Nothing
#' @importFrom magrittr %>%
#' @title Future Probability of Failure for 0.4kV UG PEX Non Pressurised Cables
#' @description This function calculates the future
#' annual probability of failure per kilometer for a 0.4kV PEX non Pressurised cables
#' The function is a cubic curve that is based on
#' the first three terms of the Taylor series for an
#' exponential function.
#' @inheritParams pof_cables_04kv_pex
#' @param simulation_end_year Numeric. The last year of simulating probability
#' of failure. Default is 100.
#' @return DataFrame. Future probability of failure
#' along with future health score
#' @export
#' @examples
#' # future annual probability of failure for 0.4kV cable pex, 50 years old
#' pof_future_cables_04kv_pex(
#' utilisation_pct = 80,
#' operating_voltage_pct = 60,
#' sheath_test = "Default",
#' partial_discharge = "Default",
#' fault_hist = "Default",
#' reliability_factor = "Default",
#' age = 50,
#' k_value = 0.0658,
#' c_value = 1.087,
#' normal_expected_life = 80,
#' simulation_end_year = 100)
pof_future_cables_04kv_pex <-
function(utilisation_pct = "Default",
operating_voltage_pct = "Default",
sheath_test = "Default",
partial_discharge = "Default",
fault_hist = "Default",
reliability_factor = "Default",
age,
k_value = 0.0658,
c_value = 1.087,
normal_expected_life = 80,
simulation_end_year = 100) {
`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: Sheath Test Result` =
`Condition Criteria: Partial Discharge Test Result` =
NULL
pseudo_cable_type <- "33kV UG Cable (Non Pressurised)"
sub_division <- "Lead sheath - Copper conductor"
# Ref. table Categorisation of Assets and Generic Terms for Assets --
asset_category <- gb_ref$categorisation_of_assets %>%
dplyr::filter(`Asset Register Category` == pseudo_cable_type) %>%
dplyr::select(`Health Index Asset Category`) %>% dplyr::pull()
generic_term_1 <- gb_ref$generic_terms_for_assets %>%
dplyr::filter(`Health Index Asset Category` == asset_category) %>%
dplyr::select(`Generic Term...1`) %>% dplyr::pull()
generic_term_2 <- gb_ref$generic_terms_for_assets %>%
dplyr::filter(`Health Index Asset Category` == asset_category) %>%
dplyr::select(`Generic Term...2`) %>% dplyr::pull()
# Constants C and K for PoF function --------------------------------------
k <- k_value/100
c <- c_value
duty_factor_cable <-
duty_factor_cables(utilisation_pct,
operating_voltage_pct,
voltage_level = "HV")
# Expected life ------------------------------ # the expected life set to 80 accordingly to p. 33 in "DE-10kV apb kabler CNAIM"
expected_life_years <- expected_life(normal_expected_life,
duty_factor_cable,
location_factor = 1)
# b1 (Initial Ageing Rate) ------------------------------------------------
b1 <- beta_1(expected_life_years)
# Initial health score ----------------------------------------------------
initial_health_score <- initial_health(b1, age)
## NOTE
# Typically, the Health Score Collar is 0.5 and
# Health Score Cap is 5.5 (p. 33 in DE-10kV apb kabler CNAIM), implying no overriding
# of the Health Score. However, in some instances
# these parameters are set to other values in the
# Health Score Modifier calibration tables.
# Measured condition inputs ---------------------------------------------
asset_category_mmi <- stringr::str_remove(asset_category, pattern = "UG")
asset_category_mmi <- stringr::str_squish(asset_category_mmi)
mcm_mmi_cal_df <-
gb_ref$measured_cond_modifier_mmi_cal
mmi_type <- mcm_mmi_cal_df$`Asset Category`[which(
grepl(asset_category_mmi,
mcm_mmi_cal_df$`Asset Category`,
fixed = TRUE) == TRUE
)]
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`
)
# Sheath test -------------------------------------------------------------
mci_ehv_cbl_non_pr_sheath_test <-
gb_ref$mci_ehv_cbl_non_pr_sheath_test %>% dplyr::filter(
`Condition Criteria: Sheath Test Result` == sheath_test
)
ci_factor_sheath <- mci_ehv_cbl_non_pr_sheath_test$`Condition Input Factor`
ci_cap_sheath <- mci_ehv_cbl_non_pr_sheath_test$`Condition Input Cap`
ci_collar_sheath <- mci_ehv_cbl_non_pr_sheath_test$`Condition Input Collar`
# Partial discharge-------------------------------------------------------
mci_ehv_cbl_non_pr_prtl_disch <-
gb_ref$mci_ehv_cbl_non_pr_prtl_disch %>% dplyr::filter(
`Condition Criteria: Partial Discharge Test Result` == partial_discharge
)
ci_factor_partial <- mci_ehv_cbl_non_pr_prtl_disch$`Condition Input Factor`
ci_cap_partial <- mci_ehv_cbl_non_pr_prtl_disch$`Condition Input Cap`
ci_collar_partial <- mci_ehv_cbl_non_pr_prtl_disch$`Condition Input Collar`
# Fault -------------------------------------------------------
mci_ehv_cbl_non_pr_fault_hist <-
gb_ref$mci_ehv_cbl_non_pr_fault_hist
for (n in 2:4) {
if (fault_hist == 'Default' || fault_hist ==
'No historic faults recorded') {
no_row <- which(mci_ehv_cbl_non_pr_fault_hist$Upper == fault_hist)
ci_factor_fault <-
mci_ehv_cbl_non_pr_fault_hist$`Condition Input Factor`[no_row]
ci_cap_fault <-
mci_ehv_cbl_non_pr_fault_hist$`Condition Input Cap`[no_row]
ci_collar_fault <-
mci_ehv_cbl_non_pr_fault_hist$`Condition Input Collar`[no_row]
break
} else if (fault_hist >=
as.numeric(mci_ehv_cbl_non_pr_fault_hist$Lower[n]) &
fault_hist <
as.numeric(mci_ehv_cbl_non_pr_fault_hist$Upper[n])) {
ci_factor_fault <-
mci_ehv_cbl_non_pr_fault_hist$`Condition Input Factor`[n]
ci_cap_fault <-
mci_ehv_cbl_non_pr_fault_hist$`Condition Input Cap`[n]
ci_collar_fault <-
mci_ehv_cbl_non_pr_fault_hist$`Condition Input Collar`[n]
break
}
}
# Measured conditions
factors <- c(ci_factor_sheath,
ci_factor_partial,
ci_factor_fault)
measured_condition_factor <- mmi(factors,
factor_divider_1,
factor_divider_2,
max_no_combined_factors)
caps <- c(ci_cap_sheath,
ci_cap_partial,
ci_cap_fault)
measured_condition_cap <- min(caps)
# Measured condition collar -----------------------------------------------
collars <- c(ci_collar_sheath,
ci_collar_partial,
ci_collar_fault)
measured_condition_collar <- max(collars)
# Measured condition modifier ---------------------------------------------
measured_condition_modifier <- data.frame(measured_condition_factor,
measured_condition_cap,
measured_condition_collar)
# Health score factor ---------------------------------------------------
health_score_factor <- measured_condition_modifier$measured_condition_factor
# Health score cap --------------------------------------------------------
health_score_cap <- measured_condition_modifier$measured_condition_cap
# Health score collar -----------------------------------------------------
health_score_collar <- measured_condition_modifier$measured_condition_collar
# Health score modifier ---------------------------------------------------
health_score_modifier <- data.frame(health_score_factor,
health_score_cap,
health_score_collar)
# Current health score ----------------------------------------------------
current_health_score <-
current_health(
initial_health_score = initial_health_score,
health_score_factor= health_score_modifier$health_score_factor,
health_score_cap = health_score_modifier$health_score_cap,
health_score_collar = health_score_modifier$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)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.