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scada_funcs_plots.py
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scada_funcs_plots.py
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from __future__ import annotations
import logging
from typing import TYPE_CHECKING
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from tabulate import tabulate
from wind_up.backporting import strict_zip
from wind_up.constants import SCATTER_ALPHA, SCATTER_MARKERSCALE, SCATTER_S, DataColumns
from wind_up.plots.misc_plots import bubble_plot
if TYPE_CHECKING:
from wind_up.models import PlotConfig, WindUpConfig
logger = logging.getLogger(__name__)
def plot_data_coverage_heatmap(df: pd.DataFrame, plot_title: str, plot_cfg: PlotConfig) -> None:
# calculate data coverage per turbine
covdf = df.groupby("TurbineName", observed=False).agg(
power=pd.NamedAgg(column="ActivePowerMean", aggfunc=lambda x: x.count() / x.size),
windspeed=pd.NamedAgg(column="WindSpeedMean", aggfunc=lambda x: x.count() / x.size),
yaw=pd.NamedAgg(column="YawAngleMean", aggfunc=lambda x: x.count() / x.size),
rpm=pd.NamedAgg(column="GenRpmMean", aggfunc=lambda x: x.count() / x.size),
pitch=pd.NamedAgg(column="PitchAngleMean", aggfunc=lambda x: x.count() / x.size),
)
plt.figure()
sns.heatmap(covdf, annot=True, fmt=".2f", vmax=1, vmin=min(0.5, covdf.min().min()))
plt.title(plot_title)
if plot_cfg.save_plots:
plt.savefig(plot_cfg.plots_dir / f"{plot_title}.png")
if plot_cfg.show_plots:
plt.show()
plt.close()
def calc_cf_by_turbine(scada_df: pd.DataFrame, cfg: WindUpConfig) -> pd.DataFrame:
rows_per_hour = 3600 / cfg.timebase_s
cf_df = scada_df.groupby("TurbineName", observed=False).agg(
hours=pd.NamedAgg(column="TurbineName", aggfunc=lambda x: x.count() / rows_per_hour),
MWh=pd.NamedAgg(column="ActivePowerMean", aggfunc=lambda x: x.sum() / rows_per_hour / 1000),
)
for i, rp in strict_zip(
[x.name for x in cfg.asset.wtgs],
[x.turbine_type.rated_power_kw for x in cfg.asset.wtgs],
):
cf_df.loc[i, "rated_power_kW"] = rp
cf_df["CF"] = cf_df["MWh"] / (cf_df["hours"] * cf_df["rated_power_kW"] / 1000)
return cf_df
def print_and_plot_capacity_factor(scada_df: pd.DataFrame, cfg: WindUpConfig, plots_cfg: PlotConfig) -> None:
cf_df = calc_cf_by_turbine(scada_df=scada_df, cfg=cfg)
title = f"{cfg.asset.name} capacity factor"
plots_cfg.plots_dir.mkdir(parents=True, exist_ok=True)
bubble_plot(
cfg=cfg,
series=cf_df["CF"] * 100,
title=f"{cfg.asset.name} capacity factor",
cbarunits="%",
save_path=plots_cfg.plots_dir / f"{title}.png",
show_plot=plots_cfg.show_plots,
)
logger.info(f'average capacity factor: {cf_df["CF"].mean() * 100:.1f}%')
_table = tabulate(
(cf_df.sort_values(by="CF", ascending=False)["CF"][0:3] * 100).to_frame(),
tablefmt="outline",
floatfmt=".1f",
)
logger.info(f"top 3 capacity factor [%]:\n{_table}")
_table = tabulate((cf_df.sort_values(by="CF")["CF"][0:3] * 100).to_frame(), tablefmt="outline", floatfmt=".1f")
logger.info(f"bottom 3 capacity factor [%]:\n{_table}")
def plot_ops_curves_per_ttype(cfg: WindUpConfig, df: pd.DataFrame, title_end: str, plot_cfg: PlotConfig) -> None:
for ttype in cfg.list_unique_turbine_types():
wtgs = cfg.list_turbine_ids_of_type(ttype)
df_ttype = df.loc[wtgs]
plot_ops_curves_one_ttype_or_wtg(
df=df_ttype,
ttype_or_wtg=ttype.turbine_type,
title_end=title_end,
plot_cfg=plot_cfg,
)
if not plot_cfg.skip_per_turbine_plots:
for wtg in wtgs:
plot_ops_curves_one_ttype_or_wtg(
df=df_ttype.loc[[wtg]],
ttype_or_wtg=wtg,
title_end=title_end,
plot_cfg=plot_cfg,
)
plot_ops_curve_timelines_one_wtg(
wtg_df=df_ttype.loc[wtg],
wtg_name=wtg,
title_end=title_end,
plot_cfg=plot_cfg,
)
def plot_ops_curves_one_ttype_or_wtg(df: pd.DataFrame, ttype_or_wtg: str, title_end: str, plot_cfg: PlotConfig) -> None:
plt.figure()
plt.scatter(df["WindSpeedMean"], df["ActivePowerMean"], s=SCATTER_S, alpha=SCATTER_ALPHA)
plot_title = f"{ttype_or_wtg} power curve {title_end}"
plt.title(plot_title)
plt.xlabel("WindSpeedMean [m/s]")
plt.ylabel("ActivePowerMean [kW]")
plt.grid()
if plot_cfg.show_plots:
plt.show()
if plot_cfg.save_plots:
t_dir = plot_cfg.plots_dir / ttype_or_wtg
t_dir.mkdir(exist_ok=True, parents=True)
plt.savefig(t_dir / f"{plot_title}.png")
plt.close()
# plot rpm and pitch vs power and wind speed in a 2 by 2 grid
plt.figure(figsize=(12, 8))
plt.subplot(2, 2, 1)
plt.scatter(df["ActivePowerMean"], df["GenRpmMean"], s=SCATTER_S, alpha=SCATTER_ALPHA)
plt.xlabel("ActivePowerMean [kW]")
plt.ylabel("GenRpmMean [RPM]")
plt.grid()
plt.subplot(2, 2, 2)
plt.scatter(df["WindSpeedMean"], df["GenRpmMean"], s=SCATTER_S, alpha=SCATTER_ALPHA)
plt.xlabel("WindSpeedMean [m/s]")
plt.ylabel("GenRpmMean [RPM]")
plt.grid()
plt.subplot(2, 2, 3)
plt.scatter(df["ActivePowerMean"], df["PitchAngleMean"], s=SCATTER_S, alpha=SCATTER_ALPHA)
plt.xlabel("ActivePowerMean [kW]")
plt.ylabel("PitchAngleMean [deg]")
plt.grid()
plt.subplot(2, 2, 4)
plt.scatter(df["WindSpeedMean"], df["PitchAngleMean"], s=SCATTER_S, alpha=SCATTER_ALPHA)
plt.xlabel("WindSpeedMean [m/s]")
plt.ylabel("PitchAngleMean [deg]")
plt.grid()
plot_title = f"{ttype_or_wtg} ops curves, {title_end}"
plt.suptitle(plot_title)
if plot_cfg.show_plots:
plt.show()
if plot_cfg.save_plots:
plt.savefig(t_dir / f"{plot_title}.png")
plt.close()
def plot_ops_curve_timelines_one_wtg(wtg_df: pd.DataFrame, wtg_name: str, title_end: str, plot_cfg: PlotConfig) -> None:
dropna_df = wtg_df.dropna(
subset=[
DataColumns.wind_speed_mean,
DataColumns.active_power_mean,
DataColumns.gen_rpm_mean,
DataColumns.pitch_angle_mean,
]
)
gen_df = dropna_df[dropna_df[DataColumns.active_power_mean] > 0].copy()
for descr, x_var, y_var, x_bin_width in [
("power curve", DataColumns.wind_speed_mean, DataColumns.active_power_mean, 1),
("rpm v power", DataColumns.active_power_mean, DataColumns.gen_rpm_mean, 0),
("pitch v ws", DataColumns.wind_speed_mean, DataColumns.pitch_angle_mean, 1),
]:
bins = np.arange(0, gen_df[x_var].max() + x_bin_width, x_bin_width) if x_bin_width > 0 else 10
mean_curve = gen_df.groupby(pd.cut(gen_df[x_var], bins=bins, retbins=False), observed=True).agg(
x_mean=pd.NamedAgg(column=x_var, aggfunc="mean"),
y_mean=pd.NamedAgg(column=y_var, aggfunc="mean"),
)
gen_df["expected_y"] = np.interp(gen_df[x_var], mean_curve["x_mean"], mean_curve["y_mean"])
daily_df = gen_df.resample("D").mean()
monthly_df = gen_df.resample("ME").mean()
if y_var == DataColumns.pitch_angle_mean:
daily_df["relative_y"] = daily_df[y_var] - daily_df["expected_y"]
monthly_df["relative_y"] = monthly_df[y_var] - monthly_df["expected_y"]
else:
daily_df["relative_y"] = (daily_df[y_var] / daily_df["expected_y"]).clip(0.5, 1.5)
monthly_df["relative_y"] = (monthly_df[y_var] / monthly_df["expected_y"]).clip(0.5, 1.5)
plt.figure()
plt.plot(daily_df.index, daily_df["relative_y"], label="daily")
plt.plot(monthly_df.index, monthly_df["relative_y"], label="monthly")
plot_title = f"{wtg_name} relative {descr} timeline {title_end}"
plt.title(plot_title)
plt.xlabel("date")
plt.ylabel(f"relative {descr}")
plt.legend()
plt.grid()
plt.tight_layout()
if plot_cfg.show_plots:
plt.show()
if plot_cfg.save_plots:
t_dir = plot_cfg.plots_dir / wtg_name
plt.savefig(t_dir / f"{plot_title}.png")
plt.close()
def plot_toggle_ops_curves_one_ttype_or_wtg(
input_df: pd.DataFrame,
*,
ttype_or_wtg: str,
title_end: str,
toggle_name: str,
ws_col: str,
pw_col: str,
pt_col: str,
rpm_col: str,
plot_cfg: PlotConfig,
sub_dir: str | None = None,
) -> None:
pd.set_option("future.no_silent_downcasting", True) # noqa FBT003
if "toggle_on" not in input_df.columns or "toggle_off" not in input_df.columns:
df_off = input_df[input_df["test_toggle_off"].fillna(value=False).infer_objects(copy=False)]
df_on = input_df[input_df["test_toggle_on"].fillna(value=False).infer_objects(copy=False)]
else:
df_off = input_df[input_df["toggle_off"].fillna(value=False).infer_objects(copy=False)]
df_on = input_df[input_df["toggle_on"].fillna(value=False).infer_objects(copy=False)]
plt.figure()
plt.scatter(
df_off[ws_col],
df_off[pw_col],
s=SCATTER_S,
alpha=SCATTER_ALPHA,
label=f"{toggle_name} OFF",
)
plt.scatter(
df_on[ws_col],
df_on[pw_col],
s=SCATTER_S,
alpha=SCATTER_ALPHA,
label=f"{toggle_name} ON",
)
plot_title = f"{ttype_or_wtg} power curve by {toggle_name}, {title_end}"
plt.title(plot_title)
plt.xlabel(f"{ws_col} [m/s]")
plt.ylabel(f"{pw_col} [kW]")
plt.grid()
plt.legend(loc="best", markerscale=SCATTER_MARKERSCALE)
if plot_cfg.show_plots:
plt.show()
if plot_cfg.save_plots:
t_dir = plot_cfg.plots_dir / ttype_or_wtg if sub_dir is None else plot_cfg.plots_dir / sub_dir
t_dir.mkdir(exist_ok=True, parents=True)
plt.savefig(t_dir / f"{plot_title}.png")
plt.close()
# plot rpm and pitch vs power and wind speed in a 2 by 2 grid
plt.figure(figsize=(12, 8))
plt.subplot(2, 2, 1)
plt.scatter(
df_off[pw_col],
df_off[rpm_col],
s=SCATTER_S,
alpha=SCATTER_ALPHA,
label=f"{toggle_name} OFF",
)
plt.scatter(
df_on[pw_col],
df_on[rpm_col],
s=SCATTER_S,
alpha=SCATTER_ALPHA,
label=f"{toggle_name} ON",
)
plt.xlabel(f"{pw_col} [kW]")
plt.ylabel(f"{rpm_col} [RPM]")
plt.grid()
plt.legend(loc="best", markerscale=SCATTER_MARKERSCALE)
plt.subplot(2, 2, 2)
plt.scatter(df_off[ws_col], df_off[rpm_col], s=SCATTER_S, alpha=SCATTER_ALPHA)
plt.scatter(df_on[ws_col], df_on[rpm_col], s=SCATTER_S, alpha=SCATTER_ALPHA)
plt.xlabel(f"{ws_col} [m/s]")
plt.ylabel(f"{rpm_col} [RPM]")
plt.grid()
plt.subplot(2, 2, 3)
plt.scatter(df_off[pw_col], df_off[pt_col], s=SCATTER_S, alpha=SCATTER_ALPHA)
plt.scatter(df_on[pw_col], df_on[pt_col], s=SCATTER_S, alpha=SCATTER_ALPHA)
plt.xlabel(f"{pw_col} [kW]")
plt.ylabel(f"{pt_col} [deg]")
plt.grid()
plt.subplot(2, 2, 4)
plt.scatter(df_off[ws_col], df_off[pt_col], s=SCATTER_S, alpha=SCATTER_ALPHA)
plt.scatter(df_on[ws_col], df_on[pt_col], s=SCATTER_S, alpha=SCATTER_ALPHA)
plt.xlabel(f"{ws_col} [m/s]")
plt.ylabel(f"{pt_col} [deg]")
plt.grid()
plot_title = f"{ttype_or_wtg} ops curves by {toggle_name}, {title_end}"
plt.suptitle(plot_title)
if plot_cfg.show_plots:
plt.show()
if plot_cfg.save_plots:
plt.savefig(t_dir / f"{plot_title}.png")
plt.close()
def compare_ops_curves_pre_post(
pre_df: pd.DataFrame,
post_df: pd.DataFrame,
*,
wtg_name: str,
ws_col: str,
pw_col: str,
pt_col: str,
rpm_col: str,
plot_cfg: PlotConfig,
is_toggle_test: bool,
sub_dir: str | None = None,
) -> None:
if is_toggle_test:
plot_toggle_ops_curves_one_ttype_or_wtg(
input_df=pd.concat([pre_df, post_df]),
ttype_or_wtg=wtg_name,
title_end="power performance data",
toggle_name="toggle",
ws_col=ws_col,
pw_col=pw_col,
pt_col=pt_col,
rpm_col=rpm_col,
plot_cfg=plot_cfg,
sub_dir=sub_dir,
)
else:
pre_df_fake_toggle = pre_df.copy()
post_df_fake_toggle = post_df.copy()
pre_df_fake_toggle["test_toggle_off"] = True
post_df_fake_toggle["test_toggle_on"] = True
plot_toggle_ops_curves_one_ttype_or_wtg(
input_df=pd.concat([pre_df_fake_toggle, post_df_fake_toggle]),
ttype_or_wtg=wtg_name,
title_end="power performance data",
toggle_name="upgrade",
ws_col=ws_col,
pw_col=pw_col,
pt_col=pt_col,
rpm_col=rpm_col,
plot_cfg=plot_cfg,
sub_dir=sub_dir,
)
def plot_filter_rpm_and_pt_curve_one_ttype_or_wtg(
df: pd.DataFrame,
ttype_or_wtg: str,
pt_v_pw_curve: pd.DataFrame,
pt_v_ws_curve: pd.DataFrame,
rpm_v_pw_curve: pd.DataFrame,
rpm_v_ws_curve: pd.DataFrame,
plot_cfg: PlotConfig,
) -> None:
plt.figure(figsize=(12, 8))
plt.subplot(2, 2, 1)
plt.scatter(df["pw_clipped"], df["GenRpmMean"], s=SCATTER_S, alpha=SCATTER_ALPHA)
x = [rpm_v_pw_curve.index[0].left] + [x.mid for x in rpm_v_pw_curve.index] + [rpm_v_pw_curve.index[-1].right]
y = [rpm_v_pw_curve["y_limit"].iloc[0], *list(rpm_v_pw_curve["y_limit"]), rpm_v_pw_curve["y_limit"].iloc[-1]]
plt.plot(x, y, color="red")
plt.xlabel("pw_clipped [kW]")
plt.ylabel("GenRpmMean [deg]")
plt.grid()
plt.subplot(2, 2, 2)
plt.scatter(df["WindSpeedMean"], df["GenRpmMean"], s=SCATTER_S, alpha=SCATTER_ALPHA)
x = [rpm_v_ws_curve.index[0].left] + [x.mid for x in rpm_v_ws_curve.index] + [rpm_v_ws_curve.index[-1].right]
y = [rpm_v_ws_curve["y_limit"].iloc[0], *list(rpm_v_ws_curve["y_limit"]), rpm_v_ws_curve["y_limit"].iloc[-1]]
plt.plot(x, y, color="red")
plt.xlabel("WindSpeedMean [m/s]")
plt.ylabel("GenRpmMean [deg]")
plt.grid()
plt.subplot(2, 2, 3)
plt.scatter(df["pw_clipped"], df["PitchAngleMean"], s=SCATTER_S, alpha=SCATTER_ALPHA)
x = [pt_v_pw_curve.index[0].left] + [x.mid for x in pt_v_pw_curve.index] + [pt_v_pw_curve.index[-1].right]
y = [pt_v_pw_curve["y_limit"].iloc[0], *list(pt_v_pw_curve["y_limit"]), pt_v_pw_curve["y_limit"].iloc[-1]]
plt.plot(x, y, color="red")
plt.xlabel("pw_clipped [kW]")
plt.ylabel("PitchAngleMean [deg]")
plt.grid()
plt.subplot(2, 2, 4)
plt.scatter(df["WindSpeedMean"], df["PitchAngleMean"], s=SCATTER_S, alpha=SCATTER_ALPHA)
x = [pt_v_ws_curve.index[0].left] + [x.mid for x in pt_v_ws_curve.index] + [pt_v_ws_curve.index[-1].right]
y = [pt_v_ws_curve["y_limit"].iloc[0], *list(pt_v_ws_curve["y_limit"]), pt_v_ws_curve["y_limit"].iloc[-1]]
plt.plot(x, y, color="red")
plt.xlabel("WindSpeedMean [m/s]")
plt.ylabel("PitchAngleMean [deg]")
plt.grid()
plot_title = f"{ttype_or_wtg} rpm and pitch curve filters"
plt.suptitle(plot_title)
if plot_cfg.show_plots:
plt.show()
if plot_cfg.save_plots:
(plot_cfg.plots_dir / ttype_or_wtg).mkdir(exist_ok=True, parents=True)
plt.savefig(plot_cfg.plots_dir / ttype_or_wtg / f"{plot_title}.png")
plt.close()
def print_filter_stats(
filter_name: str,
na_rows: int,
total_rows: int,
*,
just_yaw: bool = False,
just_min_max: bool = False,
reason: str = "",
) -> None:
min_max_str = " Min & Max" if just_min_max else ""
if len(reason) > 0:
reason = f" because of {reason}"
if just_yaw:
logger.info(
f"{filter_name} set {na_rows} rows [{100 * na_rows / total_rows:.1f}%] to NA yaw{min_max_str}{reason}"
)
else:
logger.info(f"{filter_name} set {na_rows} rows [{100 * na_rows / total_rows:.1f}%] to NA{reason}")