R/pof_cables_04kv_pex.R

In CNAIM: Common Network Asset Indices Methodology (CNAIM)

#' @importFrom magrittr %>%
#' @title Current Probability of Failure for 0.4kV UG PEX Non Pressurised Cables
#' @description This function calculates the current
#' 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 duty_factor_cables
#' @param sheath_test String. Only applied for non pressurised cables.
#' Indicating the state of the sheath. Options:
#' \code{sheath_test = c("Pass", "Failed Minor", "Failed Major",
#' "Default")}.
#' @param partial_discharge String. Only applied for non pressurised cables.
#' Indicating the level of partial discharge. Options:
#' \code{partial_discharge = c("Low", "Medium", "High",
#'  "Default")}.
#' @param fault_hist Numeric. Only applied for non pressurised cables.
#' The calculated fault rate for the cable in the period per kilometer.
#' A setting of \code{"No historic faults recorded"}
#' indicates no fault.
#' @inheritParams current_health
#' @param age Numeric. The current age in years of the cable.
#' @param k_value Numeric. \code{k_value = 0.0658} by default.
#' @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 = 80} by default.
#' @return DataFrame Current probability of failure
#' per annum per kilometer along with current health score.
#' @export
#' @examples
#' # Current annual probability of failure for 0.4kV non pressurised pex cable, 50 years old
#' pof_cables_04kv_pex(
#' utilisation_pct = 80,
#' operating_voltage_pct = "Default",
#' 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)
pof_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) {

  `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


  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` == 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 ------------------------------
  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, 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)))

  return(data.frame(pof = probability_of_failure, chs = current_health_score))
}