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#' @importFrom magrittr %>%
#' @title Future Probability of Failure for 50kV OHL Conductors
#' @description This function calculates the future
#' annual probability of failure per kilometer 50kV OHL conductors.
#' The function is a cubic curve that is based on
#' the first three terms of the Taylor series for an
#' exponential function.
#' @inheritParams pof_ohl_cond_132_66_33kv
#' @param simulation_end_year Numeric. The last year of simulating probability
#' of failure. Default is 100.
#' @param k_value Numeric. \code{k_value = 0.0069} by default. This number is
#' given in a percentage. The default value is accordingly to the CNAIM standard
#' on p. 110.
#' @param c_value Numeric. \code{c_value = 1.087} by default.
#' The default value is accordingly to the CNAIM standard see page 110
#' @param normal_expected_life Numeric. \code{normal_expected_life = 60} by default.
#' The default value is accordingly to the CNAIM standard on page 107.
#' @return DataFrame. Future probability of failure
#' along with future health score
#' @export
#' @examples
#' # Future annual probability of failure for 50kV OHL (Tower Line) Conductor
#' pof_future_ohl_cond_50kv(
#' sub_division = "Cu",
#' placement = "Default",
#' altitude_m = "Default",
#' distance_from_coast_km = "Default",
#' corrosion_category_index = "Default",
#' age = 10,
#' conductor_samp = "Default",
#' corr_mon_survey = "Default",
#' visual_cond = "Default",
#' midspan_joints = "Default",
#' reliability_factor = "Default",
#' k_value = 0.0080,
#' c_value = 1.087,
#' normal_expected_life = "Default",
#' simulation_end_year = 100)
pof_future_ohl_cond_50kv <-
function(sub_division = "Cu",
placement = "Default",
altitude_m = "Default",
distance_from_coast_km = "Default",
corrosion_category_index = "Default",
age,
conductor_samp = "Default",
corr_mon_survey = "Default",
visual_cond = "Default",
midspan_joints = "Default",
reliability_factor = "Default",
k_value = 0.0080,
c_value = 1.087,
normal_expected_life = "Default",
simulation_end_year = 100) {
ohl_conductor <- "66kV OHL (Tower Line) Conductor"
`Asset Register Category` = `Health Index Asset Category` =
`Generic Term...1` = `Generic Term...2` = `Functional Failure Category` =
`K-Value (%)` = `C-Value` = `Asset Register Category` = `Sub-division` =
`Condition Criteria: Conductor Sampling Result` =
`Condition Criteria: Corrosion Monitoring Survey Result` =
`Condition Criteria: Observed Condition` =
`Condition Criteria: No. of Midspan Joints` = NULL
# due to NSE notes in R CMD check
# Ref. table Categorisation of Assets and Generic Terms for Assets --
asset_category <- gb_ref$categorisation_of_assets %>%
dplyr::filter(`Asset Register Category` == ohl_conductor) %>%
dplyr::select(`Health Index Asset Category`) %>% dplyr::pull()
generic_term_1 <- gb_ref$generic_terms_for_assets %>%
dplyr::filter(`Health Index Asset Category` == asset_category) %>%
dplyr::select(`Generic Term...1`) %>% dplyr::pull()
generic_term_2 <- gb_ref$generic_terms_for_assets %>%
dplyr::filter(`Health Index Asset Category` == asset_category) %>%
dplyr::select(`Generic Term...2`) %>% dplyr::pull()
# Normal expected life -------------------------
normal_expected_life_cond <- gb_ref$normal_expected_life %>%
dplyr::filter(`Asset Register Category` == ohl_conductor &
`Sub-division` == sub_division) %>%
dplyr::pull()
if (normal_expected_life == "Default") {
normal_expected_life_cond <- gb_ref$normal_expected_life %>%
dplyr::filter(`Asset Register Category` == ohl_conductor &
`Sub-division` == sub_division) %>%
dplyr::pull()
} else {
normal_expected_life_cond <- normal_expected_life
}
# Constants C and K for PoF function --------------------------------------
k <- k_value/100
c <- c_value
# Duty factor -------------------------------------------------------------
duty_factor_cond <- 1
# Location factor ----------------------------------------------------
location_factor_cond <- location_factor(placement,
altitude_m,
distance_from_coast_km,
corrosion_category_index,
asset_type = ohl_conductor)
# Expected life ------------------------------
expected_life_years <- expected_life(normal_expected_life_cond,
duty_factor_cond,
location_factor_cond)
# b1 (Initial Ageing Rate) ------------------------------------------------
b1 <- beta_1(expected_life_years)
# Initial health score ----------------------------------------------------
initial_health_score <- initial_health(b1, age)
## NOTE
# Typically, the Health Score Collar is 0.5 and
# Health Score Cap is 10, implying no overriding
# of the Health Score. However, in some instances
# these parameters are set to other values in the
# Health Score Modifier calibration tables.
# Measured condition inputs ---------------------------------------------
if (asset_category == "EHV OHL Conductor (Tower Lines)") {
asset_category_mmi <- "EHV Tower Line Conductor"
}
if (asset_category == "132kV OHL Conductor (Tower Lines)") {
asset_category_mmi <- "132kV Tower Line Conductor"
}
mcm_mmi_cal_df <-
gb_ref$measured_cond_modifier_mmi_cal
mcm_mmi_cal_df <-
mcm_mmi_cal_df[which(
mcm_mmi_cal_df$`Asset Category` == asset_category_mmi), ]
factor_divider_1 <-
as.numeric(
mcm_mmi_cal_df$
`Parameters for Combination Using MMI Technique - Factor Divider 1`)
factor_divider_2 <-
as.numeric(
mcm_mmi_cal_df$
`Parameters for Combination Using MMI Technique - Factor Divider 2`)
max_no_combined_factors <-
as.numeric(
mcm_mmi_cal_df$
`Parameters for Combination Using MMI Technique - Max. No. of Combined Factors`
)
# Measured inputs-----------------------------------------------------------
# Conductor sampling
if (asset_category == "132kV OHL Conductor (Tower Lines)") {
mci_132kv_twr_line_cond_sampl <-
gb_ref$mci_132kv_twr_line_cond_sampl %>% dplyr::filter(
`Condition Criteria: Conductor Sampling Result` == conductor_samp
)
ci_factor_cond_samp <-
mci_132kv_twr_line_cond_sampl$`Condition Input Factor`
ci_cap_cond_samp <-
mci_132kv_twr_line_cond_sampl$`Condition Input Cap`
ci_collar_cond_samp <-
mci_132kv_twr_line_cond_sampl$`Condition Input Collar`
# Corrosion monitoring survey
mci_132kv_twr_line_cond_srvy <-
gb_ref$mci_132kv_twr_line_cond_srvy %>% dplyr::filter(
`Condition Criteria: Corrosion Monitoring Survey Result` ==
corr_mon_survey
)
ci_factor_cond_srvy <-
mci_132kv_twr_line_cond_srvy$`Condition Input Factor`
ci_cap_cond_srvy <- mci_132kv_twr_line_cond_srvy$`Condition Input Cap`
ci_collar_cond_srvy <-
mci_132kv_twr_line_cond_srvy$`Condition Input Collar`
} else {
mci_ehv_twr_line_cond_sampl <-
gb_ref$mci_ehv_twr_line_cond_sampl %>% dplyr::filter(
`Condition Criteria: Conductor Sampling Result` == conductor_samp
)
ci_factor_cond_samp <-
mci_ehv_twr_line_cond_sampl$`Condition Input Factor`
ci_cap_cond_samp <-
mci_ehv_twr_line_cond_sampl$`Condition Input Cap`
ci_collar_cond_samp <-
mci_ehv_twr_line_cond_sampl$`Condition Input Collar`
# Corrosion monitoring survey
mci_ehv_twr_line_cond_srvy <-
gb_ref$mci_ehv_twr_line_cond_srvy %>% dplyr::filter(
`Condition Criteria: Corrosion Monitoring Survey Result` ==
corr_mon_survey
)
ci_factor_cond_srvy <-
mci_ehv_twr_line_cond_srvy$`Condition Input Factor`
ci_cap_cond_srvy <- mci_ehv_twr_line_cond_srvy$`Condition Input Cap`
ci_collar_cond_srvy <-
mci_ehv_twr_line_cond_srvy$`Condition Input Collar`
}
# Measured conditions
factors <- c(ci_factor_cond_samp,
ci_factor_cond_srvy)
measured_condition_factor <- mmi(factors,
factor_divider_1,
factor_divider_2,
max_no_combined_factors)
caps <- c(ci_cap_cond_samp,
ci_cap_cond_srvy)
measured_condition_cap <- min(caps)
# Measured condition collar ----------------------------------------------
collars <- c(ci_collar_cond_samp,
ci_collar_cond_srvy)
measured_condition_collar <- max(collars)
# Measured condition modifier ---------------------------------------------
measured_condition_modifier <- data.frame(measured_condition_factor,
measured_condition_cap,
measured_condition_collar)
# Observed conditions -----------------------------------------------------
oci_mmi_cal_df <-
gb_ref$observed_cond_modifier_mmi_cal
oci_mmi_cal_df <-
oci_mmi_cal_df[which(
oci_mmi_cal_df$`Asset Category` == asset_category_mmi), ]
factor_divider_1 <-
as.numeric(
oci_mmi_cal_df$`Parameters for Combination Using MMI Technique - Factor Divider 1`)
factor_divider_2 <-
as.numeric(
oci_mmi_cal_df$`Parameters for Combination Using MMI Technique - Factor Divider 2`)
max_no_combined_factors <-
as.numeric(
oci_mmi_cal_df$
`Parameters for Combination Using MMI Technique - Max. No. of Combined Factors`
)
# Observed inputs-----------------------------------------------------------
# Visual condition
if (asset_category == "132kV OHL Conductor (Tower Lines)") {
oci_132kv_twr_line_visual_cond <-
gb_ref$oci_132kv_twr_line_visual_cond %>% dplyr::filter(
`Condition Criteria: Observed Condition` == visual_cond
)
ci_factor_visual_cond <-
oci_132kv_twr_line_visual_cond$`Condition Input Factor`
ci_cap_visual_cond <-
oci_132kv_twr_line_visual_cond$`Condition Input Cap`
ci_collar_visual_cond <-
oci_132kv_twr_line_visual_cond$`Condition Input Collar`
# Midspan joints
if (is.numeric(midspan_joints)) {
if(midspan_joints < 3) {
midspan_joints <- as.character(midspan_joints)
} else if (midspan_joints > 2){
midspan_joints <- ">2"
}
}
oci_132kv_twr_line_cond_midspn <-
gb_ref$oci_132kv_twr_line_cond_midspn %>% dplyr::filter(
`Condition Criteria: No. of Midspan Joints` == midspan_joints
)
ci_factor_midspan_joints <-
oci_132kv_twr_line_cond_midspn$`Condition Input Factor`
ci_cap_midspan_joints <- oci_132kv_twr_line_cond_midspn$`Condition Input Cap`
ci_collar_midspan_joints <-
oci_132kv_twr_line_cond_midspn$`Condition Input Collar`
} else {
oci_ehv_twr_line_visal_cond <-
gb_ref$oci_ehv_twr_line_visal_cond %>% dplyr::filter(
`Condition Criteria: Observed Condition` == visual_cond
)
ci_factor_visual_cond <-
oci_ehv_twr_line_visal_cond$`Condition Input Factor`
ci_cap_visual_cond <-
oci_ehv_twr_line_visal_cond$`Condition Input Cap`
ci_collar_visual_cond <-
oci_ehv_twr_line_visal_cond$`Condition Input Collar`
# Midspan joints
if (is.numeric(midspan_joints)) {
if(midspan_joints < 3) {
midspan_joints <- as.character(midspan_joints)
} else if (midspan_joints > 2){
midspan_joints <- ">2"
}
}
oci_ehv_twr_cond_midspan_joint <-
gb_ref$oci_ehv_twr_cond_midspan_joint %>% dplyr::filter(
`Condition Criteria: No. of Midspan Joints` == midspan_joints
)
ci_factor_midspan_joints <-
oci_ehv_twr_cond_midspan_joint$`Condition Input Factor`
ci_cap_midspan_joints <- oci_ehv_twr_cond_midspan_joint$`Condition Input Cap`
ci_collar_midspan_joints <-
oci_ehv_twr_cond_midspan_joint$`Condition Input Collar`
}
# Observed conditions
factors <- c(ci_factor_visual_cond,
ci_factor_midspan_joints)
observed_condition_factor <- mmi(factors,
factor_divider_1,
factor_divider_2,
max_no_combined_factors)
caps <- c(ci_cap_visual_cond,
ci_cap_midspan_joints)
observed_condition_cap <- min(caps)
# Observed condition collar ----------------------------------------------
collars <- c(ci_collar_visual_cond,
ci_collar_midspan_joints)
observed_condition_collar <- max(collars)
# Observed condition modifier ---------------------------------------------
observed_condition_modifier <- data.frame(observed_condition_factor,
observed_condition_cap,
observed_condition_collar)
# Health score factor ---------------------------------------------------
health_score_factor <-
health_score_excl_ehv_132kv_tf(observed_condition_factor,
measured_condition_factor)
# Health score cap --------------------------------------------------------
health_score_cap <- min(observed_condition_cap, measured_condition_cap)
# Health score collar -----------------------------------------------------
health_score_collar <- max(observed_condition_collar,
measured_condition_collar)
# Health score modifier ---------------------------------------------------
health_score_modifier <- data.frame(health_score_factor,
health_score_cap,
health_score_collar)
# Current health score ----------------------------------------------------
current_health_score <-
current_health(initial_health_score,
health_score_modifier$health_score_factor,
health_score_modifier$health_score_cap,
health_score_modifier$health_score_collar,
reliability_factor = reliability_factor)
# Probability of failure ---------------------------------------------------
probability_of_failure <- k *
(1 + (c * current_health_score) +
(((c * current_health_score)^2) / factorial(2)) +
(((c * current_health_score)^3) / factorial(3)))
# Future probability of failure -------------------------------------------
# the Health Score of a new asset
H_new <- 0.5
# the Health Score of the asset when it reaches its Expected Life
b2 <- beta_2(current_health_score, age)
print(b2)
if (b2 > 2*b1){
b2 <- b1*2
} else if (current_health_score == 0.5){
b2 <- b1
}
if (current_health_score < 2) {
ageing_reduction_factor <- 1
} else if (current_health_score <= 5.5) {
ageing_reduction_factor <- ((current_health_score - 2)/7) + 1
} else {
ageing_reduction_factor <- 1.5
}
# Dynamic part
pof_year <- list()
future_health_score_list <- list()
year <- seq(from=0,to=simulation_end_year,by=1)
for (y in 1:length(year)){
t <- year[y]
future_health_Score <- current_health_score*exp((b2/ageing_reduction_factor) * t)
H <- future_health_Score
future_health_score_limit <- 15
if (H > future_health_score_limit){
H <- future_health_score_limit
} else if (H < 4) {
H <- 4
}
future_health_score_list[[paste(y)]] <- future_health_Score
pof_year[[paste(y)]] <- k * (1 + (c * H) +
(((c * H)^2) / factorial(2)) +
(((c * H)^3) / factorial(3)))
}
pof_future <- data.frame(
year=year,
PoF=as.numeric(unlist(pof_year)),
future_health_score = as.numeric(unlist(future_health_score_list)))
pof_future$age <- NA
pof_future$age[1] <- age
for(i in 2:nrow(pof_future)) {
pof_future$age[i] <- age + i -1
}
return(pof_future)
}
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