energy-data-trans/tmp_file/power_derating_biaozhun.py

import os

import matplotlib
import numpy as np
from matplotlib import pyplot as plt

from utils.draw.draw_file import scatter

matplotlib.use('Agg')
matplotlib.rcParams['font.family'] = 'SimHei'  # 或者 'Microsoft YaHei'
matplotlib.rcParams['font.sans-serif'] = ['SimHei']  # 或者 ['Microsoft YaHei']

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


class ContractPowerCurve(object):

    def __init__(self, df: pd.DataFrame, wind_velocity='风速', active_power='功率'):
        self.df = df
        self.wind_velocity = wind_velocity
        self.active_power = active_power


def marker_active_power(contract_power_curve_class: ContractPowerCurve, df: pd.DataFrame, active_power='有功功率 kW均值',
                        wind_velocity='风速 m/s均值'):
    """
    标记有功功率为正的记录
    :param contract_power_curve_class: 合同功率曲线
    :param df: 原始数据
    :return: 标记有功功率为正的原始数据
    """
    contract_power_curve_df = contract_power_curve_class.df
    curve_wv = contract_power_curve_df[contract_power_curve_class.wind_velocity].values
    curve_ap = contract_power_curve_df[contract_power_curve_class.active_power].values

    df.dropna(subset=[active_power, wind_velocity], inplace=True)
    ap_gt_0_df = df[df[active_power] > 0]
    ap_le_0_df = df[df[active_power] <= 0]
    ap_le_0_df["marker"] = -1

    active_power_values = ap_gt_0_df[active_power].values
    wind_speed_values = ap_gt_0_df[wind_velocity].values
    ap_gt_0_in = [0] * ap_gt_0_df.shape[0]

    for i in range(len(ap_gt_0_in)):
        wind_speed = wind_speed_values[i]
        active_power = active_power_values[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
    return pd.concat([ap_gt_0_df, ap_le_0_df])


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")

    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"

    wind_power_df_class = ContractPowerCurve(wind_power_df)

    for file in all_files:
        name = os.path.basename(file).split("@")[0]
        try:
            df = read_file_to_df(file)
            df = marker_active_power(wind_power_df_class, df)
            df = df[df['marker'] == 1]
            df.to_csv(os.path.join(save_path, name + '.csv'), index=False, encoding='utf-8')

            # 使用scatter函数绘制散点图
            if not df.empty:
                scatter(name, x_label='风速均值', y_label='有功功率均值', x_values=df['风速 m/s均值'].values,
                        y_values=df['有功功率 kW均值'].values, color='green',
                        save_file_path=os.path.join(save_path, name + '均值.png'))

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