WTOAAM/wtoaamapi/apps/business/algorithm/tsrWindSpeedAnalyst.py

import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from .analyst import Analyst
from .utils.directoryUtil import DirectoryUtil as dir
from confBusiness import ConfBusiness


class TSRWindSpeedAnalyst(Analyst):
    """
    风电机组叶尖速比分析
    """

    def typeAnalyst(self):
        return "tsr_windspeed"

    def turbineAnalysis(self,
                        dataFrame,
                        outputAnalysisDir,
                        outputFilePath,
                        confData: ConfBusiness,
                        turbineName):

        self.tsr(dataFrame, outputFilePath,
                 confData.field_wind_speed, confData.field_rotor_speed, confData.field_power, confData.field_pitch_angle1, confData.rotor_diameter)

    def tsr(self, dataFrame, output_path,  field_wind_speed, field_rotort_speed, field_power_active, field_angle_pitch, rotor_diameter):
        dataFrame['power'] = dataFrame[field_power_active]  # Alias the power column
        # Calculate 'wind_speed_floor'
        dataFrame['wind_speed_floor'] = (dataFrame[field_wind_speed] / 1).astype(int) + 0.5

        # Ensure the necessary columns are of float type
        dataFrame['wind_speed'] = dataFrame[field_wind_speed].astype(float)
        dataFrame['rotor_speed'] = dataFrame[field_rotort_speed].astype(float)
        rotor_diameter = pd.to_numeric(rotor_diameter, errors='coerce')
        # Calculate TSR
        dataFrame['tsr'] = (dataFrame['rotor_speed'] * 0.104667 *
                            (rotor_diameter / 2)) / dataFrame['wind_speed']

        # Group by 'wind_speed_floor' and calculate mean, max, and min of TSR
        grouped = dataFrame.groupby('wind_speed_floor').agg({
            'power': 'mean',
            'rotor_speed': 'mean',
            'tsr': ['mean', 'max', 'min']
        }).reset_index()

        # Rename columns for clarity post aggregation
        grouped.columns = ['wind_speed_floor', 'power',
                           'rotor_speed', 'tsr', 'tsr_max', 'tsr_min']

        # Sort by 'wind_speed_floor'
        grouped = grouped.sort_values('wind_speed_floor')

        # Write the aggregated dataFrame to a new CSV file
        grouped.to_csv(output_path, index=False)

    def turbinesAnalysis(self, dataFrameMerge, outputAnalysisDir, confData: ConfBusiness):
        self.plot_tsr_distribution(outputAnalysisDir, confData.farm_name)

    def plot_tsr_distribution(self, csvFileDirOfCp, farm_name, encoding='utf-8'):
        """
        Generates Cp distribution plots for turbines in a wind farm.

        Parameters:
        - csvFileDirOfCp: str, path to the directory containing input CSV files.
        - farm_name: str, name of the wind farm.
        - encoding: str, encoding of the input CSV files. Defaults to 'utf-8'.
        """
        field_Name_Turbine = "turbine_name"
        x_name = 'wind_speed_floor'
        y_name = 'tsr'
        split_way = '_tsr_windspeed.csv'

        # 设置绘图样式
        sns.set_palette('deep')

        # 初始化结果DataFrame
        res = pd.DataFrame()

        # 遍历输入目录中的所有文件
        for root, dir_names, file_names in dir.list_directory(csvFileDirOfCp):
            for file_name in file_names:

                if not file_name.endswith(".csv"):
                    continue

                file_path = os.path.join(root, file_name)

                # 读取CSV文件
                frame = pd.read_csv(file_path, encoding=encoding)

                # 提取设备名
                turbine_name = file_name.split(split_way)[0]

                # 添加设备名作为新列
                frame[field_Name_Turbine] = turbine_name

                # 选择需要的列并合并到结果DataFrame中
                res = pd.concat(
                    [res, frame.loc[:, [field_Name_Turbine, x_name, y_name]]], axis=0)

        # 重置索引
        ress = res.reset_index()

        # 绘制全场TSR分布图
        fig, ax = plt.subplots(figsize=(16, 8))
        ax = sns.lineplot(x=x_name, y=y_name, data=ress,
                          hue=field_Name_Turbine)
        ax.set_title('TSR-Distibute')
        plt.legend(ncol=4)
        plt.savefig(csvFileDirOfCp + r"/{}-TSR-Distibute.png".format(farm_name),
                    bbox_inches='tight', dpi=300)
        plt.close(fig)

        # 绘制每个设备的TSR分布图
        grouped = ress.groupby(field_Name_Turbine)
        for name, group in grouped:
            color = ["lightgrey"] * len(ress[field_Name_Turbine].unique())
            fig, ax = plt.subplots(figsize=(8, 8))
            ax = sns.lineplot(x=x_name, y=y_name, data=ress, hue=field_Name_Turbine,
                              palette=sns.set_palette(color), legend=False)
            ax = sns.lineplot(x=x_name, y=y_name, data=group,
                              color='darkblue', legend=False)
            ax.set_title('turbine name={}'.format(name))
            plt.savefig(csvFileDirOfCp + r"/{}.png".format(name),
                        bbox_inches='tight', dpi=120)
            plt.close(fig)