quantify.gain: Quantify Gain Based on Period 1 and Period 2 Prediction

In gainML: Machine Learning-Based Analysis of Potential Power Gain from Passive Device Installation on Wind Turbine Generators

Description

Calculates effect curve, offset curve, and gain curve, and quantifies gain by using both period 1 and period 2 prediction results.

Usage

quantify.gain(p1.res, p2.res, ratedPW, AEP, pw.freq, freq.id = 3)

Arguments

p1.res	A list containing the period 1 analysis results.
p2.res	A list containing the period 2 prediction results.
ratedPW	A kW value that describes the (common) rated power of the selected turbines (REF and CTR-b).
AEP	A kWh value describing the annual energy production from a single turbine.
pw.freq	A matrix or a dataframe that includes power output bins and corresponding frequency in terms of the accumulated hours during an annual period.
freq.id	An integer indicating the column number of pw.freq that describes the frequency of power bins in terms of the accumulated hours during an annual period. By default, this parameter is set to 3.

Value

The function returns a list containing the following.

effectCurve

A vector of length m illustrating REF turbine's power output difference between period 1 and 2, where m is the number of power bins.

offsetCurve

A vector of length m illustrating CTR-b turbine's power output difference between period 1 and 2.

gainCurve

A vector of length m illustrating the bin-wise gain. Equivalent to effCurve - offCurve.

gain

A scalar representing the final gain after offset adjustment (derived from gainCurve).

effect

A scalar representing the initial effect without offset correction (derived from effCurve).

offset

A scalar representing the offset value for the final gain quantification (derived from offCurve).

References

H. Hwangbo, Y. Ding, and D. Cabezon, 'Machine Learning Based Analysis and Quantification of Potential Power Gain from Passive Device Installation,' arXiv:1906.05776 [stat.AP], Jun. 2019. https://arxiv.org/abs/1906.05776.

Examples

df.ref <- with(wtg, data.frame(time = time, turb.id = 1, wind.dir = D,
 power = y, air.dens = rho))
df.ctrb <- with(wtg, data.frame(time = time, turb.id = 2, wind.spd = V,
 power = y))
df.ctrn <- df.ctrb
df.ctrn$turb.id <- 3

data <- arrange.data(df.ref, df.ctrb, df.ctrn, p1.beg = '2014-10-24',
 p1.end = '2014-10-25', p2.beg = '2014-10-25', p2.end = '2014-10-26',
 k.fold = 2)

p1.res <- analyze.p1(data$train, data$test, ratedPW = 1000)
p2.res <- analyze.p2(data$per1, data$per2, p1.res$opt.cov)

res <- quantify.gain(p1.res, p2.res, ratedPW = 1000, AEP = 300000, pw.freq = pw.freq)

res$effect - res$offset #This should be equivalent to the final gain below.
res$gain

res$gainCurve #This shows the bin-wise gain (after offset adjustment).

gainML documentation built on June 28, 2019, 5:05 p.m.