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Sampling function for a single turbine in the mCRM

Source: R/sample_turbine_mCRM.R
sample_turbine_mCRM.Rd

Samples and aggregates appropriate data for a single wind turbine

Usage

sample_turbine_mCRM(
  rtn_speed_pars,
  bld_pitch_pars,
  rtr_radius_pars,
  bld_width_pars,
  season_specs,
  n_iter = 10,
  trb_wind_avbl,
  trb_downtime_pars
)

Arguments

rtn_speed_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the rotation speed.

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.

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.

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.

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.

n_iter

An integer value. The number of samples to generate

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.

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.

Value

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)
#>    RotorRadius BladeWidth RotorSpeed      Pitch PrBMigration_DT PrBMigration_OT
#> 1           80          8  11.601249 0.06136483      0.10935238       0.9004818
#> 2           80          8  13.608397 0.05353304      0.08944869       0.9004818
#> 3           80          8   8.887488 0.04858740      0.08270511       0.9004818
#> 4           80          8   8.562781 0.04535419      0.09356532       0.9004818
#> 5           80          8   9.654915 0.05613066      0.07500891       0.9004818
#> 6           80          8   5.849815 0.05870977      0.11590970       0.9004818
#> 7           80          8  21.483483 0.05538532      0.07309718       0.9004818
#> 8           80          8  17.289497 0.06055223      0.11872701       0.9004818
#> 9           80          8  12.881980 0.05536382      0.04533733       0.9004818
#> 10          80          8  19.795382 0.05153243      0.11028632       0.9004818
#>    PoBMigration_DT PoBMigration_OT OMigration_DT OMigration_OT
#> 1       0.07932316       0.9079167    0.10018255     0.9257853
#> 2       0.07501109       0.9079167    0.10820380     0.9257853
#> 3       0.05356090       0.9079167    0.06694812     0.9257853
#> 4       0.07868917       0.9079167    0.03845806     0.9257853
#> 5       0.06082757       0.9079167    0.05248013     0.9257853
#> 6       0.08843688       0.9079167    0.08347915     0.9257853
#> 7       0.05997408       0.9079167    0.14004056     0.9257853
#> 8       0.10399099       0.9079167    0.05301178     0.9257853
#> 9       0.05903683       0.9079167    0.06425183     0.9257853
#> 10      0.06600393       0.9079167    0.07014271     0.9257853