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#' @importFrom magrittr %>%
#' @title Current Probability of Failure for Meters
#' @description This function calculates the current
#' annual probability of failure meter
#' The function is a cubic curve that is based on
#' the first three terms of the Taylor series for an
#' exponential function.
#' @param placement String. Specify if the asset is located outdoor or indoor.
#' @param altitude_m Numeric. Specify the altitude location for
#' the asset measured in meters from sea level.\code{altitude_m}
#' is used to derive the altitude factor. A setting of \code{"Default"}
#' will set the altitude factor to 1 independent of \code{asset_type}.
#' @param distance_from_coast_km Numeric. Specify the distance from the
#' coast measured in kilometers. \code{distance_from_coast_km} is used
#' to derive the distance from coast factor. A setting of \code{"Default"} will set the
#' distance from coast factor to 1 independent of \code{asset_type}.
#' @param corrosion_category_index Integer.
#' Specify the corrosion index category, 1-5.
#' @param age Numeric. The current age in years of the conductor.
#' @param measured_condition_inputs Named list observed_conditions_input
#' @param observed_condition_inputs Named list observed_conditions_input
#' \code{conductor_samp = c("Low","Medium/Normal","High","Default")}.
#' @inheritParams current_health
#' @param k_value Numeric. \code{k_value = 0.128} 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 = 50} by default.
#' The default value is accordingly to the CNAIM standard on page 107.
#' @return DataFrame Current probability of failure
#' per annum per kilometer along with current health score.
#' @export
#' @examples
#' # Current annual probability of failure for meter
#' pof_meter(
#' placement = "Default",
#' altitude_m = "Default",
#' distance_from_coast_km = "Default",
#' corrosion_category_index = "Default",
#' age = 10,
#' observed_condition_inputs =
#' list("external_condition" =
#' list("Condition Criteria: Observed Condition" = "Default"),
#' "oil_gas" = list("Condition Criteria: Observed Condition" = "Default"),
#' "thermo_assment" = list("Condition Criteria: Observed Condition" = "Default"),
#' "internal_condition" = list("Condition Criteria: Observed Condition" = "Default"),
#' "indoor_env" = list("Condition Criteria: Observed Condition" = "Default")),
#' measured_condition_inputs =
#' list("partial_discharge" =
#' list("Condition Criteria: Partial Discharge Test Results" = "Default"),
#' "ductor_test" = list("Condition Criteria: Ductor Test Results" = "Default"),
#' "oil_test" = list("Condition Criteria: Oil Test Results" = "Default"),
#' "temp_reading" = list("Condition Criteria: Temperature Readings" = "Default"),
#' "trip_test" = list("Condition Criteria: Trip Timing Test Result" = "Default")),
#' reliability_factor = "Default",
#' k_value = 0.128,
#' c_value = 1.087,
#' normal_expected_life = 25)
pof_meter <-
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 = 0.128,
c_value = 1.087,
normal_expected_life = 25) {
hv_asset_category <- "6.6/11kV CB (GM) Secondary"
`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$categorisation_of_assets %>%
dplyr::filter(`Asset Register Category` ==
hv_asset_category) %>%
dplyr::select(`Health Index Asset Category`) %>% dplyr::pull()
generic_term_1 <- gb_ref$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()
# 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 = hv_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)
# Measured conditions
mci_table_names <- list("partial_discharge" = "mci_hv_swg_distr_prtl_dischrg",
"ductor_test" = "mci_hv_swg_distr_ductor_test",
"oil_test" = "mci_hv_swg_distr_oil_test",
"temp_reading" = "mci_hv_swg_distr_temp_reading",
"trip_test" = "mci_hv_swg_distr_trip_test")
measured_condition_modifier <-
get_measured_conditions_modifier_hv_switchgear(asset_category,
mci_table_names,
measured_condition_inputs)
# Observed conditions -----------------------------------------------------
oci_table_names <- list("external_condition" = "oci_hv_swg_dist_swg_ext_cond",
"oil_gas" = "oci_hv_swg_dist_oil_lek_gas_pr",
"thermo_assment" = "oci_hv_swg_dist_thermo_assment",
"internal_condition" = "oci_hv_swg_dist_swg_int_cond",
"indoor_env" = "oci_hv_swg_dist_indoor_environ")
observed_condition_modifier <-
get_observed_conditions_modifier_hv_switchgear(asset_category,
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)))
return(data.frame(pof = probability_of_failure, chs = current_health_score))
}
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