Source code for foxes.output.turbine_type_curves

from tabnanny import verbose
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from foxes.input.states import StatesTable
from foxes.core import WindFarm, Turbine
from foxes.algorithms import Downwind
from foxes.models.turbine_models import SetFarmVars
import foxes.variables as FV
import foxes.constants as FC
from .output import Output


[docs]class TurbineTypeCurves(Output): """ Creates power and ct curves for turbine types, optionally including derating/boost. Attributes ---------- mbook: foxes.models.ModelBook The model book :group: output """
[docs] def __init__(self, mbook): """ Constructor. Parameters ---------- mbook: foxes.models.ModelBook The model book """ self.mbook = mbook
[docs] def plot_curves( self, turbine_type, variables, P_max=None, titles=None, x_label=None, y_labels=None, ws_min=0.0, ws_max=30.0, ws_step=0.1, ti=0.05, rho=1.225, axs=None, figsize=None, pmax_args={}, **kwargs, ): """ Plot the power or ct curve. Parameters ---------- turbine_type: str The turbine type name from the model book variables: str or list of str For example FV.P or FV.CT P_max: float, optional The power mask value, if of interest titles: list of str, optional The plot titles, one for each variable x_label: str, optional The x axis label y_labels: list of str, optional The y axis lables, one for each variable ws_min: float The minimal wind speed ws_max: float The maximal wind speed ws_step: float The wind speed step size ti: float The TI value rho: float The air density value axs: list of pyplot.Axis, optional The axis, one for each variable figsize: tuple The figsize argument for plt.subplots() in case ax is not provided pmax_args: dict, optional Additionals parameters for plt.plot() for power mask case kwargs: dict, optional Additional parameters for plt.plot() Returns ------- axs: list of pyplot.Axis The plot axes, one for each variable """ vars = [variables] if isinstance(variables, str) else variables if isinstance(titles, str): titles = [titles] elif titles is None: titles = [None for v in vars] if isinstance(y_labels, str): y_labels = [y_labels] elif y_labels is None: y_labels = [None for v in vars] if not isinstance(axs, (list, tuple, np.ndarray)): axs = [axs] ws = np.arange(ws_min, ws_max + ws_step, ws_step, dtype=FC.DTYPE) n_states = len(ws) sdata = pd.DataFrame(index=range(n_states)) sdata.index.name = FC.STATE sdata[FV.WS] = ws models = [turbine_type] states = StatesTable( sdata, output_vars={FV.WS, FV.WD, FV.TI, FV.RHO}, fixed_vars={FV.WD: 270.0, FV.TI: ti, FV.RHO: rho}, ) farm = WindFarm() farm.add_turbine( Turbine(xy=[0.0, 0.0], turbine_models=models), verbosity=0, ) algo = Downwind(self.mbook, farm, states, wake_models=[], verbosity=0) results = algo.calc_farm() if P_max is not None: sname = f"_{type(self).__name__}_set_Pmax" self.mbook.turbine_models[sname] = SetFarmVars() self.mbook.turbine_models[sname].add_var(FV.MAX_P, P_max) models += [sname, "PMask"] farm = WindFarm() farm.add_turbine( Turbine(xy=[0.0, 0.0], turbine_models=models), verbosity=0, ) algo = Downwind(self.mbook, farm, states, wake_models=[], verbosity=0) results1 = algo.calc_farm() del self.mbook.turbine_models[sname] for i, v in enumerate(vars): ax = axs[i] if ax is None: __, ax = plt.subplots(figsize=figsize) pargs = {"linewidth": 2.5} pargs.update(kwargs) ax.plot(ws, results[v][:, 0], label="default", **pargs) if P_max is not None: pargs = {"linewidth": 1.8} pargs.update(pmax_args) ax.plot(ws, results1[v][:, 0], label="PMask", **pargs) if v == FV.P: vv = "Power curve" elif v == FV.CT: vv = "Thrust curve" else: vv = v t = f"{vv}, {turbine_type}" if titles[i] is None else titles[i] ax.set_title(t) l = "Wind speed [m/s]" if x_label is None else x_label ax.set_xlabel(l) if y_labels[i] is None: if v == FV.P: l = f"Power [kW]" elif v == FV.CT: l = "ct [-]" else: l = v else: l = y_labels[i] ax.set_ylabel(l) ax.grid() if P_max is not None: ax.legend() return axs