# R/turbine_influences.R In windfarmGA: Genetic Algorithm for Wind Farm Layout Optimization

#### Documented in get_dist_anglesturbine_influences

```#' @title Find potentially influencing turbines
#' @name turbine_influences
#' @description Find all turbines that could potentially influence another
#'   turbine and save them to a list.
#'
#' @export
#'
#' @inheritParams calculate_energy
#' @param t A data.frame of the current individual with X and Y coordinates
#' @param dist A numeric value indicating the distance, after which the wake
#'   effects are considered to be eliminated.
#' @param polYgon A shapefile representing the considered area
#' @param dirct A numeric value indicating the current wind direction
#' @param plotAngles A logical variable, which is used to plot the distances and
#'   angles. Default is \code{FALSE}
#'
#' @family Wind Energy Calculation Functions
#' @return Returns a list of all individuals of the current generation which
#'   could potentially influence other turbines. List includes the relevant
#'   coordinates, the distances and angles in between and assigns the Point ID.
#'
#' @examples
#' ## Exemplary input Polygon with 2km x 2km:
#' library(sf)
#'
#' Polygon1 <- sf::st_as_sf(sf::st_sfc(
#'   sf::st_polygon(list(cbind(
#'     c(0, 0, 2000, 2000, 0),
#'     c(0, 2000, 2000, 0, 0)))),
#'   crs = 3035
#' ))
#'
#' t <- st_coordinates(st_sample(Polygon1, 10))
#' wnkl <- 20
#' dist <- 100000
#' dirct <- 0
#'
#' res <- turbine_influences(t, wnkl, dist, Polygon1, dirct, plotAngles = TRUE)
#'
turbine_influences <- function(t, wnkl, dist, polYgon, dirct,
plotAngles = FALSE) {
## For every turbine in the wind farm, find all other turbines,
## that stand in front, next and inside a certain angle of the
## incoming wind direction and assing to the list
lapply(seq_along(t[, 1]), function(i) {
## Calculate the angles and distances of potentially influencing turbines
ee <- get_dist_angles(t = t, o = i, wnkl = wnkl, dist = dist,
polYgon = polYgon, plotAngles = plotAngles)
## Add the wind direction to the data.frame
## Assign the iteration as point ID of the current turbine
## Necessary for multiple wake effects
cbind(ee, "Windrichtung" = dirct, "Punkt_id" = i)
})
}

#' @title Calculate distances and angles of possibly influencing turbines
#' @name get_dist_angles
#' @description Calculate distances and angles for a turbine and all it's
#'   potentially influencing turbines.
#' @export
#'
#' @param o A numeric value indicating the index of the current turbine
#' @inheritParams turbine_influences
#'
#' @family Wind Energy Calculation Functions
#' @return Returns a matrix with the distances and angles of potentially
#'   influencing turbines
#' @examples
#' library(sf)
#'
#' ## Exemplary input Polygon with 2km x 2km:
#' Polygon1 <- sf::st_as_sf(sf::st_sfc(
#'   sf::st_polygon(list(cbind(
#'     c(4498482, 4498482, 4499991, 4499991, 4498482),
#'     c(2668272, 2669343, 2669343, 2668272, 2668272)))),
#'   crs = 3035
#' ))
#'
#' ## Create a random windfarm with 10 turbines
#' t <- st_coordinates(st_sample(Polygon1, 10))
#' wnkl <- 20
#' dist <- 100000
#'
#' ## Evaluate and plot for every turbine all other potentially influencing turbines
#' potInfTur <- list()
#' for (i in 1:(length(t[,1]))) {
#'   potInfTur[[i]] <- get_dist_angles(t = t, o = i, wnkl = wnkl,
#'                                     dist = dist, polYgon = Polygon1, plotAngles = TRUE)
#' }
#' potInfTur
#'
get_dist_angles <- function(t, o, wnkl, dist, polYgon, plotAngles = FALSE) {
col_names <- c("Ax", "Ay", "Bx", "By", "Cx", "Cy",
"Laenge_C", "Laenge_B", "Laenge_A",
"alpha", "betha", "gamma")
## Get coordinates of actual turbine location
turbine_loc <- c(x = t[o, 1L], y = t[o, 2L])
## Find all turbines that are in front of actual turbine
turbines_ahead <- subset.matrix(x = t, subset = (t[o, 2L] < t[, 2L]))

if (plotAngles) {
graphics::plot(t[,1], t[,2],
col.axis = "darkblue",
xlab = "X-Coordinates", ylab = "Y-Coordinates")
title(main = "Potentially Influential Turbines",
sub = paste("PointNr: ", o,";","Distance: ",
dist,"Meter",";", "Angle: ", wnkl,"Degrees"),
outer = FALSE, cex.main = 1, cex.sub = 1)
## Plot the actual turbine in green, the ones in front in red
points(x = turbine_loc, y = turbine_loc, col = "green", pch = 20, cex = 2)
angles_min <- rbind(turbine_loc,
cbind(turbine_loc+dist*cos((90-wnkl)*pi/180),
turbine_loc+dist*sin((90-wnkl)*pi/180)))
angles_plu <- rbind(turbine_loc,
cbind(turbine_loc+dist*cos((90+wnkl)*pi/180),
turbine_loc+dist*sin((90+wnkl)*pi/180)))
lines(angles_min[,1],angles_min[,2])
lines(angles_plu[,1],angles_plu[,2])
points(x = turbines_ahead[,1], y = turbines_ahead[,2], col = "red", pch = 20)
}

## Are there turbines in front or not? If yes, calculate distances and
## angles and check if they have an influence or not
if (len2 != 0L) {
## If turbines are in front of the current turbine, create a list and
## save only the ones that could possibly influence others
datalist <- lapply(1:len2, function(i) {
c(P2LFu, winkel)
})
res <- matrix(unlist(datalist), ncol = 12, byrow = TRUE)
colnames(res) <- col_names

## Dismiss the ones with too high distances or big angles.
dl <- subset.matrix(res,
subset = res[, 'alpha'] < wnkl & res[, 'Laenge_B'] < dist)

if (plotAngles) {
points(dl[,'Ax'], dl[,'Ay'], col = "orange", pch = 20, cex = 2)
}
## Are turbines left after subsetting?
if (nrow(dl) != 0) {
## If possible influencing turbines exist, save them in the variable "DataLun3"
DataLun3 <- dl
} else {
## If no possible influencing turbines remain and the variable "dl" therefoe is empty,
## the variable "DataLun3" is filled with default Values of 0 for distances and angles
DataLun3 <- matrix(data = c(0, 0, t[o, 1], t[o, 2], rep(0, 8)), nrow = 1, ncol = 12)
colnames(DataLun3) <- col_names
}
} else {
# If no turbines are in front, the variabel "DataLun3" is again filled with default
## values of 0 for angles and distances.
DataLun3 <- matrix(data = c(0, 0, t[o, 1], t[o, 2], rep(0, 8)), nrow = 1, ncol = 12)
colnames(DataLun3) <- col_names
}

return(DataLun3)
}
```

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windfarmGA documentation built on May 5, 2021, 5:08 p.m.