WTOAAM/dataAnalysisBusiness/algorithm/productionIndicatorAnalyst.py

import os
import numpy as np
import pandas as pd
from scipy.interpolate import interp1d
import plotly.graph_objects as go
from algorithmContract.confBusiness import *
from algorithmContract.contract import Contract
from behavior.analystNotFilter import AnalystNotFilter
from utils.jsonUtil import JsonUtil


class ProductionIndicatorAnalyst(AnalystNotFilter):
    """
    风电机组功率曲线散点分析。
    10分钟数据进行分析
    """

    def typeAnalyst(self):
        return "production_indicator"

    def turbinesAnalysis(self,  outputAnalysisDir, conf: Contract, turbineCodes):
        dictionary = self.processTurbineData(turbineCodes, conf, [
                                             Field_DeviceCode, Field_Time, Field_WindSpeed, Field_ActiverPower,Field_LableFlag])
        dataFrameOfTurbines = self.userDataFrame(
            dictionary, conf.dataContract.configAnalysis, self)
        # 检查所需列是否存在
        required_columns = {Field_WindSpeed, Field_ActiverPower,Field_LableFlag}
        if not required_columns.issubset(dataFrameOfTurbines.columns):
            raise ValueError(f"DataFrame缺少必要的列。需要的列有: {required_columns}")

        turbrineInfos = self.common.getTurbineInfos(
            conf.dataContract.dataFilter.powerFarmID, turbineCodes, self.turbineInfo)
        
        groupedOfTurbineModel = turbrineInfos.groupby(Field_MillTypeCode)
        resultsoftotal=[]
        returnDatas = []
        for turbineModelCode, group in groupedOfTurbineModel:
            currTurbineCodes = group[Field_CodeOfTurbine].unique().tolist()
            currTurbineModeInfo = self.common.getTurbineModelByCode(
                turbineModelCode, self.turbineModelInfo)
            # 获取合同功率曲线数据
            dataFrameOfContractPowerCurve = self.dataFrameContractOfTurbine[
                self.dataFrameContractOfTurbine[Field_MillTypeCode] == turbineModelCode]
            currDataFrameOfTurbines = dataFrameOfTurbines[dataFrameOfTurbines[Field_CodeOfTurbine].isin(
                currTurbineCodes)]

            currDataFrameOfTurbines[Field_RatedPowerSUM] = self.currPowerFarmInfo[Field_RatedPowerSUM]
            
            results,result_df=self.get_result(currDataFrameOfTurbines,outputAnalysisDir,conf,currTurbineModeInfo,dataFrameOfContractPowerCurve)
            resultsoftotal.append(results)
            returnDatas.append(result_df)


        returnResult = pd.concat(returnDatas, ignore_index=True)
        # 计算总场站指标
        Result_df = pd.concat(resultsoftotal, ignore_index=True)
        results_total=self.get_total_result(Result_df, outputAnalysisDir, conf)
        # 连接机组生产指标以及总场站生产指标
        combined_df = pd.concat([results_total, returnResult], axis=0, ignore_index=True)
        return combined_df


    def calculate_metrics(self, df, dataFrameGuaranteePowerCurve):
        '''
        计算机组生产效能指标
        '''
        # 实发电量
        EPActualTotal = (df.loc[df[Field_ActiverPower] >= 0, Field_ActiverPower] / 6).sum(skipna=True)

        # 场站总额定功率
        RatedPowerSUM=df[Field_RatedPowerSUM].iloc[0]

        # 风电机组利用小时
        Thi=EPActualTotal/self.turbineInfo[Field_RatedPower].iloc[0]
        # 弃风电量所需变量（Qdl）
        if self.turbineInfo[Field_Sightcing].iloc[0]==1:#是否为样板机
            Qyb=EPActualTotal
            # 限电时段理论发电量
            Qdl=(Qyb/self.turbineInfo[Field_RatedPower].iloc[0])*RatedPowerSUM 
        else:
            Qdl=0
            
        # 平均风速
        WindSpeedAvr = df[Field_WindSpeed].mean()
        # 切入风速
        CutInWS = self.turbineModelInfo[Field_CutInWS]
        cut_in_ws=CutInWS.iloc[0]
        # 风机可利用率
        nShouldGP = (df[Field_WindSpeed] >= cut_in_ws).sum()
        nRealGP = ((df[Field_WindSpeed] >= cut_in_ws) & (df[Field_ActiverPower] > 0)).sum()
        TurbineRunRate = (nRealGP / nShouldGP) * 100 if nShouldGP > 0 else 0

        df.dropna(subset=[Field_WindSpeed], inplace=True)
        df.reset_index(drop=True, inplace=True)
        # 计算应发电量
        EPIdealTotalAAA = 0
        for i in range(df.shape[0]):
            # if df.loc[i, Field_ActiverPower] >= 0:
            nWhichBin = np.searchsorted(dataFrameGuaranteePowerCurve[Field_WindSpeed], df.loc[i,Field_WindSpeed], side='right') - 1
            if 0 <= nWhichBin< dataFrameGuaranteePowerCurve.shape[0]-1:
                IdealPower = np.interp(df.loc[i,Field_WindSpeed], dataFrameGuaranteePowerCurve[Field_WindSpeed], dataFrameGuaranteePowerCurve.loc[:,Field_ActiverPower])
                EPIdealTotalAAA += IdealPower / 6

        # 风机能量利用率
        TurbinePowerRate=EPActualTotal/EPIdealTotalAAA*100

        
        # 计算停机损失
        EPLostStopTotal = 0
        for i in range(df.shape[0]):
            if df.loc[i, Field_LableFlag] == -1:
                nWhichBin = np.searchsorted(dataFrameGuaranteePowerCurve[Field_WindSpeed], df.loc[i,Field_WindSpeed], side='right') - 1
                if 0 <= nWhichBin < dataFrameGuaranteePowerCurve.shape[0] - 1:
                    IdealPower = np.interp(df.loc[i,Field_WindSpeed], dataFrameGuaranteePowerCurve[Field_WindSpeed], dataFrameGuaranteePowerCurve[Field_ActiverPower])
                    EPLostStopTotal += IdealPower / 6

        # 计算欠发损失
        EPLostBadTotal = 0
        for i in range(df.shape[0]):
            if df.loc[i, Field_LableFlag] == 1:
                nWhichBin = np.searchsorted(dataFrameGuaranteePowerCurve[Field_WindSpeed], df.loc[i, Field_WindSpeed], side='right') - 1
                if 0 <= nWhichBin < dataFrameGuaranteePowerCurve.shape[0] - 1:
                    IdealPower = np.interp(df.loc[i, Field_WindSpeed], dataFrameGuaranteePowerCurve[Field_WindSpeed], dataFrameGuaranteePowerCurve[Field_ActiverPower])
                    EPLostBadTotal += abs(IdealPower - df.loc[i, Field_ActiverPower]) / 6

        # 额定风速以上超发功率点
        EPOverTotal = 0
        for i in range(df.shape[0]):
            if df.loc[i, Field_LableFlag] == 3:
                EPOverTotal += (df.loc[i, Field_ActiverPower] -  self.turbineInfo[Field_RatedPower].iloc[0]) / 6
        # 构造好点数据集
        dfGoodPoint = df[df[Field_LableFlag] == 0].reset_index(drop=True)

        # 计算功率曲线未达标损失电量
        EPLostPerformTotal = 0
        for i in range(dfGoodPoint.shape[0]):
            nWhichBinI = np.searchsorted(dataFrameGuaranteePowerCurve[Field_WindSpeed], dfGoodPoint.loc[i, Field_WindSpeed], side='right') - 1
            if 0 <= nWhichBinI < dataFrameGuaranteePowerCurve.shape[0] - 1:
                IdealPower = np.interp(dfGoodPoint.loc[i, Field_WindSpeed], dataFrameGuaranteePowerCurve[Field_WindSpeed], dataFrameGuaranteePowerCurve[Field_ActiverPower])
                EPLostPerformTotal += (IdealPower - dfGoodPoint.loc[i, Field_ActiverPower]) / 6

        # 计算限电损失电量
        EPLostLimitTotal = 0
        for i in range(df.shape[0]):
            if df.loc[i, Field_LableFlag] == 4:
                nWhichBin = np.searchsorted(dataFrameGuaranteePowerCurve[Field_WindSpeed], df.loc[i, Field_WindSpeed], side='right') - 1
                if 0 <= nWhichBin < dataFrameGuaranteePowerCurve.shape[0] - 1:
                    IdealPower = np.interp(df.loc[i, Field_WindSpeed], dataFrameGuaranteePowerCurve[Field_WindSpeed], dataFrameGuaranteePowerCurve[Field_ActiverPower])
                    EPLostLimitTotal += abs(IdealPower - df.loc[i, Field_ActiverPower]) / 6

        # 计算功率曲线一致性系数
        Ws=self.get_Ws(dfGoodPoint,dataFrameGuaranteePowerCurve)


        if EPLostPerformTotal >= 0:
            EPIdealTotal = EPActualTotal + EPLostStopTotal + EPLostLimitTotal + EPLostBadTotal + EPLostPerformTotal
        else:
            EPIdealTotal = EPActualTotal + EPLostStopTotal + EPLostLimitTotal + EPLostBadTotal
        
        EPLostStopPercent = EPLostStopTotal / EPIdealTotal*100 if EPIdealTotal != 0 else 0
        EPLostBadPercent = EPLostBadTotal / EPIdealTotal*100 if EPIdealTotal != 0 else 0
        EPLostPerformPercent = EPLostPerformTotal / EPIdealTotal*100 if EPIdealTotal != 0 else 0
        EPLostLimitPercent = EPLostLimitTotal / EPIdealTotal*100 if EPIdealTotal != 0 else 0
        wind_speed_ranges, mean_width, variance_width=self.power_bin_width(df)
        # print(wind_speed_ranges)

        result= {
            'EPActualTotal':round(EPActualTotal, 2),#实发电量
            'TurbinePowerRate':round(TurbinePowerRate, 2),#风机能量利用率
            'EPLostStopPercent': round(EPLostStopPercent, 2),#停机损失百分比
            'EPLostBadPercent': round(EPLostBadPercent, 2),#欠发损失百分比
            'EPLostPerformPercent': round(EPLostPerformPercent, 2),#功率曲线未达标损失百分比
            'EPLostLimitPercent': round(EPLostLimitPercent, 2),#限电损失百分比
            'TurbineRunRate':round(TurbineRunRate, 2),#风机可利用率
            'mean_width':round(mean_width, 2),#功率水平平均宽度
            'variance_width':round(variance_width, 2),#功率水平方差
            'WindSpeedAvr':round(WindSpeedAvr, 2),#平均风速
            'Thi':round(Thi, 2),#利用小时
            'Ws':round(Ws, 2),#功率曲线一致性系数
            'Qdl':Qdl,#限电时段理论发电量
            'RatedPowerSUM':RatedPowerSUM#场站总额定容量

        }
        return result
    def power_bin_width(self, df):
        '''
        计算各功率水平的风速区间
        '''
        # 1. 筛选出Field_LabelFlag等于0的行
        filtered_df = df[df[Field_LableFlag] == 0]
        # 2. 确定功率区间的边界
        min_power = filtered_df[Field_ActiverPower].min()
        max_power = filtered_df[Field_ActiverPower].max()
        # 确保最大值所在的仓也被包括在内
        bins = np.arange(min_power - min_power % 25, max_power + 25, 25)
        # 3. 将Field_ActivePower分到不同的仓中
        filtered_df['Power_Bin'] = pd.cut(filtered_df[Field_ActiverPower], bins=bins, right=False)
        # 4. 按仓分组，计算每个组内风速的范围
        grouped = filtered_df.groupby('Power_Bin')
        wind_speed_ranges = grouped[Field_WindSpeed].agg(lambda x: x.max() - x.min())
        # 5. 计算平均宽度和方差
        mean_width = wind_speed_ranges.mean()
        variance_width = wind_speed_ranges.var()
        # 返回结果
        return wind_speed_ranges, mean_width, variance_width
    def Production_indicators(self, result_df):
        '''
        风场生产指标
        '''
        # 风场发电量
        Qp=sum(result_df['EPActualTotal'])

        # 计划发电量完成率(Qj为计划发电量)
        # Rj=Qp/Qj
        # 风场等效利用小时
        Thc=Qp/result_df['RatedPowerSUM'].iloc[0]
        # 风场弃风电量
        Qdr=sum(result_df['Qdl'])-Qp
        # 电网弃风率
        if Qp+Qdr==0:
            Rdr=0
        else:
            Rdr=Qdr/(Qp+Qdr)
        return Qp,Thc,Qdr,Rdr
    def get_Ws(self,df,dataFrameGuaranteePowerCurve):
        '''
        计算功率特性一致性系数
        '''
        # 确定新的风速范围
        # 下限：切入风速减1m/s
        cut_in_ws = self.turbineModelInfo[Field_CutInWS].iloc[0]

        lower_limit = cut_in_ws - 1.0
        # 找到对应85%额定功率的风速
        df_ideal_sorted = dataFrameGuaranteePowerCurve.sort_values(by=Field_WindSpeed)
        power_to_ws_interp = interp1d(df_ideal_sorted[Field_ActiverPower], df_ideal_sorted[Field_WindSpeed], kind='linear', fill_value='extrapolate')
        power_85 = 0.85 *  self.turbineInfo[Field_RatedPower].iloc[0]
        ws_85 = power_to_ws_interp(power_85)
        # 上限：对应85%额定功率风速的1.5倍
        upper_limit = ws_85 * 1.5
        # 生成取样点的中心，以0.5m/s为步长，在[lower_limit,upper_limit]之间
        start_center = np.ceil((lower_limit + 0.25) / 0.5) * 0.5
        sample_centers = np.arange(start_center, upper_limit + 0.25, 0.5)
        # 计算每个区间内的实际功率平均值
        actual_power_means = []
        for center in sample_centers:
            lower = center - 0.25
            upper = center + 0.25
            df_subset = df[(df[Field_WindSpeed] >= lower) & (df[Field_WindSpeed] <= upper)]
            if not df_subset.empty:
                actual_power_mean = df_subset[Field_ActiverPower].mean()
                actual_power_means.append(actual_power_mean)
            else:
                actual_power_means.append(np.nan)
        # 获取对应中心风速的理论功率
        ideal_power_interp = interp1d(df_ideal_sorted[Field_WindSpeed], df_ideal_sorted[Field_ActiverPower], kind='linear', fill_value='extrapolate')
        ideal_powers = ideal_power_interp(sample_centers)
        # 计算百分比差异，并求均值
        actual_power_means = np.array(actual_power_means)
        ideal_powers = ideal_powers[:len(actual_power_means)]
        valid_indices = (ideal_powers != 0) & (~np.isnan(actual_power_means)) & (~np.isnan(ideal_powers))
        percentage_diff = ((actual_power_means[valid_indices] - ideal_powers[valid_indices]) / ideal_powers[valid_indices]) * 100
        Ws = np.mean(percentage_diff)
        return Ws
    def get_result(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir, conf: Contract, turbineModelInfo: pd.Series,dataFrameOfContractPowerCurve:pd.DataFrame):
        # 按设备名分组数据
        grouped = dataFrameMerge.groupby(
            [Field_NameOfTurbine, Field_CodeOfTurbine])
        results = []
        result_rows=[]
        # 计算每个设备的功率曲线
        for name, group in grouped:
            # 创建结果字典，首先添加风机名称wind_turbine_name
            result = {'wind_turbine_name':name[0]}
            # 更新结果字典，添加计算的指标
            result.update(self.calculate_metrics(group, dataFrameOfContractPowerCurve))
            results.append(result)
            # 将results转换成DataFrame
        results = pd.DataFrame(results)
        Ws_mean=results['Ws'].mean()
        results['Wr']=results['Ws']/Ws_mean

        #保存为csv文件
        a=f"{turbineModelInfo[Field_MachineTypeCode]}-production-indicator.csv"
        filepath=self.escape_special_characters(a)
        filePathOfproductionindicator = os.path.join(
            # outputAnalysisDir, f"production_indicator{turbineModelInfo[Field_MachineTypeCode]}{CSVSuffix}")
            outputAnalysisDir, filepath)

        results.to_csv(filePathOfproductionindicator, index=False)

        result_rows.append({
            Field_Return_TypeAnalyst: self.typeAnalyst(),
            Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
            Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
            Field_CodeOfTurbine: "total",
            Field_MillTypeCode:turbineModelInfo[Field_MillTypeCode],
            Field_Return_FilePath: filePathOfproductionindicator,
                    Field_Return_IsSaveDatabase: True
        })

        result_df = pd.DataFrame(result_rows)

        return results,result_df
    def get_total_result(self,dataFrameresults: pd.DataFrame, outputAnalysisDir, conf: Contract):
        # 创建一个空的 DataFrame
        dataFrameResult_total = pd.DataFrame()
        
        # 计算场站总体指标Qp, Thc, Rdr
        Qp, Thc, Qdr,Rdr = self.Production_indicators(dataFrameresults)
        Qp=round(Qp,2)
        Thc=round(Thc,2)
        Rdr = Rdr if Rdr is not None else 0
        print("Rdr:",Rdr)
        Qdr=round(Qdr,2)
        # 将Qp, Thc, Rdr添加到results_df中
        dataFrameResult_total['Qp'] = [Qp]#风场总发电量
        dataFrameResult_total['Thc'] = [Thc]#风场等效利用小时
        dataFrameResult_total['Rdr'] = [Rdr]#风场弃风率
        dataFrameResult_total['Qdr'] = [Qdr]#风场弃风电量
                #保存为csv文件

        # print("dataFrameResult_total:",dataFrameResult_total)
        filePathOfproductionindicator_total = os.path.join(
            outputAnalysisDir, f"production_indicator_total.csv")
        dataFrameResult_total.to_csv(filePathOfproductionindicator_total, index=False)

        result_rows=[]
        result_rows.append({
            Field_Return_TypeAnalyst: self.typeAnalyst(),
            Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
            Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
            Field_CodeOfTurbine: "total",
            Field_MillTypeCode:"total_production_indicator",
            Field_Return_FilePath: filePathOfproductionindicator_total,
                    Field_Return_IsSaveDatabase: True
        })

        result_df = pd.DataFrame(result_rows)
        return result_df