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#' @importFrom magrittr %>%
#' @title Current Probability of Failure for Poles
#' @description This function calculates the current
#' annual probability of failure per kilometer Poles
#' The function is a cubic curve that is based on
#' the first three terms of the Taylor series for an
#' exponential function. For more information about the
#' probability of failure function see section 6
#' on page 34 in CNAIM (2021).
#' @param pole_asset_category String The type of asset category
#' @param sub_division String. Refers to material the pole is made of.
#' @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. See page 111,
#' table 23 in CNAIM (2021). 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 See page 110,
#' table 22 in CNAIM (2021). 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")}.
#' See page 161, table 199 and 201 in CNAIM (2021).
#' @inheritParams current_health
#' @return DataFrame Current probability of failure
#' per annum per kilometer along with current health score.
#' @source DNO Common Network Asset Indices Methodology (CNAIM),
#' Health & Criticality - Version 2.1, 2021:
#' \url{https://www.ofgem.gov.uk/sites/default/files/docs/2021/04/dno_common_network_asset_indices_methodology_v2.1_final_01-04-2021.pdf}
#' @export
#' @examples
#' # Current annual probability of failure for HV Poles
#' pof_poles(
#' pole_asset_category = "20kV Poles",
#' sub_division = "Wood",
#' placement = "Default",
#' altitude_m = "Default",
#' distance_from_coast_km = "Default",
#' corrosion_category_index = "Default",
#' age = 10,
#' observed_condition_inputs =
#' list("visual_pole_cond" =
#' list("Condition Criteria: Pole Top Rot Present?" = "Default"),
#' "pole_leaning" = list("Condition Criteria: Pole Leaning?" = "Default"),
#' "bird_animal_damage" =
#' list("Condition Criteria: Bird/Animal Damage?" = "Default"),
#' "top_rot" = list("Condition Criteria: Pole Top Rot Present?" = "Default")),
#' measured_condition_inputs =
#' list("pole_decay" =
#' list("Condition Criteria: Degree of Decay/Deterioration" = "Default")),
#' reliability_factor = "Default")
pof_poles <-
function(pole_asset_category = "20kV Poles",
sub_division = "Wood",
placement = "Default",
altitude_m = "Default",
distance_from_coast_km = "Default",
corrosion_category_index = "Default",
age,
measured_condition_inputs,
observed_condition_inputs,
reliability_factor = "Default") {
`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` = NULL
# due to NSE notes in R CMD check
asset_category <- gb_ref$categorisation_of_assets %>%
dplyr::filter(`Asset Register Category` ==
pole_asset_category) %>%
dplyr::select(`Health Index Asset Category`) %>% dplyr::pull()
generic_term_1 <- gb_ref$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` ==
pole_asset_category,
`Sub-division` == sub_division) %>%
dplyr::pull()
# Constants C and K for PoF function --------------------------------------
# POF function asset category.
pof_asset_category <- "Poles"
k <- gb_ref$pof_curve_parameters %>%
dplyr::filter(`Functional Failure Category` %in% pof_asset_category) %>%
dplyr::select(`K-Value (%)`) %>%
dplyr::pull()/100
c <- gb_ref$pof_curve_parameters %>%
dplyr::filter(`Functional Failure Category` %in% pof_asset_category) %>%
dplyr::select(`C-Value`) %>%
dplyr::pull()
# 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 = pole_asset_category,
sub_division = sub_division)
# 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)
# Measured conditions
# The table data is same for all poles category
mci_table_names <- list("pole_decay" = "mci_ehv_pole_pole_decay_deter")
# The table data is same for all poles category
asset_category_mmi <- "HV Poles"
measured_condition_modifier <-
get_measured_conditions_modifier_hv_switchgear(asset_category_mmi,
mci_table_names,
measured_condition_inputs)
# Observed conditions -----------------------------------------------------
# The table data is same for all poles category
oci_table_names <- list("visual_pole_cond" = "oci_hv_pole_visual_pole_cond",
"pole_leaning" = "oci_ehv_pole_pole_leaning",
"bird_animal_damage" = "oci_ehv_pole_bird_animal_damag",
"top_rot" = "oci_ehv_pole_pole_top_rot")
observed_condition_modifier <-
get_observed_conditions_modifier_hv_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)))
return(data.frame(pof = probability_of_failure, chs = current_health_score))
}
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