves generated by disturbance signals S5, S6, S7 and S8 respectively, the third time domain database comprises a plurality of fundamental frequency amplitude curves generated by disturbance signals S9, S10, S11 and S12 respectively at random, the fourth time domain database comprises a plurality of fundamental frequency amplitude curves generated by disturbance signals S13, S14, S15 and S16 respectively, and the S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 and S16 are 16 disturbance signals.
Optionally, the comparing the characteristic curve of the disturbance signal to be identified with the characteristic curve in the characteristic curve database, and determining the category of the characteristic curve of the disturbance signal to be identified specifically includes:
improving the discrete Fourier distance algorithm to obtain an improved discrete Fourier distance algorithm;
calculating the distance between a frequency amplitude part curve in the characteristic curve of the disturbance signal to be identified and a frequency amplitude part curve in each frequency domain database by adopting an improved discrete Fourier distance algorithm, and determining the frequency amplitude part curve with the minimum distance in the frequency domain database;
determining a frequency domain database to which the frequency amplitude part with the minimum distance belongs to obtain a subordination frequency domain database;
calculating the distance between the fundamental frequency amplitude curve of the disturbance signal to be identified and the fundamental frequency amplitude curve in the time domain database corresponding to the subordinate frequency domain database by adopting an improved discrete Fourier distance algorithm, and determining the fundamental frequency amplitude curve with the minimum distance in the time domain database;
and determining the class of the fundamental frequency amplitude curve with the minimum distance as the class of the disturbance signal to be identified.
Optionally, the performing multiresolution hyperbolic S transform on the disturbance signal to be identified specifically includes:
performing multiresolution hyperbolic S transformation on the lowfrequency part of the disturbance signal to be identified;
performing multiresolution hyperbolic S conversion of an intermediate frequency part on the disturbance signal to be identified;
and performing multiresolution hyperbolic S transformation on the highfrequency part of the disturbance signal to be identified.
Optionally, determining a characteristic curve of the disturbance signal to be identified specifically includes:
selecting a fundamental frequency amplitude curve, wherein the fundamental frequency amplitude curve is a row vector of a multiresolution hyperbolic S transformation mode matrix with the frequency of 50 Hz;
and selecting a frequency amplitude part curve which is the maximum value of each row of the multiresolution Stransform mode matrix, and selecting a range larger than 100Hz on the basis of the frequency amplitude part curve.
An online power quality disturbance identification system based on a timefrequency database, the system comprising:
the treeshaped timefrequency database module is used for establishing a timefrequency line database of a tree structure according to various disturbance signal characteristic curves, wherein a father node of the tree structure is a frequency domain database, a child node of the tree structure is a time domain database, a frequency amplitude partial curve of the disturbance signal characteristic curve is stored in the frequency domain database, and a fundamental frequency amplitude curve of the disturbance signal characteristic curve is stored in the time domain database;
the signal acquisition module is used for acquiring a quality disturbance signal of the electric energy to be identified;
the S conversion module is used for performing multiresolution hyperbolic S conversion on the disturbance signal to be identified;
the characteristic curve extraction module is used for determining a characteristic curve of the disturbance signal to be identified according to the disturbance signal to be identified after the multiresolution hyperbolic S transformation;
and the category identification module is used for comparing the characteristic curve of the disturbance signal to be identified with the characteristic curve in the characteristic curve database and determining the category of the characteristic curve of the disturbance signal to be identified.
Optionally, the category identifying module specifically includes:
the discrete Fourier distance algorithm improving unit is used for improving the discrete Fourier distance algorithm to obtain an improved discrete Fourier distance algorithm;
determining a frequency amplitude part curve unit with the minimum distance, which is used for calculating the distance between the frequency amplitude part curve of the disturbance signal to be identified and the frequency amplitude part curves in each frequency domain database by adopting an improved discrete Fourier distance algorithm, and determining the frequency amplitude part curve with the minimum distance in the frequency domain database;
the membership degree unit is used for determining a frequency domain database to which the frequency amplitude partial curve with the minimum distance belongs to obtain a membership frequency domain database;
determining a fundamental frequency amplitude curve unit with the minimum distance, which is used for calculating the distance between the fundamental frequency amplitude curve of the disturbance signal to be identified and the fundamental frequency amplitude curve in the time domain database corresponding to the subordinate frequency domain database by adopting an improved discrete Fourier distance algorithm, and determining the fundamental frequency amplitude curve with the minimum distance in the time domain database;
and the identification unit is used for determining the class of the fundamental frequency amplitude curve with the minimum distance as the class of the disturbance signal to be identified.
Optionally, the S transformation module specifically includes:
the lowfrequency part S transformation unit is used for performing multiresolution hyperbolic S transformation on the lowfrequency part of the disturbance signal to be identified;
the intermediate frequency part S conversion unit is used for performing multiresolution hyperbolic S conversion of the intermediate frequency part on the disturbance signal to be identified;
and the highfrequency part S conversion unit is used for performing multiresolution hyperbolic S conversion on the highfrequency part of the disturbance signal to be identified.
Optionally, the characteristic curve extracting module specifically includes:
the base frequency amplitude curve selecting unit is used for selecting a base frequency amplitude curve, and the base frequency amplitude curve is a row vector of a multiresolution hyperbolic S transformation mode matrix with the frequency of 50 Hz;
and the frequency amplitude part curve selecting unit is used for selecting a frequency amplitude part curve which is the maximum value of each row of the multiresolution S transformation mode matrix and selecting a range larger than 100Hz on the basis of the frequency amplitude part curve.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention provides a method and a system for identifying power quality disturbance online based on a time frequency database.A time frequency database of a tree structure is established according to various disturbance signal characteristic curves, a father node of the tree structure is a frequency domain database, a child node of the tree structure is a time domain database, a frequency amplitude partial curve of the disturbance signal characteristic curve is stored in the frequency domain database, and a fundamental frequency amplitude curve of the disturbance signal characteristic curve is stored in the time domain database; collecting a quality disturbance signal of electric energy to be identified; performing multiresolution hyperbolic S transformation on a disturbance signal to be identified; determining a characteristic curve of the disturbance signal to be identified according to the disturbance signal to be identified after multiresolution hyperbolic S transformation; and comparing the characteristic curve of the disturbance signal to be identified with the characteristic curve in the characteristic curve database, determining the category of the characteristic curve of the disturbance signal to be identified, wherein the identification rate of the disturbance signal is up to more than 99.3%.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic structural diagram of an online power quality disturbance identification method based on a timefrequency database according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an online power quality disturbance identification system based on a timefrequency database according to an embodiment of the present invention;
FIG. 3 is a flowchart of an online power quality disturbance identification method and system based on a timefrequency database according to an embodiment of the present invention;
FIG. 4 is a characteristic graph of 16 disturbance signals according to the embodiment of the present invention;
FIG. 5 is a treelike timefrequency data diagram according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating a treelike timefrequency database query process according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide an online power quality disturbance identification method and system based on a timefrequency database, provides a scientific and reasonable method, is high in accuracy, abandons complex threshold setting and has good application value.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic structural diagram of an online power quality disturbance identification method based on a timefrequency database according to an embodiment of the present invention, and as shown in fig. 1, the online power quality disturbance identification method based on the timefrequency database provided by the present invention specifically includes the following steps:
step 101: establishing a timefrequency database of a tree structure according to various disturbance signal characteristic curves, wherein a father node of the tree structure is a frequency domain database, a child node of the tree structure is a time domain database, a frequency amplitude partial curve of the disturbance signal characteristic curve is stored in the frequency domain database, and a fundamental frequency amplitude curve of the disturbance signal characteristic curve is stored in the time domain database;
step 102: collecting a quality disturbance signal of electric energy to be identified;
step 103: performing multiresolution hyperbolic S transformation on a disturbance signal to be identified;
step 104: determining a characteristic curve of the disturbance signal to be identified according to the disturbance signal to be identified after multiresolution hyperbolic S transformation;
step 105: and determining a characteristic curve of the disturbance signal to be identified according to the disturbance signal to be identified after the multiresolution hyperbolic S transformation.
Wherein, step 101 specifically includes: the frequency domain database 501 comprises a first frequency domain database, a second frequency domain database, a third frequency domain database and a fourth frequency domain database, wherein the first frequency domain database comprises a plurality of frequency amplitude partial curves generated by disturbance signals S1, S2, S3 and S4, the second frequency domain database comprises a plurality of frequency amplitude partial curves generated by disturbance signals S5, S6, S7 and S8, the third frequency domain database comprises a plurality of frequency amplitude partial curves generated by disturbance signals S9, S10, S11 and S12, the fourth frequency domain database comprises a plurality of frequency amplitude partial curves generated by disturbance signals S13, S14, S15 and S16, the time domain database comprises a first time domain database 502, a second time domain database 503, a third time domain database 504 and a fourth time domain database 505, the first time domain database 502 comprises a plurality of frequency amplitude partial curves generated by disturbance signals S1, S2 and S3, S16, and the first time domain database 502 comprises a plurality of disturbance signals S1, S2 and S3, The S4 generates a plurality of fundamental frequency amplitude curves, the second time domain database 503 includes a plurality of fundamental frequency amplitude curves generated by disturbance signals S5, S6, S7, S8, the third time domain database 504 includes a plurality of fundamental frequency amplitude curves generated by disturbance signals S9, S10, S11, S12, the fourth time domain database 505 includes a plurality of fundamental frequency amplitude curves generated by disturbance signals S13, S14, S15, S16, the S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15, and S16 are 16 kinds of disturbance signals.
Aiming at 16 types of disturbance design timefrequency databases, the timefrequency databases are respectively a voltage interruption S1, a voltage sag S2, a voltage transient rise S3, a flicker S4, a harmonic S5, a transient fall harmonic S6, a transient rise harmonic S7, a flicker harmonic S8, a transient oscillation S9, a transient fall transient oscillation S10, a transient rise transient oscillation S11, a flicker transient oscillation S12, a transient oscillation harmonic S13, a transient oscillation harmonic and voltage sag S14, a transient oscillation harmonic and voltage transient rise S15 and a transient oscillation harmonic and flicker S16. The frequency domain database 501 is a frequency amplitude partial curve, and is composed of four parts, namely a first frequency domain database 1b1, a second frequency domain database 1b2, a third frequency domain database 1b3 and a fourth frequency domain database 1b 4. The time domain database is a fundamental frequency amplitude curve, and is composed of four flat subdatabases, namely a first time domain database 503, a second time domain database 504, a third time domain database 505 and a fourth time domain database 506. The first timedomain database 503 is composed of four parts, 2b11, 2b12, 2b13 and 2b 14; the second time domain database 504 is composed of four parts, 2b21, 2b22, 2b23, 2b 24; the third time domain database consists of four parts, namely 2b31, 2b32, 2b33 and 2b 34; the fourth time domain database is composed of four parts, 2b41, 2b42, 2b43 and 2b 44. The parts of the database are explained as follows:
1b 1: the frequency amplitude part curves of the four disturbance multiresolution hyperbolic S transforms S1, S2, S3 and S4 have the same characteristics that the curves are smooth in level and the curve amplitude is close to zero. The frequency amplitude part curves of the four disturbance multiresolution S transformations of S1, S2, S3 and S4 are taken 50 pieces to form 200 pieces of waveform data, namely 1b 1.
1b 2: the frequency amplitude part curves of the four disturbance multiresolution hyperbolic S transformations of S5, S6, S7 and S8 have the same characteristic that the curves have a plurality of spikes in the intermediate frequency part (100Hz to 900Hz), and other parts are horizontally smooth and the curve amplitude is close to zero. Under random conditions, 50 frequency amplitude partial curves of four disturbance multiresolution S transformations of S5, S6, S7 and S8 are respectively adopted to form 200 pieces of waveform data, namely 1b 2.
1b 3: the frequency amplitude part curves of the four disturbance multiresolution hyperbolic S transforms of S9, S10, S11 and S12 have the same characteristic that the curves have a slowly rising peak in a highfrequency part (above 900Hz), and the other parts are smooth horizontally and the curve amplitude is close to zero. The frequency amplitude part curves of the four disturbance multiresolution S transformations of S9, S10, S11 and S12 are taken 50 pieces to form 200 pieces of waveform data, namely 1b 3.
1b 4: the frequency amplitude part curves of the four disturbance multiresolution hyperbolic S transformations of S13, S14, S15 and S16 have the same characteristics that the curves have multiple peaks in the intermediate frequency part (100Hz to 900Hz) and a slowly rising peak in the high frequency part (above 900Hz), and the other parts are horizontally smooth and the curve amplitude is close to zero. 200 pieces of waveform data are formed by 50 pieces of frequency amplitude partial curves of four disturbance multiresolution hyperbolic S transformation of S13, S14, S15 and S16 respectively, and the data are 1b 4.
2b21, 2b31 and 2b41 are respectively taken from fundamental frequency amplitude curves of three disturbance multiresolution hyperbolic S transformation of S5, S9 and S13, and 50 disturbance are selected for each disturbance. The fundamental frequency amplitude curves of the three disturbance multiresolution hyperbolic S transformations have the same characteristics, namely the amplitude of the fundamental frequency amplitude curves is basically unchanged and is in a horizontal straight line shape.
2b11, 2b12, 2b22, 2b32 and 2b42 are respectively taken from fundamental frequency amplitude curves of multiresolution hyperbolic S transformation of five kinds of disturbances, namely S1, S2, S6, S10 and S14, and 50 disturbances are selected for each kind of disturbance. The fundamental frequency amplitude curves of the five disturbance multiresolution hyperbolic Stransform have the same characteristic that the amplitude of the fundamental frequency amplitude curves firstly falls and then rises to form a concave shape, wherein the concave degrees of the fundamental frequency amplitude curves of S1 and S2 are different.
2b13, 2b23, 2b33 and 2b43 are respectively taken from fundamental frequency amplitude curves of four disturbance multiresolution hyperbolic S transformation of S3, S7, S11 and S15, and 50 disturbance are selected for each kind of disturbance. The four disturbed multiresolution hyperbolic Stransformed fundamental frequency amplitude curves have the same characteristic, namely, the amplitude of the fundamental frequency amplitude curve rises first and then falls, and the fundamental frequency amplitude curve is in an upward convex shape.
2b14, 2b24, 2b34 and 2b44 are respectively taken from fundamental frequency amplitude curves of four disturbance multiresolution hyperbolic S transformation of S4, S8, S12 and S16, and 50 disturbance are selected for each kind of disturbance. The fundamental frequency amplitude curves of the four disturbance multiresolution hyperbolic S transformations have the same characteristics, namely, the amplitude of the fundamental frequency amplitude curves is periodically transformed to present the shape of waves.
Step 102 specifically includes:
and carrying out online acquisition and recording on related disturbance signals by using a voltage transformer, a current transformer and secondary equipment in the transformer substation.
Step 103 specifically comprises:
when S transformation is carried out on the collected disturbance signals, the frequency is divided into three parts for S transformation, and the method specifically comprises the following steps:
performing multiresolution hyperbolic S transformation on the lowfrequency part of the disturbance signal to be identified;
performing multiresolution hyperbolic S conversion of an intermediate frequency part on the disturbance signal to be identified;
performing multiresolution hyperbolic S transformation on the highfrequency part of the disturbance signal to be identified;
the method of multiresolution hyperbolic Stransform is determined by equation (1) and equation (2):
order toτ → jT, the discrete expression of the hyperbolic S transform is as follows:
when n ≠ 0, it is determined,
when n is 0 (corresponding to zero frequency),
wherein j, m, N is 0, 1. G_{H}Discretized expression of (m, n):
n is the number of discrete signals for hyperbolic S transformation; t, acquiring the time of two adjacent points of the signal, namely a sampling period; f is the frequency; t is time; m is used to implement the conversion at different frequencies; the parameter τ is used to control the position of the hyperbolic window on the time axis. The signal is obtained by fast Fourier transformAdding the parameter m to obtainG_{H}(m, n) is a hyperbolic window function; g_{H}The discretization expression of (m, n) is solved by formula (3), and the variable X in formula (3) is solved by formula (4);determining the attenuation degree of the front half curve of the hyperbolic window;determining the attenuation degree of a half curve of the hyperbolic window; lambda [ alpha ]_{HY}The curvature of the hyperbolic window is determined.The increase in (b) attenuates the front and rear halves of the hyperbolic window, thereby changing the shape of the hyperbolic window. By adjustingThe sum of the values enables the window width to change with the frequency in inverse proportion, and the purpose of adjusting the time frequency precision is achieved.
Wherein, when the lowfrequency part S conversion unit is used for conversion, the frequency range of the lowfrequency part is 1Hz and f_{L}N of multiresolution hyperbolic S transform discrete expression less than or equal to 100Hz_{L}The value range of (1) is NT less than or equal to n_{L}The parameter of < 100NT isλ_{HY}And (3) substituting the parameters into the formula (1) to perform S transformation on the lowfrequency part of the signal to be identified.
When the intermediate frequency part S conversion unit performs conversion, the frequency range of the intermediate frequency part is 101Hz and f_{M}Not more than 900Hz, multiresolution hyperbolic S conversion discrete expression n_{M}Has a value range of 101NT ≤ n_{M}A parameter of 900NT or lessλ_{HY}Substituting the above parameters into equation (1) performs Sconversion of the intermediate frequency portion of the signal to be identified, 16.
When the highfrequency part S conversion unit performs conversion, the frequency range of the highfrequency part is 901Hz ≦ f_{H}Less than or equal to 1300Hz, n of discrete expression_{H}The value range of (1) is that 901NT is less than or equal to n_{H}The parameter of < 1300NT isλ_{HY}Substituting the parameters into equation (1) for 32, Stransform the high frequency part of the signal to be identified.
And finally, obtaining a complex matrix as a result of the multiresolution hyperbolic S transformation, and performing modulus operation on each element of the matrix to obtain a modulus matrix of the multiresolution hyperbolic S transformation. The modulo matrix column represents the sampling time and the row represents the frequency.
Step 104, specifically comprising:
selecting a fundamental frequency amplitude curve, wherein the fundamental frequency amplitude curve is a row vector of a multiresolution hyperbolic S transformation mode matrix with the frequency of 50 Hz;
and selecting a frequency amplitude part curve which is the maximum value of each row of the multiresolution Stransform mode matrix, and selecting a range larger than 100Hz on the basis of the frequency amplitude part curve.
Step 105, specifically comprising:
improving the discrete Fourier distance algorithm to obtain an improved discrete Fourier distance algorithm;
calculating the distance between the frequency amplitude part curve of the disturbance signal to be identified and the frequency amplitude part curve in each frequency domain database by adopting an improved discrete Fourier distance algorithm, and determining the frequency amplitude part curve with the minimum distance in the frequency domain database;
determining a frequency domain database to which the frequency amplitude partial curve with the minimum distance belongs to obtain a subordinate frequency domain database;
calculating the distance between the fundamental frequency amplitude curve of the disturbance signal to be identified and the fundamental frequency amplitude curve in the time domain database corresponding to the subordinate frequency domain database by adopting an improved discrete Fourier distance algorithm, and determining the fundamental frequency amplitude curve with the minimum distance in the time domain database;
and determining the class of the fundamental frequency amplitude curve with the minimum distance as the class of the disturbance signal to be identified.
The discrete Fourier distance algorithm is improved to obtain the improved discrete Fourier distance algorithm, and the method specifically comprises the following steps:
first the discrete freschel distance is defined as:
given a polygonal chain of C vertices (or vertices) Q ═ Q_{1},q_{2},...,q_{l}>. The step h along Q partitions the high point (or low point) of Q into h disjoint nonempty subsets { Q_{i}}_{i＝1...k}(1. ltoreq. h. ltoreq.l) such that Q is < Q_{1},q_{2},...,q_{l}> and 0 ═ l_{0}＜l_{1}＜...＜l_{k}＝l。
A. B represents two given polygonal chains, wherein A ═ a_{1},...,a_{e}＞，B＝＜b_{1}，,...b_{r}>. A combined step along A and B is an hstep along A { A }_{i}}_{i＝1...h}And an hstep { B along B_{i}}_{i＝1...h}Composition such that for 1 ≦ i ≦ h, or  A_{i}1 or  B_{i}＝1。
Determining a discrete Frechst distance algorithm, the algorithm being determined by equations (5) and (6):
a is the identified perturbation signal, B is the corresponding perturbation curve in the timefrequency data database, and formula (5) represents a combined step W { (A) along A and B_{i},B_{i}) The cost of (6), formula (6) represents the discrete Freusch distance between A and B, the minimum of which is solved by formula (5) to give formula (6).
Improving the discrete Fourier distance:
the correspondence of the multiple chains A and B due to the discrete Fourier distance only needs to satisfy: or  A_{i}1 or  B_{i}1, wherein  A_{i}I represents A_{i}The middle includes the number of high points,  B_{i}I represents B_{i}Including the number of high spots, psi being a limit A_{i}And B_{i}The number of high points is contained, so that a large number of unnecessary corresponding relations exist, the calculation time is increased, and aiming at the problem, the invention provides a limiting condition (only one of the following conditions needs to be satisfied):
①ψ＞B_{i}＞A_{i}1, the constant psi satisfies C > psi > 1
②ψ＞A_{i}＞B_{i}1, the constant psi satisfies C > psi > 1
③B_{i}＝A_{i}＝1
The membership degree unit specifically comprises:
and inquiring corresponding disturbance signals according to the membership degree:
obtaining partial curves of frequency amplitude values by performing multiresolution hyperbolic S conversion on signals Y measured by the mutual inductor, and calculating the minimum d by comparing the improved discrete Fourier distance with each curve of the frequency domain database (1B)_{H}(A, B) and finding the correspondenceAnd the corresponding portions (1b1, 1b2, 1b3, 1b 4). The signal Y belongs to one of the four types 1b1, 1b2, 1b3 and 1b4 according to the principle of membership. If Y belongs to 1B1, the signal Y is subjected to multiresolution hyperbolic S conversion to obtain a fundamental frequency amplitude curve, and the fundamental frequency amplitude curve is compared with a time domain database 2B 1; if Y belongs to 1B2, the signal Y is subjected to multiresolution hyperbolic S conversion to obtain a fundamental frequency amplitude curve, and the fundamental frequency amplitude curve is compared with a time domain database 2B 2; if Y belongs to 1B3, the signal Y is subjected to multiresolution hyperbolic S conversion to obtain a fundamental frequency amplitude curve, and the fundamental frequency amplitude curve is compared with a time domain database 2B 3; if Y belongs to 1B4, the signal Y is subjected to multiresolution hyperbolic S conversion to obtain a fundamental frequency amplitude curve, and the fundamental frequency amplitude curve is compared with a time domain database 2B 4; finally, if the signal Y belongs to one of the classes 2b11, 2b12, 2b13, 2b14, 2b21, 2b22, 2b23, 2b24, 2b31, 2b32, 2b33, 2b34, 2b41, 2b42, 2b43 and 2b44, the corresponding disturbance in the signals Y is S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 and S16.
The invention provides an online power quality disturbance identification method based on a time frequency database, which comprises the steps of establishing a time frequency database of a tree structure according to various disturbance signal characteristic curves, wherein a father node of the tree structure is a frequency domain database, a child node of the tree structure is a time domain database, a frequency amplitude partial curve of the disturbance signal characteristic curve is stored in the frequency domain database, and a fundamental frequency amplitude curve of the disturbance signal characteristic curve is stored in the time domain database; collecting a quality disturbance signal of electric energy to be identified; performing multiresolution hyperbolic S transformation on a disturbance signal to be identified; determining a characteristic curve of the disturbance signal to be identified according to the disturbance signal to be identified after multiresolution hyperbolic S transformation; and comparing the characteristic curve of the disturbance signal to be identified with the characteristic curve in the characteristic curve database, determining the category of the characteristic curve of the disturbance signal to be identified, wherein the identification rate of the disturbance signal is up to more than 99.3%.
Because the actual power quality signals cannot completely reflect the diversity of the disturbance signals, different types of power quality signals are generated according to mathematical model simulation, 150 groups of disturbance signals are respectively generated randomly, the signal sampling frequency is 3.2kHz, Gaussian white noise with the signaltonoise ratio of 30dB is added into all the signals, then multiresolution hyperbolic S conversion is carried out on the original data, characteristic curves required by classification are extracted, a timefrequency database is established, a membership query method of the discrete Freund' S pause distance is improved, the effectiveness of the method is verified by using simulation signals, the method utilizes software simulation to generate simulation signals with the signaltonoise ratio of 30dB, the effectiveness of the method is verified, and the result proves that the recognition rate of the disturbance signals is higher than 99.3%.
Fig. 2 is a schematic structural diagram of the timefrequency databasebased online power quality disturbance identification system provided by the present invention, and as shown in fig. 2, the timefrequency databasebased online power quality disturbance identification system provided by the present invention specifically includes the following steps:
the treeshaped timefrequency database module 201 is used for establishing a timefrequency database of a treeshaped structure according to various disturbance signal characteristic curves, wherein a father node of the treeshaped structure is a frequency domain database, child nodes of the treeshaped structure are time domain databases, frequency amplitude partial curves of the disturbance signal characteristic curves are stored in the frequency domain databases, and fundamental frequency amplitude curves of the disturbance signal characteristic curves are stored in the time domain databases;
the signal acquisition module 202 is used for acquiring a quality disturbance signal of the electric energy to be identified;
the S conversion module 203 is used for performing multiresolution hyperbolic S conversion on the disturbance signal to be identified;
the characteristic curve extraction module 204 is configured to determine a characteristic curve of the disturbance signal to be identified according to the disturbance signal to be identified after the multiresolution hyperbolic S transform;
and the category identification module 205 is configured to determine a characteristic curve of the disturbance signal to be identified according to the disturbance signal to be identified after the multiresolution hyperbolic S transform.
Wherein, the treelike timefrequency database module 201 specifically includes: the frequency domain database 501 comprises a first frequency domain database, a second frequency domain database, a third frequency domain database and a fourth frequency domain database, wherein the first frequency domain database comprises a plurality of frequency amplitude partial curves generated by disturbance signals S1, S2, S3 and S4, the second frequency domain database comprises a plurality of frequency amplitude partial curves generated by disturbance signals S5, S6, S7 and S8, the third frequency domain database comprises a plurality of frequency amplitude partial curves generated by disturbance signals S9, S10, S11 and S12, the fourth frequency domain database comprises a plurality of frequency amplitude partial curves generated by disturbance signals S13, S14, S15 and S16, the time domain database comprises a first time domain database 502, a second time domain database 503, a third time domain database 504 and a fourth time domain database 505, the first time domain database 502 comprises a plurality of frequency amplitude partial curves generated by disturbance signals S1, S2 and S3, S16, and the first time domain database 502 comprises a plurality of disturbance signals S1, S2 and S3, The S4 generates a plurality of fundamental frequency amplitude curves, the second time domain database 503 includes a plurality of fundamental frequency amplitude curves generated by disturbance signals S5, S6, S7, S8, the third time domain database 504 includes a plurality of fundamental frequency amplitude curves generated by disturbance signals S9, S10, S11, S12, the fourth time domain database 505 includes a plurality of fundamental frequency amplitude curves generated by disturbance signals S13, S14, S15, S16, the S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15, and S16 are 16 kinds of disturbance signals.
Aiming at 16 types of disturbance design timefrequency databases, the timefrequency databases are respectively a voltage interruption S1, a voltage sag S2, a voltage transient rise S3, a flicker S4, a harmonic S5, a transient fall harmonic S6, a transient rise harmonic S7, a flicker harmonic S8, a transient oscillation S9, a transient fall transient oscillation S10, a transient rise transient oscillation S11, a flicker transient oscillation S12, a transient oscillation harmonic S13, a transient oscillation harmonic and voltage sag S14, a transient oscillation harmonic and voltage transient rise S15 and a transient oscillation harmonic and flicker S16. The frequency domain database 501 is a frequency amplitude partial curve, and is composed of four parts, namely a first frequency domain database 1b1, a second frequency domain database 1b2, a third frequency domain database 1b3 and a fourth frequency domain database 1b 4. The time domain database is a fundamental frequency amplitude curve, and is composed of four flat subdatabases, namely a first time domain database 502, a second time domain database 503, a third time domain database 504 and a fourth time domain database 505. The first timedomain database 502 is composed of four parts, 2b11, 2b12, 2b13, and 2b 14; the second time domain database 503 is composed of four parts, 2b21, 2b22, 2b23, 2b 24; the third time domain database 504 is composed of four parts, 2b31, 2b32, 2b33, 2b 34; the fourth time domain database 505 is composed of four parts, 2b41, 2b42, 2b43, and 2b 44. The parts of the database are explained as follows:
1b 1: the frequency amplitude part curves of the four disturbance multiresolution hyperbolic S transforms S1, S2, S3 and S4 have the same characteristics that the curves are smooth in level and the curve amplitude is close to zero. The frequency amplitude part curves of the four disturbance multiresolution S transformations of S1, S2, S3 and S4 are taken 50 pieces to form 200 pieces of waveform data, namely 1b 1.
1b 2: the frequency amplitude part curves of the four disturbance multiresolution hyperbolic S transformations of S5, S6, S7 and S8 have the same characteristic that the curves have a plurality of spikes in the intermediate frequency part (100Hz to 900Hz), and other parts are horizontally smooth and the curve amplitude is close to zero. Under random conditions, 50 frequency amplitude partial curves of four disturbance multiresolution S transformations of S5, S6, S7 and S8 are respectively adopted to form 200 pieces of waveform data, namely 1b 2.
1b 3: the frequency amplitude part curves of the four disturbance multiresolution hyperbolic S transforms of S9, S10, S11 and S12 have the same characteristic that the curves have a slowly rising peak in a highfrequency part (above 900Hz), and the other parts are smooth horizontally and the curve amplitude is close to zero. The frequency amplitude part curves of the four disturbance multiresolution S transformations of S9, S10, S11 and S12 are taken 50 pieces to form 200 pieces of waveform data, namely 1b 3.
1b 4: the frequency amplitude part curves of the four disturbance multiresolution hyperbolic S transformations of S13, S14, S15 and S16 have the same characteristics that the curves have multiple peaks in the intermediate frequency part (100Hz to 900Hz) and a slowly rising peak in the high frequency part (above 900Hz), and the other parts are horizontally smooth and the curve amplitude is close to zero. 200 pieces of waveform data are formed by 50 pieces of frequency amplitude partial curves of four disturbance multiresolution hyperbolic S transformation of S13, S14, S15 and S16 respectively, and the data are 1b 4.
2b21, 2b31 and 2b41 are respectively taken from fundamental frequency amplitude curves of three disturbance multiresolution hyperbolic S transformation of S5, S9 and S13, and 50 disturbance are selected for each disturbance. The fundamental frequency amplitude curves of the three disturbance multiresolution hyperbolic S transformations have the same characteristics, namely the amplitude of the fundamental frequency amplitude curves is basically unchanged and is in a horizontal straight line shape.
2b11, 2b12, 2b22, 2b32 and 2b42 are respectively taken from fundamental frequency amplitude curves of multiresolution hyperbolic S transformation of five kinds of disturbances, namely S1, S2, S6, S10 and S14, and 50 disturbances are selected for each kind of disturbance. The fundamental frequency amplitude curves of the five disturbance multiresolution hyperbolic Stransform have the same characteristic that the amplitude of the fundamental frequency amplitude curves firstly falls and then rises to form a concave shape, wherein the concave degrees of the fundamental frequency amplitude curves of S1 and S2 are different.
2b13, 2b23, 2b33 and 2b43 are respectively taken from fundamental frequency amplitude curves of four disturbance multiresolution hyperbolic S transformation of S3, S7, S11 and S15, and 50 disturbance are selected for each kind of disturbance. The four disturbed multiresolution hyperbolic Stransformed fundamental frequency amplitude curves have the same characteristic, namely, the amplitude of the fundamental frequency amplitude curve rises first and then falls, and the fundamental frequency amplitude curve is in an upward convex shape.
2b14, 2b24, 2b34 and 2b44 are respectively taken from fundamental frequency amplitude curves of four disturbance multiresolution hyperbolic S transformation of S4, S8, S12 and S16, and 50 disturbance are selected for each kind of disturbance. The fundamental frequency amplitude curves of the four disturbance multiresolution hyperbolic S transformations have the same characteristics, namely, the amplitude of the fundamental frequency amplitude curves is periodically transformed to present the shape of waves.
The S transform module 203 specifically includes:
the lowfrequency part S transformation unit is used for performing multiresolution hyperbolic S transformation on the lowfrequency part of the disturbance signal to be identified;
the intermediate frequency part S conversion unit is used for performing multiresolution hyperbolic S conversion of the intermediate frequency part on the disturbance signal to be identified;
and the highfrequency part S conversion unit is used for performing multiresolution hyperbolic S conversion on the highfrequency part of the disturbance signal to be identified.
The method of multiresolution hyperbolic Stransform is determined by equations (1), (2), (3) and (4):
order toτ → jT, the discrete expression of the hyperbolic S transform is as follows:
when n ≠ 0, it is determined,
when n is 0 (corresponding to zero frequency),
wherein j, m, N is 0, 1. G_{H}Discretized expression of (m, n):
n is the number of discrete signals for hyperbolic S transformation; t, acquiring the time of two adjacent points of the signal, namely a sampling period; f is the frequency; t is time; m is used to implement the conversion at different frequencies; the parameter τ is used to control the position of the hyperbolic window on the time axis. The signal is obtained by fast Fourier transformAdding the parameter m to obtainG_{H}(m, n) is a hyperbolic windowA function; g_{H}The discretization expression of (m, n) is solved by formula (3), and the variable X in formula (3) is solved by formula (4);determining the attenuation degree of the front half curve of the hyperbolic window;determining the attenuation degree of a half curve of the hyperbolic window; lambda [ alpha ]_{HY}The curvature of the hyperbolic window is determined.The increase in (b) attenuates the front and rear halves of the hyperbolic window, thereby changing the shape of the hyperbolic window. By adjustingThe sum of the values enables the window width to change with the frequency in inverse proportion, and the purpose of adjusting the time frequency precision is achieved.
Wherein, when the lowfrequency part S conversion unit is used for conversion, the frequency range of the lowfrequency part is 1Hz and f_{L}N of multiresolution hyperbolic S transform discrete expression less than or equal to 100Hz_{L}The value range of (1) is NT less than or equal to n_{L}The parameter of < 100NT isλ_{HY}And (3) substituting the parameters into the formula (1) to perform S transformation on the lowfrequency part of the signal to be identified.
When the intermediate frequency part S conversion unit performs conversion, the frequency range of the intermediate frequency part is 101Hz and f_{M}Not more than 900Hz, multiresolution hyperbolic S conversion discrete expression n_{M}Has a value range of 101NT ≤ n_{M}A parameter of 900NT or lessλ_{HY}Substituting the above parameters into equation (1) performs Sconversion of the intermediate frequency portion of the signal to be identified, 16.
When the highfrequency part S conversion unit performs conversionThe frequency range of the highfrequency part is 901 Hzf_{H}Less than or equal to 1300Hz, n of discrete expression_{H}The value range of (1) is that 901NT is less than or equal to n_{H}The parameter of < 1300NT isλ_{HY}Substituting the parameters into equation (1) for 32, Stransform the high frequency part of the signal to be identified.
And finally, obtaining a complex matrix as a result of the multiresolution hyperbolic S transformation, and performing modulus operation on each element of the matrix to obtain a modulus matrix of the multiresolution hyperbolic S transformation. The modulo matrix column represents the sampling time and the row represents the frequency.
The characteristic curve extraction module 204 specifically includes:
the base frequency amplitude curve selecting unit is used for selecting a base frequency amplitude curve, and the base frequency amplitude curve is a row vector of a multiresolution hyperbolic S transformation mode matrix with the frequency of 50 Hz;
and the frequency amplitude part curve selecting unit is used for selecting a frequency amplitude part curve which is the maximum value of each row of the multiresolution S transformation mode matrix and selecting a range larger than 100Hz on the basis of the frequency amplitude part curve.
The category identifying module 205 specifically includes:
the discrete Fourier distance algorithm improving unit is used for improving the discrete Fourier distance algorithm to obtain an improved discrete Fourier distance algorithm;
determining a frequency part amplitude curve unit with the minimum distance, which is used for calculating the distance between the frequency amplitude part curve of the disturbance signal to be identified and the frequency amplitude part curves in each frequency domain database by adopting an improved discrete Fourier distance algorithm, and determining the frequency amplitude part curve with the minimum distance in the frequency domain database;
the membership degree unit is used for determining a frequency domain database to which the frequency amplitude curve of the frequency amplitude curve with the minimum distance belongs to obtain a membership frequency domain database;
determining a fundamental frequency amplitude curve unit with the minimum distance, which is used for calculating the distance between the fundamental frequency amplitude curve of the disturbance signal to be identified and the fundamental frequency amplitude curve in the time domain database corresponding to the subordinate frequency domain database by adopting an improved discrete Fourier distance algorithm, and determining the fundamental frequency amplitude curve with the minimum distance in the time domain database;
and the identification unit is used for determining the class of the fundamental frequency amplitude curve with the minimum distance as the class of the disturbance signal to be identified.
The discrete Fourier distance algorithm improving unit specifically comprises:
first the discrete freschel distance is defined as:
given a polygonal chain of C vertices (or vertices) Q ═ Q_{1},q_{2},...,q_{l}>. The step h along Q partitions the high point (or low point) of Q into h disjoint nonempty subsets { Q_{i}}_{i＝1...k}(1. ltoreq. h. ltoreq.l) such that Q is < Q_{1},q_{2},...,q_{l}> and 0 ═ l_{0}＜l_{1}＜...＜l_{k}＝l。
A. B represents two given polygonal chains, wherein A ═ a_{1},...,a_{e}＞，B＝＜b_{1}，,...b_{r}>. A combined step along A and B is an hstep along A { A }_{i}}_{i＝1...h}And an hstep { B along B_{i}}_{i＝1...h}Composition such that for 1 ≦ i ≦ h, or  A_{i}1 or  B_{i}＝1。
Determining a discrete Frechst distance algorithm, the algorithm being determined by equations (5) and (6):
a is the identified perturbation signal, B is the corresponding perturbation curve in the timefrequency data database, and formula (5) represents a combined step W { (A) along A and B_{i},B_{i}) Cost of }, formula (a)6) The discrete Freund's distance between A and B is expressed and its minimum value is solved from equation (5) to yield equation (6).
Improving the discrete Fourier distance:
the correspondence of the multiple chains A and B due to the discrete Fourier distance only needs to satisfy: or  A_{i}1 or  B_{i}1, wherein  A_{i}I represents A_{i}The middle includes the number of high points,  B_{i}I represents B_{i}Including the number of high spots, c being a limit A_{i}And B_{i}The number of high points is contained, so that a large number of unnecessary corresponding relations exist, the calculation time is increased, and aiming at the problem, the invention provides a limiting condition (only one of the following conditions needs to be satisfied):
①ψ＞B_{i}＞A_{i}1, the constant psi satisfies h > psi > 1
②ψ＞A_{i}＞B_{i}1, the constant psi satisfies h > psi > 1
③B_{i}＝A_{i}＝1
The membership degree unit specifically comprises:
and inquiring corresponding disturbance signals according to the membership degree:
obtaining partial curves of frequency amplitude values by performing multiresolution hyperbolic S conversion on signals Y measured by the mutual inductor, and calculating the minimum d by comparing the improved discrete Fourier distance with each curve of the frequency domain database (1B)_{H}(a, B) and find the corresponding curves and the corresponding sections (1B1, 1B2, 1B3, 1B 4). The signal Y belongs to one of the four types 1b1, 1b2, 1b3 and 1b4 according to the principle of membership. If Y belongs to 1B1, the signal Y is subjected to multiresolution hyperbolic S conversion to obtain a fundamental frequency amplitude curve, and the fundamental frequency amplitude curve is compared with a time domain database 2B 1; if Y belongs to 1B2, the signal Y is subjected to multiresolution hyperbolic S conversion to obtain a fundamental frequency amplitude curve, and the fundamental frequency amplitude curve is compared with a time domain database 2B 2; if Y belongs to 1B3, the signal Y is subjected to multiresolution hyperbolic S conversion to obtain a fundamental frequency amplitude curve, and the fundamental frequency amplitude curve is compared with a time domain database 2B 3; if Y belongs to 1B4, the signal Y is subjected to multiresolution hyperbolic S conversion to obtain a fundamental frequency amplitude curve, and the fundamental frequency amplitude curve is compared with a time domain database 2B 4; finally obtaining the product 2b11,2b12, 2b13, 2b14, 2b21, 2b22, 2b23, 2b24, 2b31, 2b32, 2b33, 2b34, 2b41, 2b42, 2b43, 2b44, the signal Y is a corresponding disturbance in S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15, S16.
Fig. 3 is a flowchart of an online power quality disturbance identification method and system based on a timefrequency database according to an embodiment of the present invention, as shown in fig. 3:
starting 301, the timefrequency database online power quality disturbance identification system starts working;
acquiring a quality disturbance signal 302 of electric energy to be identified, and performing online acquisition and recording on a related disturbance signal by using a voltage transformer, a current transformer and secondary equipment in a transformer substation;
performing multiresolution hyperbolic S transformation 303, namely performing multiresolution hyperbolic S transformation on the disturbance signal to be identified;
extracting a characteristic curve 304, and determining the characteristic curve of the disturbance signal to be identified by selecting two characteristic curves, namely a fundamental frequency amplitude curve and a frequency amplitude part curve, according to the disturbance signal to be identified after multiresolution hyperbolic S transformation;
improving a discrete Fourier distance query treeshaped waveform subdatabase to perform classification and identification 305, comparing a characteristic curve of the disturbance signal to be identified with a characteristic curve in the characteristic curve database, and determining the category of the characteristic curve of the disturbance signal to be identified;
and end 306, indicating that the disturbance identification system stops working.
Fig. 4 is a characteristic curve diagram of 16 disturbance signals according to the embodiment of the present invention, and as shown in fig. 4, characteristic curves of the voltage interruption S1 after multiresolution hyperbolic S transformation are an S1 fundamental frequency amplitude curve 1 and an S1 frequency amplitude partial curve 2; the characteristic curve of the voltage sag S2 after multiresolution hyperbolic S transformation is an S2 fundamental frequency amplitude curve 3 and an S2 frequency amplitude partial curve 4; the characteristic curves of the voltage temporary rising S3 after multiresolution hyperbolic S transformation are an S3 fundamental frequency amplitude curve 5 and an S3 frequency amplitude partial curve 6; the characteristic curve of the flicker S4 after multiresolution hyperbolic S transformation is an S4 fundamental frequency amplitude curve 7 and an S4 frequency amplitude partial curve 8; the characteristic curve of the harmonic S5 after multiresolution hyperbolic S transformation is an S5 fundamental frequency amplitude curve 9 and an S5 frequency amplitude partial curve 10; temporarily reducing characteristic curves of harmonic S6 after multiresolution hyperbolic S transformation into an S6 fundamental frequency amplitude curve 11 and an S6 frequency amplitude partial curve 12; temporarily increasing characteristic curves of the S7 containing harmonics after multiresolution hyperbolic S conversion into an S7 fundamental frequency amplitude curve 13 and an S7 frequency amplitude partial curve 14; the characteristic curve of the flicker containing harmonic S8 after multiresolution hyperbolic S transformation is an S8 fundamental frequency amplitude curve 15 and an S8 frequency amplitude partial curve 16; the characteristic curve of the transient oscillation S9 after multiresolution hyperbolic S transformation is an S9 fundamental frequency amplitude curve 17 and an S9 frequency amplitude partial curve 18; temporarily reducing characteristic curves of transient oscillation S10 after multiresolution hyperbolic S transformation into an S10 fundamental frequency amplitude curve 19 and an S10 frequency amplitude partial curve 20; temporarily increasing a characteristic curve of transient oscillation S11 after multiresolution hyperbolic S transformation into an S11 fundamental frequency amplitude curve 21 and an S11 frequency amplitude partial curve 22; the characteristic curve of the flicker transientcontaining oscillation S12 after multiresolution hyperbolic S transformation is an S12 fundamental frequency amplitude curve 23 and an S12 frequency amplitude partial curve 24; the characteristic curve of transient oscillation containing harmonic S13 after multiresolution hyperbolic S transformation is an S13 fundamental frequency amplitude curve 25 and an S13 frequency amplitude partial curve 26; the characteristic curves of transient oscillation containing harmonic waves and voltage sag S14 after multiresolution hyperbolic S conversion are an S14 fundamental frequency amplitude curve 27 and an S14 frequency amplitude partial curve 28; the characteristic curves of transient oscillation containing harmonic waves and voltage transient S15 after multiresolution hyperbolic S conversion are an S15 fundamental frequency amplitude curve 29 and an S15 frequency amplitude partial curve 30; the characteristic curves of transient oscillation containing harmonic waves and flicker S16 after multiresolution hyperbolic S conversion are an S16 fundamental frequency amplitude curve 31 and an S16 frequency amplitude partial curve 32.
Fig. 5 is a treelike timefrequency data diagram according to an embodiment of the present invention, and as shown in fig. 5, the frequency domain database 501 is a frequency amplitude partial curve, which is composed of four parts, namely, a first frequency domain database 1b1, a second frequency domain database 1b2, a third frequency domain database 1b3, and a fourth frequency domain database 1b 4. The time domain database is a fundamental frequency amplitude curve, and is composed of four flat subdatabases, namely a first time domain database 502, a second time domain database 503, a third time domain database 504 and a fourth time domain database 505. The first timedomain database 502 is composed of four parts, 2b11, 2b12, 2b13, and 2b 14; the second time domain database 503 is composed of four parts, 2b21, 2b22, 2b23, 2b 24; the third time domain database 504 is composed of four parts, 2b31, 2b32, 2b33, 2b 34; the fourth time domain database 505 is composed of four parts, 2b41, 2b42, 2b43, and 2b 44.
Fig. 6 is a flowchart of a treelike timefrequency database query according to an embodiment of the present invention, as shown in fig. 6,
firstly, waveform data 601 is obtained;
querying the frequency domain database 1B to obtain a classification category 602, and obtaining that the identified disturbance signals belong to one category of 1B1, 1B2, 1B3 and 1B 4;
querying a corresponding time domain database 2B1 according to 602 to obtain a classification category 603, or a time domain database 2B2 to obtain a classification category 604, or a time domain database 2B3 to obtain a classification category 605, or a time domain database 2B4 to obtain a classification category 606;
finally, if the disturbance signal belongs to one of 2b11, 2b12, 2b13, 2b14, 2b21, 2b22, 2b23, 2b24, 2b31, 2b32, 2b33, 2b34, 2b41, 2b42, 2b43 and 2b44, the corresponding disturbance signal is identified as S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 and S16.
The invention provides an online power quality disturbance identification system based on a time frequency database, which is characterized in that the time frequency database of a tree structure is established according to various disturbance signal characteristic curves, a father node of the tree structure is a frequency domain database, a child node of the tree structure is a time domain database, a frequency amplitude partial curve of the disturbance signal characteristic curve is stored in the frequency domain database, and a fundamental frequency amplitude curve of the disturbance signal characteristic curve is stored in the time domain database; collecting a quality disturbance signal of electric energy to be identified; performing multiresolution hyperbolic S transformation on a disturbance signal to be identified; determining a characteristic curve of the disturbance signal to be identified according to the disturbance signal to be identified after multiresolution hyperbolic S transformation; and comparing the characteristic curve of the disturbance signal to be identified with the characteristic curve in the characteristic curve database, determining the category of the characteristic curve of the disturbance signal to be identified, wherein the identification rate of the disturbance signal is up to more than 99.3%.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (9)
1. An online electric energy quality disturbance identification method based on a timefrequency database is characterized by comprising the following steps:
establishing a timefrequency database of a tree structure according to various disturbance signal characteristic curves, wherein a father node of the tree structure is a frequency domain database, a child node of the tree structure is a time domain database, a frequency amplitude partial curve of the disturbance signal characteristic curve is stored in the frequency domain database, and a fundamental frequency amplitude curve of the disturbance signal characteristic curve is stored in the time domain database;
collecting a quality disturbance signal of electric energy to be identified;
performing multiresolution hyperbolic S transformation on a disturbance signal to be identified;
determining a characteristic curve of the disturbance signal to be identified according to the disturbance signal to be identified after multiresolution hyperbolic S transformation;
and comparing the characteristic curve of the disturbance signal to be identified with the characteristic curve in the time frequency database, and determining the category of the characteristic curve of the disturbance signal to be identified.
2. The timefrequency databasebased online power quality disturbance identification method according to claim 1, wherein the frequency domain database includes a first frequency domain database, a second frequency domain database, a third frequency domain database and a fourth frequency domain database, the first frequency domain database includes a plurality of frequency amplitude partial curves generated randomly by disturbance signals S1, S2, S3 and S4, the second frequency domain database includes a plurality of frequency amplitude partial curves generated by disturbance signals S5, S6, S7 and S8, the third frequency domain database includes a plurality of frequency amplitude partial curves generated by disturbance signals S9, S10, S11 and S12, the fourth frequency domain database includes a plurality of frequency amplitude partial curves generated by disturbance signals S13, S14, S15 and S16, the time domain database includes a first time domain database, a second time domain database, a third time domain database and a fourth time domain database, the first time domain database comprises a plurality of fundamental frequency amplitude curves generated by disturbance signals S1, S2, S3 and S4, the second time domain database comprises a plurality of fundamental frequency amplitude curves generated by disturbance signals S5, S6, S7 and S8, the third time domain database comprises a plurality of fundamental frequency amplitude curves generated by disturbance signals S9, S10, S11 and S12, the fourth time domain database comprises a plurality of fundamental frequency amplitude curves generated by disturbance signals S13, S14, S15 and S16, and the S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 and S16 are 16 kinds of disturbance signals; the frequency amplitude part curves of the four disturbance multiresolution hyperbolic S transforms of S1, S2, S3 and S4 have the same characteristics that the curves are smooth in horizontal direction and the curve amplitude is close to zero; the frequency amplitude part curves of the four disturbance multiresolution hyperbolic S transformations of S5, S6, S7 and S8 have the same characteristics, namely, the curves have a plurality of peaks in an intermediate frequency part, the intermediate frequency part is 100Hz to 900Hz, other parts are smooth horizontally, and the curve amplitude is close to zero; the frequency amplitude part curves of the four disturbance multiresolution hyperbolic S transformations of S9, S10, S11 and S12 have the same characteristics, namely, a slowly rising peak appears in a highfrequency part of the curve, the highfrequency part is above 900Hz, other parts are smooth horizontally, and the amplitude of the curve is close to zero; the frequency amplitude part curves of the four disturbance multiresolution hyperbolic S transforms of S13, S14, S15 and S16 have the same characteristics that the curves have a plurality of peaks in the middle frequency part and a slowly rising peak in the high frequency part, and the other parts are smooth horizontally and the curve amplitude is close to zero.
3. The method for identifying the disturbance of the quality of the electric energy online based on the timefrequency database according to claim 1, wherein the step of comparing the characteristic curve of the disturbance signal to be identified with the characteristic curve in the characteristic curve database to determine the category of the characteristic curve of the disturbance signal to be identified specifically comprises the steps of:
improving the discrete Fourier distance algorithm to obtain an improved discrete Fourier distance algorithm;
calculating the distance between the characteristic curve of the disturbance signal to be identified and the frequency amplitude part curve in each frequency domain database by adopting an improved discrete Fourier distance algorithm, and determining the frequency amplitude part curve with the minimum distance in the frequency domain database;
determining a frequency domain database to which the frequency amplitude part with the minimum distance belongs to obtain a subordination frequency domain database;
calculating the distance between the fundamental frequency amplitude curve of the disturbance signal to be identified and the fundamental frequency amplitude curve in the time domain database corresponding to the subordinate frequency domain database by adopting an improved discrete Fourier distance algorithm, and determining the fundamental frequency amplitude curve with the minimum distance in the time domain database;
and determining the class of the fundamental frequency amplitude curve with the minimum distance as the class of the disturbance signal to be identified.
4. The timefrequency databasebased online electric energy quality disturbance identification method according to claim 1, wherein the performing multiresolution hyperbolic S transformation on the disturbance signal to be identified specifically comprises:
performing multiresolution hyperbolic S transformation on the lowfrequency part of the disturbance signal to be identified;
performing multiresolution hyperbolic S conversion of an intermediate frequency part on the disturbance signal to be identified;
and performing multiresolution hyperbolic S transformation on the highfrequency part of the disturbance signal to be identified.
5. The method for identifying the disturbance of the quality of the electric energy on line based on the timefrequency database according to claim 1, wherein the determining of the characteristic curve of the disturbance signal to be identified specifically comprises:
selecting a fundamental frequency amplitude curve, wherein the fundamental frequency amplitude curve is a row vector of a multiresolution hyperbolic S transformation mode matrix with the frequency of 50 Hz;
and selecting a frequency amplitude part curve which is the maximum value of each row of the multiresolution hyperbolic Stransform mode matrix, and selecting a range larger than 100Hz on the basis of the frequency amplitude part curve.
6. An online electric energy quality disturbance identification system based on a timefrequency database is characterized in that the system comprises:
the treeshaped timefrequency database module is used for establishing a timefrequency database of a tree structure according to various disturbance signal characteristic curves, wherein a father node of the tree structure is a frequency domain database, a child node of the tree structure is a time domain database, a frequency amplitude partial curve of the disturbance signal characteristic curve is stored in the frequency domain database, and a fundamental frequency amplitude curve of the disturbance signal characteristic curve is stored in the time domain database;
the signal acquisition module is used for acquiring a quality disturbance signal of the electric energy to be identified;
the S conversion module is used for performing multiresolution hyperbolic S conversion on the disturbance signal to be identified;
the characteristic curve extraction module is used for determining a characteristic curve of the disturbance signal to be identified according to the disturbance signal to be identified after the multiresolution hyperbolic S transformation;
and the category identification module is used for comparing the characteristic curve of the disturbance signal to be identified with the characteristic curve in the time frequency database and determining the category of the characteristic curve of the disturbance signal to be identified.
7. The system for identifying the quality disturbance of the online electric energy based on the timefrequency database according to claim 6, wherein the category identification module specifically comprises:
the discrete Fourier distance algorithm improving unit is used for improving the discrete Fourier distance algorithm to obtain an improved discrete Fourier distance algorithm;
determining a frequency amplitude part curve unit with the minimum distance, and calculating the distance between the characteristic curve of the disturbance signal to be identified and the frequency amplitude part curve in each frequency domain database by adopting an improved discrete Fourier distance algorithm, and determining the frequency amplitude part curve with the minimum distance;
the membership degree unit is used for determining a frequency domain database to which the frequency amplitude partial curve with the minimum distance belongs to obtain a membership frequency domain database;
determining a fundamental frequency amplitude curve unit with the minimum distance, which is used for calculating the distance between the fundamental frequency amplitude curve of the disturbance signal to be identified and the fundamental frequency amplitude curve in the time domain database corresponding to the subordinate frequency domain database by adopting an improved discrete Fourier distance algorithm, and determining the fundamental frequency amplitude curve with the minimum distance in the time domain database;
and the identification unit is used for determining the class of the fundamental frequency amplitude curve with the minimum distance as the class of the disturbance signal to be identified.
8. The system for identifying the power quality disturbance on line based on the timefrequency database according to claim 6, wherein the S transformation module specifically comprises:
the lowfrequency part S transformation unit is used for performing multiresolution hyperbolic S transformation on the lowfrequency part of the disturbance signal to be identified;
the intermediate frequency part S conversion unit is used for performing multiresolution hyperbolic S conversion of the intermediate frequency part on the disturbance signal to be identified;
and the highfrequency part S conversion unit is used for performing multiresolution hyperbolic S conversion on the highfrequency part of the disturbance signal to be identified.
9. The system for identifying the power quality disturbance on line based on the timefrequency database according to claim 6, wherein the characteristic curve extraction module specifically comprises:
the base frequency amplitude curve selecting unit is used for selecting a base frequency amplitude curve, and the base frequency amplitude curve is a row vector of a multiresolution hyperbolic S transformation mode matrix with the frequency of 50 Hz;
and the frequency amplitude part curve selecting unit is used for selecting a frequency amplitude part curve which is the maximum value of each row of the multiresolution hyperbolic Stransform mode matrix and selecting a range larger than 100Hz on the basis of the frequency amplitude part curve.
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