R/sample_turbine_mCRM.R

In HiDef-Aerial-Surveying/stochLAB: Stochastic Collision Risk Model

#' Sampling function for a single turbine in the mCRM
#'
#' Samples and aggregates appropriate data for a single wind turbine
#'
#' @param rtn_speed_pars A single row data frame with columns `mean` and `sd`,
#'   the mean and standard deviation of the rotation speed.
#' @param bld_pitch_pars A single row data frame with columns `mean` and `sd`,
#'   the mean and standard deviation of the blade pitch angle,
#'   i.e. the angle between the blade surface and the rotor plane,
#'   in degrees. Assumed to follow a *tnorm-lw0* distribution.
#' @param rtr_radius_pars A single row data frame with columns `mean` and `sd`,
#'   the mean and standard deviation of the radius of the rotor, in metres.
#'   Assumed to follow a *tnorm-lw0* distribution.
#' @param bld_width_pars A single row data frame with columns `mean` and `sd`,
#'   the mean and standard deviation of the maximum blade width, in metres.
#'   Assumed to be *tnorm-lw0* distribution.
#' @param season_specs A data frame
#'   defining the seasons for aggregating over collision estimates. It must
#'   comprise the following columns:
#'   * `season_id`, (unique) season identifier,
#'   * `start_month`, name of the season's first month,
#'   * `end_month`, name of the season's last month.
#' @param trb_wind_avbl A data frame with the monthly estimates of operational
#'   wind availability. It must contain the columns:
#'   * `month`, (unique) month names,
#'   * `pctg`, the percentage of time wind conditions allow for turbine operation
#'   per month.
#' @param trb_downtime_pars A data frame with monthly estimates of maintenance
#'   downtime, assumed to follow a *tnorm-lw0* distribution. It
#'   must contain the following columns:
#'   * `month`, (unique) month names,
#'   * `mean`, numeric, the mean percentage of time in each month when turbines
#'   are not operating due to maintenance,
#'   * `sd`, the standard deviation of monthly maintenance downtime.
#' @param n_iter An integer value. The number of samples to generate
#'
#' @return A data frame of all the information sampled for the turbine with nrow = n_iter
#'
#' @examples
#' season_specs <- data.frame(
#'   season_id = c("PrBMigration", "PoBMigration","OMigration"),
#'   start_month = c("Mar", "May", "Oct"), end_month = c("Apr", "Sep", "Feb")
#'   )
#'
#'   windavb <- data.frame(
#'     month = month.abb,
#'     pctg = runif(12, 85, 98)
#'   )
#'
#'   dwntm <- data.frame(
#'     month = month.abb,
#'     mean = runif(12, 6, 10),
#'     sd = rep(2, 12))
#'
#'   sample_turbine_mCRM(rtn_speed_pars = data.frame(mean = 13.1, sd = 4),
#'                       bld_pitch_pars = data.frame(mean = 3, sd = 0.3),
#'                       rtr_radius_pars = data.frame(mean = 80, sd = 0),
#'                       bld_width_pars = data.frame(mean = 8, sd = 0),
#'                       season_specs = season_specs,
#'                       n_iter = 10,
#'                       trb_wind_avbl = windavb,
#'                       trb_downtime_pars = dwntm)
#' @export

sample_turbine_mCRM <- function(rtn_speed_pars,
                                bld_pitch_pars,
                                rtr_radius_pars,
                                bld_width_pars,
                                season_specs,
                                n_iter = 10,
                                trb_wind_avbl,
                                trb_downtime_pars){

  ## Create an empty dataframe to store information
  sampledTurbine <- data.frame(matrix(data = 0, ncol = 4, nrow = n_iter))
  names(sampledTurbine) <- c("RotorRadius", "BladeWidth", "RotorSpeed", "Pitch")

  rotorSpeed <- numeric()
  rotorPitch <- numeric()

  sampledTurbine$RotorSpeed<- rtnorm(n_iter, rtn_speed_pars$mean, rtn_speed_pars$sd, lower = 0)
  sampledTurbine$Pitch<- rtnorm(n_iter, bld_pitch_pars$mean, bld_pitch_pars$sd, lower = 0)
  #### Transform Pitch from degrees to radians, needed for Collision Risk Sheet
  sampledTurbine$Pitch <- sampledTurbine$Pitch*pi / 180

  # Radius  -----------------------------------------------------------------
  sampledTurbine$RotorRadius <- rep(rtr_radius_pars$mean,n_iter)

  # Blade width -------------------------------------------------------------
  sampledTurbine$BladeWidth <- rep(bld_width_pars$mean,n_iter)


  for(bp in 1:nrow(season_specs)){
    if(!is.na(season_specs$start_month[bp])){
      mnths <- seq_months(season_specs$start_month[bp],season_specs$end_month[bp])
      ## Get wind availability for all months in the season
      windavsamp <- trb_wind_avbl %>% dplyr::filter(month %in% mnths)
      ## Get downtime for all months in the season
      dwntmsamp <- trb_downtime_pars %>% dplyr::filter(month %in% mnths)
      ## Calculate pooled standard deviation across the months
      pooled_sd <- sqrt(mean(sapply(1:nrow(dwntmsamp),function(x){
        va <- dwntmsamp$sd[x]^2
      })))
      ## Calculate mean downtime
      mnDT <- mean(dwntmsamp$mean)
      ## Calculate mean operational time
      mnOpTi <- mean(windavsamp$pctg)
      downtimenm <- paste0(season_specs$season_id[bp],"_DT")
      Opttimenm <- paste0(season_specs$season_id[bp],"_OT")
      sampledTurbine[downtimenm] <- rtnorm(n_iter, mnDT, pooled_sd, lower = 0)/100
      sampledTurbine[Opttimenm] <- rep(mnOpTi,n_iter)/100
    }
  }

  return(sampledTurbine)
}