energy-data-trans/tmp_file/power_derating.py

import multiprocessing
import os

import matplotlib
matplotlib.use('Agg')
matplotlib.rcParams['font.family'] = 'SimHei'
matplotlib.rcParams['font.sans-serif'] = ['SimHei']

import numpy as np
from matplotlib import pyplot as plt

from utils.file.trans_methods import read_file_to_df
from utils.file.trans_methods import read_excel_files
import pandas as pd


def select_data(file, curve_wv, curve_ap, save_path):
    name = os.path.basename(file).split("@")[0]
    try:
        df = read_file_to_df(file)
        df.dropna(subset=['有功功率 kW均值', '风速 m/s均值', '有功功率设定 kW均值'], inplace=True)
        ap_gt_0_df = df[df['有功功率 kW均值'] > 0]
        ap_le_0_df = df[df['有功功率 kW均值'] <= 0]
        ap_le_0_df["marker"] = -1

        ap = ap_gt_0_df['有功功率 kW均值'].values
        wv = ap_gt_0_df['风速 m/s均值'].values
        ap_set = ap_gt_0_df['有功功率设定 kW均值'].values

        ap_gt_0_in = [0] * ap_gt_0_df.shape[0]

        for i in range(len(ap_set)):
            wind_speed = wv[i]
            active_power = ap[i]
            active_power_set = ap_set[i]

            if active_power >= 2200 - 200:
                ap_gt_0_in[i] = 1
            else:
                diffs = np.abs(curve_wv - wind_speed)
                # 找到差值最小的索引和对应的差值
                minDiff, idx = np.min(diffs), np.argmin(diffs)

                # 使用找到的索引获取对应的值
                closestValue = curve_ap[idx]
                if active_power - closestValue >= -100:
                    ap_gt_0_in[i] = 1

        ap_gt_0_df['marker'] = ap_gt_0_in
        df = pd.concat([ap_gt_0_df, ap_le_0_df])

        df.to_csv(os.path.join(save_path, name + '.csv'), index=False, encoding='utf-8')

        df = df[['时间', '风速 m/s均值', '有功功率 kW均值', '有功功率设定 kW均值', 'marker']]

        df = df[df['marker'] == 1]

        x = df['风速 m/s均值'].values
        y = df['有功功率 kW均值'].values
        # 使用scatter函数绘制散点图
        if not df.empty:
            plt.scatter(x, y, s=10, c='blue')

            # 添加标题和坐标轴标签
            plt.title(name)
            plt.xlabel('风速均值')
            plt.ylabel('有功功率均值')

            # 保存
            plt.savefig(os.path.join(save_path, name + '均值.png'))

    except Exception as e:
        print(os.path.basename(file), "出错", str(e))
        raise e


if __name__ == '__main__':
    wind_power_df = read_file_to_df(r"D:\中能智能\matlib计算相关\标记derating\PV_Curve.csv")
    curve_wv = wind_power_df["风速"].values
    curve_ap = wind_power_df["功率"].values

    all_files = read_excel_files(r"Z:\collection_data\1进行中\诺木洪风电场-甘肃-华电\清理数据\min-666")
    save_path = r"D:\trans_data\诺木洪\清理数据\min-666-derating"

    # save_path = r"Z:\collection_data\1进行中\诺木洪风电场-甘肃-华电\清理数据\min-666-marker"

    # for file in all_files:

    with multiprocessing.Pool(10) as pool:
        pool.starmap(select_data, [(i, curve_wv, curve_ap, save_path) for i in all_files])