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#' @importFrom magrittr %>%
#' @title Future Probability of Failure for 0.4kV Pillar
#' @description This function calculates the future
#' annual probability of failure per kilometer 0.4kV Pillar.
#' The function is a cubic curve that is based on
#' the first three terms of the Taylor series for an
#' exponential function.
#' @inheritParams pof_ehv_fittings
#' @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.
#' @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 Pillar
#' pof_future_pillar_04kv(
#' placement = "Default",
#' altitude_m = "Default",
#' distance_from_coast_km = "Default",
#' corrosion_category_index = "Default",
#' age = 10,
#' observed_condition_inputs =
#' list("external_cond" =
#' list("Condition Criteria: Observed Condition" = "Default"),
#' "compound_leaks" = list("Condition Criteria: Observed Condition" = "Default"),
#' "internal_cond" = list("Condition Criteria: Observed Condition" = "Default"),
#' "insulation" = list("Condition Criteria: Observed Condition" = "Default"),
#' "signs_heating" = list("Condition Criteria: Observed Condition" = "Default"),
#' "phase_barriers" = list("Condition Criteria: Observed Condition" = "Default")),
#' measured_condition_inputs =
#' list("opsal_adequacy" =
#' list("Condition Criteria: Operational Adequacy" = "Default")),
#' reliability_factor = "Default",
#' k_value = 0.0046,
#' c_value = 1.087,
#' normal_expected_life = 60,
#' simulation_end_year = 100)
pof_future_pillar_04kv <-
function(placement = "Default",
altitude_m = "Default",
distance_from_coast_km = "Default",
corrosion_category_index = "Default",
age,
measured_condition_inputs,
observed_condition_inputs,
reliability_factor = "Default",
k_value = 0.0046,
c_value = 1.087,
normal_expected_life = 60,
simulation_end_year = 100){
lv_asset_category <- "LV Pillar (ID)"
`Asset Register Category` = `Health Index Asset Category` =
`Generic Term...1` = `Generic Term...2` = `Functional Failure Category` =
`K-Value (%)` = `C-Value` = `Asset Register Category` = NULL
# due to NSE notes in R CMD check
asset_category <- gb_ref$categorisation_of_assets %>%
dplyr::filter(`Asset Register Category` ==
lv_asset_category) %>%
dplyr::select(`Health Index Asset Category`) %>% dplyr::pull()
generic_term_1 <- gb_ref$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` ==
lv_asset_category) %>%
dplyr::pull()
# 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 = lv_asset_category)
# Expected life ------------------------------
expected_life_years <- expected_life(normal_expected_life,
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)
asset_category_mmi <- get_mmi_lv_switchgear_asset_category(lv_asset_category)
# Measured conditions
mci_table_names <- list("opsal_adequacy" = "mci_lv_pillar_opsal_adequacy")
measured_condition_modifier <-
get_measured_conditions_modifier_lv_switchgear(asset_category_mmi,
mci_table_names,
measured_condition_inputs)
# Observed conditions -----------------------------------------------------
oci_table_names <- list(
"external_cond" = "oci_lv_pillar_swg_ext_cond",
"compound_leaks" = "oci_lv_pillar_compound_leak",
"internal_cond" = "oci_lv_pillar_swg_int_cond_op",
"insulation" = "oci_lv_pillar_insulation_cond",
"signs_heating" = "oci_lv_pillar_signs_heating",
"phase_barriers" = "oci_lv_pillar_phase_barrier"
)
observed_condition_modifier <-
get_observed_conditions_modifier_lv_switchgear(asset_category_mmi,
oci_table_names,
observed_condition_inputs)
# Health score factor ---------------------------------------------------
health_score_factor <-
health_score_excl_ehv_132kv_tf(observed_condition_modifier$condition_factor,
measured_condition_modifier$condition_factor)
# Health score cap --------------------------------------------------------
health_score_cap <- min(observed_condition_modifier$condition_cap,
measured_condition_modifier$condition_cap)
# Health score collar -----------------------------------------------------
health_score_collar <- max(observed_condition_modifier$condition_collar,
measured_condition_modifier$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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