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@@ -1,597 +1,597 @@
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-import os
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-
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-import pandas as pd
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-import plotly.express as px
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-import plotly.graph_objects as go
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-from algorithmContract.confBusiness import *
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-from algorithmContract.contract import Contract
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-from behavior.analystWithGoodBadLimitPoint import AnalystWithGoodBadLimitPoint
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-from plotly.subplots import make_subplots
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-from utils.directoryUtil import DirectoryUtil as dir
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-
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-
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-class Generator:
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- def __init__(self) -> None:
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- self.fieldTemperatorOfDEBearing = None
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- self.fieldTemperatorOfNDEBearing = None
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-
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-
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-TemperatureColumns = {Field_MainBearTemp: "主轴承温度",
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- Field_GbMsBearTemp: "齿轮箱中速轴温度",
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- Field_GbLsBearTemp: "齿轮箱低速轴温度",
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- Field_GbHsBearTemp: "齿轮箱高速轴温度",
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- Field_GeneratorDE: "发电机驱动端轴承温度",
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- Field_GeneratorNDE: "发电机非驱动端轴承温度",
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- Field_GenWiTemp1: "发电机绕组温度"}
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-
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-GeneratorTemperatureAnslysisColumns = [
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- Field_GeneratorDE, Field_GeneratorNDE, Field_NacTemp]
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-
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-
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-class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
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- """
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- 风电机组大部件温升分析
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- """
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-
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- def typeAnalyst(self):
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- return "temperature_large_components"
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-
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- def getUseColumns(self, dataFrame: pd.DataFrame, temperatureColumns: list[dict]):
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- # 获取非全为空的列名
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- non_empty_cols = self.getNoneEmptyFields(dataFrame, temperatureColumns)
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-
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- useCols = []
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- # useCols.append(Field_Time)
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- useCols.append(Field_ActiverPower)
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-
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- if not self.common.isNone(Field_EnvTemp) and Field_EnvTemp in dataFrame.columns:
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- useCols.append(Field_EnvTemp)
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-
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- if not self.common.isNone(Field_NacTemp) and Field_NacTemp in dataFrame.columns:
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- useCols.append(Field_NacTemp)
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-
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- useCols.extend(non_empty_cols)
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-
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- return useCols
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-
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- def getNoneEmptyFields(self, dataFrame: pd.DataFrame, temperatureColumns: dict) -> list:
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- # 使用set和列表推导式来获取在DataFrame中存在的字段
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- existing_fields = [
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- key for key in TemperatureColumns.keys() if key in dataFrame.columns
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- ]
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- # 检查指定列中非全为空的列
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- non_empty_columns = dataFrame[existing_fields].apply(
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- lambda x: x.notnull().any(), axis=0)
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- # 获取非全为空的列名
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- noneEmptyFields = non_empty_columns[non_empty_columns].index.tolist()
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- return noneEmptyFields
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-
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- def dataReprocess(self, dataFrame: pd.DataFrame, non_empty_cols: list):
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- # Initialize an empty df for aggregation
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- agg_dict = {col: 'median' for col in non_empty_cols}
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-
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- # Group by 'power_floor' and aggregate
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- grouped = dataFrame.groupby([Field_PowerFloor, Field_CodeOfTurbine, Field_NameOfTurbine]).agg(agg_dict).reset_index()
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-
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- # Sort by 'power_floor'
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- grouped.sort_values(
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- [Field_PowerFloor, Field_CodeOfTurbine, Field_NameOfTurbine], inplace=True)
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-
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- return grouped
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-
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- def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes):
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- select=[Field_DeviceCode, Field_Time, Field_WindSpeed, Field_ActiverPower]
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- select=select+[Field_EnvTemp,Field_NacTemp]+list(TemperatureColumns.keys())
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-
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- dictionary = self.processTurbineData(turbineCodes, conf,select )
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- dataFrameOfTurbines = self.userDataFrame(
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- dictionary, conf.dataContract.configAnalysis, self)
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-
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- turbrineInfos = self.common.getTurbineInfos(
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- conf.dataContract.dataFilter.powerFarmID, turbineCodes, self.turbineInfo)
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-
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- groupedOfTurbineModel = turbrineInfos.groupby(Field_MillTypeCode)
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-
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- returnDatas = []
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- for turbineModelCode, group in groupedOfTurbineModel:
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- currTurbineCodes = group[Field_CodeOfTurbine].unique().tolist()
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- currTurbineModeInfo = self.common.getTurbineModelByCode(
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- turbineModelCode, self.turbineModelInfo)
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-
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- currDataFrameOfTurbines = dataFrameOfTurbines[dataFrameOfTurbines[Field_CodeOfTurbine].isin(
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- currTurbineCodes)]
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-
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- # 将 currTurbineInfos 转换为字典
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- currTurbineInfos_dict = turbrineInfos.set_index(Field_CodeOfTurbine)[Field_NameOfTurbine].to_dict()
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- # 使用 map 函数来填充 Field_NameOfTurbine 列
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- currDataFrameOfTurbines[Field_NameOfTurbine] = currDataFrameOfTurbines[Field_CodeOfTurbine].map(currTurbineInfos_dict).fillna("")
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-
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- # 获取非全为空的列名
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- non_empty_cols = self.getUseColumns(currDataFrameOfTurbines, TemperatureColumns)
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- dataFrame = self.dataReprocess(currDataFrameOfTurbines, non_empty_cols)
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- # non_empty_cols.remove(Field_Time)
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- non_empty_cols.remove(Field_ActiverPower)
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- non_empty_cols.remove(Field_EnvTemp)
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- non_empty_cols.remove(Field_NacTemp)
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-
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- returnData= self.drawTemperatureGraph(dataFrame, outputAnalysisDir, conf, non_empty_cols,currTurbineModeInfo)
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-
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- returnDatas.append(returnData)
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-
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- returnResult = pd.concat(returnDatas, ignore_index=True)
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-
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- return returnResult
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-
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-
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- def drawTemperatureGraph(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir, conf: Contract, temperatureCols: list, turbineModelInfo: pd.Series):
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- """
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- 大部件温度传感器分析
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- """
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- y_name = '温差(℃)'
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-
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- outputDir = os.path.join(outputAnalysisDir, "GeneratorTemperature")
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- dir.create_directory(outputDir)
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- # 按设备名分组数据
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- grouped = dataFrameMerge.groupby(Field_CodeOfTurbine)
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-
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- result_rows = []
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- # Create output directories if they don't exist
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- for column in temperatureCols:
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- if not column in dataFrameMerge.columns:
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- continue
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-
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- # 判断是否是机舱温度本身
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- is_nac_temp = (column == Field_NacTemp)
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-
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- columnZH = TemperatureColumns.get(column)
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-
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- if not is_nac_temp:
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- columnZH = f"{columnZH}温差"
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-
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- outputPath = os.path.join(outputAnalysisDir, column)
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- dir.create_directory(outputPath)
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-
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- fig = go.Figure()
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-
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- # 获取 Plotly Express 中的颜色序列
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- colors = px.colors.sequential.Turbo
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-
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- # 创建一个列表来存储各个风电机组的数据
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- turbine_data_list = []
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-
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- # Add traces for each turbine
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- for idx, (name, group) in enumerate(grouped):
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- currTurbineInfo_group = self.common.getTurbineInfo(
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- conf.dataContract.dataFilter.powerFarmID, name, self.turbineInfo)
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-
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- # --- 数据计算逻辑 ---
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- x_data = group[Field_PowerFloor]
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- y_data = group[column]
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-
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- # 如果不是机舱温度本身，且存在机舱温度数据，则计算温差（部件温度 - 机舱温度）
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- if not is_nac_temp and Field_NacTemp in group.columns:
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- y_data = y_data - group[Field_NacTemp]
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- # ------------------
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-
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- fig.add_trace(go.Scatter(
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- x=group[Field_PowerFloor],
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- y=group[column],
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- mode='lines',
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- name=currTurbineInfo_group[Field_NameOfTurbine],
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- # 从 'Rainbow' 色组中循环选择颜色
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- line=dict(color=colors[idx % len(colors)])
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- ))
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- # 提取数据
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- turbine_data_total = {
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- "engineName": currTurbineInfo_group[Field_NameOfTurbine],
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- "engineCode": name,
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- "xData": group[Field_PowerFloor].tolist(),
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- "yData": group[column].tolist(),
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- "color": colors[idx % len(colors)]
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- }
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- turbine_data_list.append(turbine_data_total)
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-
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- # Update layout and axes
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- fig.update_layout(
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- title={'text': f'{columnZH}分布-{turbineModelInfo[Field_MachineTypeCode]}', 'x': 0.5},
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- xaxis_title='功率',
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- yaxis_title=y_name,
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- xaxis=dict(
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- range=[
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- self.axisLowerLimitActivePower,
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- self.axisUpperLimitActivePower
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- ],
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- dtick=self.axisStepActivePower
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- ),
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- yaxis=dict(
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- range=[0, 100],
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- dtick=20
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- ),
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- # legend_title_text='Turbine'，
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- legend=dict(
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- orientation="h", # Horizontal orientation
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- xanchor="center", # Anchor the legend to the center
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- x=0.5, # Position legend at the center of the x-axis
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- y=-0.2, # Position legend below the x-axis
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- # itemsizing='constant', # Keep the size of the legend entries constant
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- # # Set the width of the legend items (in pixels)
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- # itemwidth=50
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- )
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- )
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-
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- # Save the plot as a PNG/HTML file
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- # filePathOfImage = os.path.join(outputPath, f"{columnZH}-{turbineModelInfo[Field_MillTypeCode]}.png")
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- # fig.write_image(filePathOfImage, scale=3)
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- # filePathOfHtml = os.path.join(outputPath, f"{columnZH}-{turbineModelInfo[Field_MillTypeCode]}.html")
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- #fig.write_html(filePathOfHtml)
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-
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- engineTypeCode = turbineModelInfo.get(Field_MillTypeCode, "")
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- if isinstance(engineTypeCode, pd.Series):
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- engineTypeCode = engineTypeCode.iloc[0]
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-
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- engineTypeName = turbineModelInfo.get(Field_MachineTypeCode, "")
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- if isinstance(engineTypeName, pd.Series):
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- engineTypeName = engineTypeName.iloc[0]
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- # 构建最终的JSON对象
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- json_output = {
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- "analysisTypeCode": "大部件温度传感器分析",
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- "typecode": turbineModelInfo[Field_MillTypeCode],
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- "engineCode": engineTypeCode,
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- "engineTypeName": engineTypeName,
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- "title": f'{columnZH}分布-{turbineModelInfo[Field_MachineTypeCode]}',
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- "xaixs": "功率(kW)",
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- "yaixs": y_name,
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- "data": turbine_data_list
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- }
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-
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- # 将JSON对象保存到文件
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- output_json_path = os.path.join(outputPath,f"{column}_{turbineModelInfo[Field_MillTypeCode]}.json")
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- with open(output_json_path, 'w', encoding='utf-8') as f:
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- import json
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- json.dump(json_output, f, ensure_ascii=False, indent=4)
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-
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- # result_rows.append({
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- # Field_Return_TypeAnalyst: self.typeAnalyst(),
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- # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
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- # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
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- # Field_CodeOfTurbine: Const_Output_Total,
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- # Field_Return_FilePath: filePathOfImage,
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- # Field_Return_IsSaveDatabase: False
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- # })
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-
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- # result_rows.append({
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- # Field_Return_TypeAnalyst: self.typeAnalyst(),
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- # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
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- # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
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- # Field_CodeOfTurbine: Const_Output_Total,
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- # Field_Return_FilePath: filePathOfHtml,
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- # Field_Return_IsSaveDatabase: True
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- # })
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-
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- # 如果需要返回DataFrame，可以包含文件路径
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- result_rows.append({
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- Field_Return_TypeAnalyst: self.typeAnalyst(),
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- Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
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- Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
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- Field_CodeOfTurbine: 'total',
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- Field_MillTypeCode: f'{column}_{turbineModelInfo[Field_MillTypeCode]}',
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- Field_Return_FilePath: output_json_path,
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- Field_Return_IsSaveDatabase: True
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- })
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-
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- # Create individual plots with specific turbine highlighted
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- # for name, group in grouped:
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- for idx, (name, group) in enumerate(grouped):
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- single_fig = go.Figure()
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- currTurbineInfo_each = self.common.getTurbineInfo(
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- conf.dataContract.dataFilter.powerFarmID, name, self.turbineInfo)
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-
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- # 创建一个列表来存储各个风电机组的数据
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- turbine_data_list_each = []
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-
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- # Add all other turbines in grey first
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- # for other_name, other_group in grouped:
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- for idx, (other_name, other_group) in enumerate(grouped):
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- if other_name != name:
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- tempTurbineInfo = self.common.getTurbineInfo(
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- conf.dataContract.dataFilter.powerFarmID, other_name, self.turbineInfo)
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-
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- # --- 数据计算 ---
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- ox_data = other_group[Field_PowerFloor]
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- oy_data = other_group[column]
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- if not is_nac_temp and Field_NacTemp in other_group.columns:
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- oy_data = oy_data - other_group[Field_NacTemp]
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- # ---------------
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-
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- single_fig.add_trace(go.Scatter(
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- x=ox_data,
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- y=oy_data,
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- mode='lines',
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- name=tempTurbineInfo[Field_NameOfTurbine],
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- line=dict(color='lightgrey', width=1),
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- showlegend=False
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- ))
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- # 提取数据
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- turbine_data_other_each = {
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- "engineName": tempTurbineInfo[Field_NameOfTurbine],
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- "engineCode": other_name,
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- "xData": other_group[Field_PowerFloor].tolist(),
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- "yData": other_group[column].tolist(),
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- }
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- turbine_data_list_each.append(turbine_data_other_each)
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-
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- # Add the turbine of interest in dark blue
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- # --- 数据计算 ---
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- gx_data = group[Field_PowerFloor]
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- gy_data = group[column]
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- if not is_nac_temp and Field_NacTemp in group.columns:
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- gy_data = gy_data - group[Field_NacTemp]
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- # ---------------
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-
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- single_fig.add_trace(go.Scatter(
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- x=gx_data,
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- y=gy_data,
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- mode='lines',
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- # Make it slightly thicker for visibility
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- line=dict(color='darkblue', width=2),
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- showlegend=False # Disable legend for cleaner look
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- ))
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-
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- turbine_data_curr = {
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- "engineName": currTurbineInfo_each[Field_NameOfTurbine],
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- "engineCode": currTurbineInfo_each[Field_CodeOfTurbine],
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- "xData": group[Field_PowerFloor].tolist(),
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- "yData": group[column].tolist(),
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- }
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- turbine_data_list_each.append(turbine_data_curr)
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-
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- # Update layout and axes for the individual plot
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- single_fig.update_layout(
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- # title={'text': f'Turbine: {name}', 'x': 0.5},
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- title={'text': f'{columnZH}分布: {currTurbineInfo_each[Field_NameOfTurbine]}', 'x': 0.5},
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- xaxis_title='功率',
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- yaxis_title=y_name,
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- xaxis=dict(
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- range=[0, Field_RatedPower],
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- dtick=200
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- ),
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- yaxis=dict(
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- range=[0, 100],
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- dtick=20
|
|
|
- )
|
|
|
- )
|
|
|
-
|
|
|
- engineTypeCode = turbineModelInfo.get(Field_MillTypeCode, "")
|
|
|
- if isinstance(engineTypeCode, pd.Series):
|
|
|
- engineTypeCode = engineTypeCode.iloc[0]
|
|
|
-
|
|
|
- engineTypeName = turbineModelInfo.get(Field_MachineTypeCode, "")
|
|
|
- if isinstance(engineTypeName, pd.Series):
|
|
|
- engineTypeName = engineTypeName.iloc[0]
|
|
|
- # 构建最终的JSON对象
|
|
|
- json_output = {
|
|
|
- "analysisTypeCode": "大部件温度传感器分析",
|
|
|
- "typecode": turbineModelInfo[Field_MillTypeCode],
|
|
|
- "engineCode": engineTypeCode,
|
|
|
- "engineTypeName": engineTypeName,
|
|
|
- "title": f'{columnZH}分布: {currTurbineInfo_each[Field_NameOfTurbine]}',
|
|
|
- "xaixs": "功率(kW)",
|
|
|
- "yaixs": y_name,
|
|
|
- "data": turbine_data_list_each
|
|
|
- }
|
|
|
-
|
|
|
- # 将JSON对象保存到文件
|
|
|
- output_json_path_each = os.path.join(outputPath, f"{currTurbineInfo_each[Field_NameOfTurbine]}.json")
|
|
|
- with open(output_json_path_each, 'w', encoding='utf-8') as f:
|
|
|
- import json
|
|
|
- json.dump(json_output, f, ensure_ascii=False, indent=4)
|
|
|
-
|
|
|
- # filePathOfImage = os.path.join(
|
|
|
- # outputPath, f"{currTurbineInfo_each[Field_NameOfTurbine]}.png")
|
|
|
- # single_fig.write_image(filePathOfImage, scale=3)
|
|
|
- # filePathOfHtml = os.path.join(
|
|
|
- # outputPath, f"{name}.html")
|
|
|
- # single_fig.write_html(filePathOfHtml)
|
|
|
-
|
|
|
- # result_rows.append({
|
|
|
- # Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
- # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
- # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
- # Field_CodeOfTurbine: group[Field_CodeOfTurbine].iloc[0],
|
|
|
- # Field_Return_FilePath: filePathOfImage,
|
|
|
- # Field_Return_IsSaveDatabase: False
|
|
|
- # })
|
|
|
-
|
|
|
- # 如果需要返回DataFrame，可以包含文件路径
|
|
|
- result_rows.append({
|
|
|
- Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
- Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
- Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
- Field_CodeOfTurbine: group[Field_CodeOfTurbine].iloc[0],
|
|
|
- Field_Return_FilePath: output_json_path_each,
|
|
|
- Field_Return_IsSaveDatabase: True
|
|
|
- })
|
|
|
-
|
|
|
- # result_rows.append({
|
|
|
- # Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
- # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
- # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
- # Field_CodeOfTurbine: group[Field_CodeOfTurbine].iloc[0],
|
|
|
- # Field_Return_FilePath: filePathOfHtml,
|
|
|
- # Field_Return_IsSaveDatabase: True
|
|
|
- # })
|
|
|
-
|
|
|
- for idx, (name, group) in enumerate(grouped):
|
|
|
- # 绘制每台机组发电机的，驱动轴承温度、非驱动轴承温度、发电机轴承温度BIAS、发电机轴承温度和机舱温度BIAS 均与有功功率的折线图
|
|
|
- if not Field_GeneratorDE in group.columns or not Field_GeneratorNDE in group.columns or group[Field_GeneratorDE].isna().all() or group[Field_GeneratorNDE].isna().all():
|
|
|
- self.logger.warning(f"{name} 不具备发电机温度测点，或测点值全为空")
|
|
|
- continue
|
|
|
- refFieldTemperature = Field_EnvTemp if group[Field_NacTemp].isna(
|
|
|
- ).all() else Field_NacTemp
|
|
|
- result_rows1 = self.drawGeneratorTemperature(
|
|
|
- group, conf, Field_GeneratorDE, Field_GeneratorNDE, refFieldTemperature, Field_PowerFloor, name, outputDir)
|
|
|
- result_rows.extend(result_rows1)
|
|
|
-
|
|
|
- result_df = pd.DataFrame(result_rows)
|
|
|
-
|
|
|
- return result_df
|
|
|
-
|
|
|
- def drawGeneratorTemperature(self, dataFrame: pd.DataFrame, conf: Contract, yAxisDE, yAxisNDE, diffTemperature, xAxis, turbineCode, outputDir):
|
|
|
- tempTurbineInfo1 = self.common.getTurbineInfo(
|
|
|
- conf.dataContract.dataFilter.powerFarmID, turbineCode, self.turbineInfo)
|
|
|
- turbineName = tempTurbineInfo1[Field_NameOfTurbine]
|
|
|
- # 发电机驱动轴承温度 和 发电机非驱动轴承 温差
|
|
|
- fieldBIAS_DE_NDE = 'BIAS_DE-NDE'
|
|
|
- fieldBIAS_DE = 'BIAS_DE'
|
|
|
- fieldBIAS_NDE = 'BIAS_NDE'
|
|
|
-
|
|
|
- # Prepare Data
|
|
|
- dataFrame[fieldBIAS_DE] = dataFrame[yAxisDE] - \
|
|
|
- dataFrame[diffTemperature]
|
|
|
- dataFrame[fieldBIAS_NDE] = dataFrame[yAxisNDE] - \
|
|
|
- dataFrame[diffTemperature]
|
|
|
- dataFrame[fieldBIAS_DE_NDE] = dataFrame[yAxisDE] - dataFrame[yAxisNDE]
|
|
|
-
|
|
|
- # Create a plot with dual y-axes
|
|
|
- fig = make_subplots(specs=[[{"secondary_y": True}]])
|
|
|
-
|
|
|
- plot_data_list_each = []
|
|
|
-
|
|
|
- # Plot DE Bearing Temperature
|
|
|
- fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[yAxisDE], name='驱动端轴承温度', line=dict(
|
|
|
- color='blue')), secondary_y=False)
|
|
|
-
|
|
|
- plot_data_curr = {
|
|
|
- "Name": '驱动端轴承温度',
|
|
|
- "xData": dataFrame[xAxis].tolist(),
|
|
|
- "yData": dataFrame[yAxisDE].tolist(),
|
|
|
- "color": 'blue',
|
|
|
- }
|
|
|
- plot_data_list_each.append(plot_data_curr)
|
|
|
-
|
|
|
- # Plot NDE Bearing Temperature
|
|
|
- fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[yAxisNDE], name='非驱动端轴承温度', line=dict(
|
|
|
- color='green')), secondary_y=False)
|
|
|
-
|
|
|
- plot_data_curr = {
|
|
|
- "Name": '非驱动端轴承温度',
|
|
|
- "xData": dataFrame[xAxis].tolist(),
|
|
|
- "yData": dataFrame[yAxisNDE].tolist(),
|
|
|
- "color": 'green',
|
|
|
- }
|
|
|
- plot_data_list_each.append(plot_data_curr)
|
|
|
-
|
|
|
- # Plot Temperature Differences
|
|
|
- fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[fieldBIAS_DE], name='驱动端轴承温度与机舱温度偏差', line=dict(
|
|
|
- color='blue', dash='dot')), secondary_y=False)
|
|
|
- plot_data_curr = {
|
|
|
- "Name": '驱动端轴承温度与机舱温度偏差',
|
|
|
- "xData": dataFrame[xAxis].tolist(),
|
|
|
- "yData": dataFrame[fieldBIAS_DE].tolist(),
|
|
|
- "color": 'blue',
|
|
|
- }
|
|
|
- plot_data_list_each.append(plot_data_curr)
|
|
|
-
|
|
|
- fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[fieldBIAS_NDE], name='非驱动端轴承温度与机舱温度偏差', line=dict(
|
|
|
- color='green', dash='dot')), secondary_y=False)
|
|
|
- plot_data_curr = {
|
|
|
- "Name": '非驱动端轴承温度与机舱温度偏差',
|
|
|
- "xData": dataFrame[xAxis].tolist(),
|
|
|
- "yData": dataFrame[fieldBIAS_NDE].tolist(),
|
|
|
- "color": 'green',
|
|
|
- }
|
|
|
- plot_data_list_each.append(plot_data_curr)
|
|
|
-
|
|
|
- fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[fieldBIAS_DE_NDE],
|
|
|
- name='驱动端轴承与非驱动端轴承温度偏差', line=dict(color='black', dash='dash')), secondary_y=False)
|
|
|
- plot_data_curr = {
|
|
|
- "Name": '驱动端轴承与非驱动端轴承温度偏差',
|
|
|
- "xData": dataFrame[xAxis].tolist(),
|
|
|
- "yData": dataFrame[fieldBIAS_DE_NDE].tolist(),
|
|
|
- "color": 'black',
|
|
|
- }
|
|
|
- plot_data_list_each.append(plot_data_curr)
|
|
|
-
|
|
|
- # Plot Nacelle Temperature
|
|
|
- fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[diffTemperature],
|
|
|
- name='机舱温度', line=dict(color='orange')), secondary_y=False)
|
|
|
- plot_data_curr = {
|
|
|
- "Name": '机舱温度',
|
|
|
- "xData": dataFrame[xAxis].tolist(),
|
|
|
- "yData": dataFrame[diffTemperature].tolist(),
|
|
|
- "color": 'orange',
|
|
|
- }
|
|
|
- plot_data_list_each.append(plot_data_curr)
|
|
|
-
|
|
|
- # Add horizontal reference lines
|
|
|
- fig.add_hline(y=5, line_dash="dot", line_color="#FFDB58")
|
|
|
- fig.add_hline(y=-5, line_dash="dot", line_color="#FFDB58")
|
|
|
-
|
|
|
- fig.add_hline(y=15, line_dash="dot", line_color="red")
|
|
|
- fig.add_hline(y=-15, line_dash="dot", line_color="red")
|
|
|
-
|
|
|
- # Update layout
|
|
|
- fig.update_layout(
|
|
|
- title={'text': f'发电机温度偏差: {turbineName}'},
|
|
|
- xaxis_title="功率",
|
|
|
- yaxis_title='轴承温度 & 偏差',
|
|
|
- legend_title='温度 & 偏差',
|
|
|
- legend=dict(x=1.1, y=0.5, bgcolor='rgba(255, 255, 255, 0.5)'),
|
|
|
- margin=dict(r=200) # Adjust margin to fit legend
|
|
|
- )
|
|
|
-
|
|
|
- fig.update_yaxes(range=[-20, 100], secondary_y=False)
|
|
|
-
|
|
|
-
|
|
|
- # 构建最终的JSON对象
|
|
|
- json_output = {
|
|
|
- "analysisTypeCode": "发电机温度传感器分析",
|
|
|
- "turbineName": tempTurbineInfo1[Field_NameOfTurbine],
|
|
|
- "turbineCode": tempTurbineInfo1[Field_CodeOfTurbine],
|
|
|
- "title": f'发电机温度偏差: {turbineName}',
|
|
|
- "xaixs": "功率(kW)",
|
|
|
- "yaixs": '轴承温度 & 偏差(℃)',
|
|
|
- "data": plot_data_list_each
|
|
|
- }
|
|
|
-
|
|
|
- # 将JSON对象保存到文件
|
|
|
- output_json_path_each = os.path.join(outputDir, f"{turbineName}.json")
|
|
|
- with open(output_json_path_each, 'w', encoding='utf-8') as f:
|
|
|
- import json
|
|
|
- json.dump(json_output, f, ensure_ascii=False, indent=4)
|
|
|
- # Save the plot as a PNG/HTML file
|
|
|
- # filePathOfImage = os.path.join(outputDir, f"{turbineName}.png")
|
|
|
- # fig.write_image(filePathOfImage, width=800, height=600, scale=3)
|
|
|
- # filePathOfHtml = os.path.join(outputDir, f"{turbineName}.html")
|
|
|
- # fig.write_html(filePathOfHtml)
|
|
|
-
|
|
|
- result_rows1 = []
|
|
|
- # result_rows1.append({
|
|
|
- # Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
- # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
- # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
- # Field_CodeOfTurbine: dataFrame[Field_CodeOfTurbine].iloc[0],
|
|
|
- # Field_Return_FilePath: filePathOfImage,
|
|
|
- # Field_Return_IsSaveDatabase: False
|
|
|
- # })
|
|
|
-
|
|
|
- # 如果需要返回DataFrame，可以包含文件路径
|
|
|
- result_rows1.append({
|
|
|
- Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
- Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
- Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
- Field_CodeOfTurbine: dataFrame[Field_CodeOfTurbine].iloc[0],
|
|
|
- Field_Return_FilePath: output_json_path_each,
|
|
|
- Field_Return_IsSaveDatabase: True
|
|
|
- })
|
|
|
-
|
|
|
- # result_rows1.append({
|
|
|
- # Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
- # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
- # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
- # Field_CodeOfTurbine: dataFrame[Field_CodeOfTurbine].iloc[0],
|
|
|
- # Field_Return_FilePath: filePathOfHtml,
|
|
|
- # Field_Return_IsSaveDatabase: True
|
|
|
- # })
|
|
|
-
|
|
|
- return result_rows1
|
|
|
+import os
|
|
|
+
|
|
|
+import pandas as pd
|
|
|
+import plotly.express as px
|
|
|
+import plotly.graph_objects as go
|
|
|
+from algorithmContract.confBusiness import *
|
|
|
+from algorithmContract.contract import Contract
|
|
|
+from behavior.analystWithGoodBadLimitPoint import AnalystWithGoodBadLimitPoint
|
|
|
+from plotly.subplots import make_subplots
|
|
|
+from utils.directoryUtil import DirectoryUtil as dir
|
|
|
+
|
|
|
+
|
|
|
+class Generator:
|
|
|
+ def __init__(self) -> None:
|
|
|
+ self.fieldTemperatorOfDEBearing = None
|
|
|
+ self.fieldTemperatorOfNDEBearing = None
|
|
|
+
|
|
|
+
|
|
|
+TemperatureColumns = {Field_MainBearTemp: "主轴承温度",
|
|
|
+ Field_GbMsBearTemp: "齿轮箱中速轴温度",
|
|
|
+ Field_GbLsBearTemp: "齿轮箱低速轴温度",
|
|
|
+ Field_GbHsBearTemp: "齿轮箱高速轴温度",
|
|
|
+ Field_GeneratorDE: "发电机驱动端轴承温度",
|
|
|
+ Field_GeneratorNDE: "发电机非驱动端轴承温度",
|
|
|
+ Field_GenWiTemp1: "发电机绕组温度"}
|
|
|
+
|
|
|
+GeneratorTemperatureAnslysisColumns = [
|
|
|
+ Field_GeneratorDE, Field_GeneratorNDE, Field_NacTemp]
|
|
|
+
|
|
|
+
|
|
|
+class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
|
|
|
+ """
|
|
|
+ 风电机组大部件温升分析
|
|
|
+ """
|
|
|
+
|
|
|
+ def typeAnalyst(self):
|
|
|
+ return "temperature_large_components"
|
|
|
+
|
|
|
+ def getUseColumns(self, dataFrame: pd.DataFrame, temperatureColumns: list[dict]):
|
|
|
+ # 获取非全为空的列名
|
|
|
+ non_empty_cols = self.getNoneEmptyFields(dataFrame, temperatureColumns)
|
|
|
+
|
|
|
+ useCols = []
|
|
|
+ # useCols.append(Field_Time)
|
|
|
+ useCols.append(Field_ActiverPower)
|
|
|
+
|
|
|
+ if not self.common.isNone(Field_EnvTemp) and Field_EnvTemp in dataFrame.columns:
|
|
|
+ useCols.append(Field_EnvTemp)
|
|
|
+
|
|
|
+ if not self.common.isNone(Field_NacTemp) and Field_NacTemp in dataFrame.columns:
|
|
|
+ useCols.append(Field_NacTemp)
|
|
|
+
|
|
|
+ useCols.extend(non_empty_cols)
|
|
|
+
|
|
|
+ return useCols
|
|
|
+
|
|
|
+ def getNoneEmptyFields(self, dataFrame: pd.DataFrame, temperatureColumns: dict) -> list:
|
|
|
+ # 使用set和列表推导式来获取在DataFrame中存在的字段
|
|
|
+ existing_fields = [
|
|
|
+ key for key in TemperatureColumns.keys() if key in dataFrame.columns
|
|
|
+ ]
|
|
|
+ # 检查指定列中非全为空的列
|
|
|
+ non_empty_columns = dataFrame[existing_fields].apply(
|
|
|
+ lambda x: x.notnull().any(), axis=0)
|
|
|
+ # 获取非全为空的列名
|
|
|
+ noneEmptyFields = non_empty_columns[non_empty_columns].index.tolist()
|
|
|
+ return noneEmptyFields
|
|
|
+
|
|
|
+ def dataReprocess(self, dataFrame: pd.DataFrame, non_empty_cols: list):
|
|
|
+ # Initialize an empty df for aggregation
|
|
|
+ agg_dict = {col: 'median' for col in non_empty_cols}
|
|
|
+
|
|
|
+ # Group by 'power_floor' and aggregate
|
|
|
+ grouped = dataFrame.groupby([Field_PowerFloor, Field_CodeOfTurbine, Field_NameOfTurbine]).agg(agg_dict).reset_index()
|
|
|
+
|
|
|
+ # Sort by 'power_floor'
|
|
|
+ grouped.sort_values(
|
|
|
+ [Field_PowerFloor, Field_CodeOfTurbine, Field_NameOfTurbine], inplace=True)
|
|
|
+
|
|
|
+ return grouped
|
|
|
+
|
|
|
+ def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes):
|
|
|
+ select=[Field_DeviceCode, Field_Time, Field_WindSpeed, Field_ActiverPower]
|
|
|
+ select=select+[Field_EnvTemp,Field_NacTemp]+list(TemperatureColumns.keys())
|
|
|
+
|
|
|
+ dictionary = self.processTurbineData(turbineCodes, conf,select )
|
|
|
+ dataFrameOfTurbines = self.userDataFrame(
|
|
|
+ dictionary, conf.dataContract.configAnalysis, self)
|
|
|
+
|
|
|
+ turbrineInfos = self.common.getTurbineInfos(
|
|
|
+ conf.dataContract.dataFilter.powerFarmID, turbineCodes, self.turbineInfo)
|
|
|
+
|
|
|
+ groupedOfTurbineModel = turbrineInfos.groupby(Field_MillTypeCode)
|
|
|
+
|
|
|
+ returnDatas = []
|
|
|
+ for turbineModelCode, group in groupedOfTurbineModel:
|
|
|
+ currTurbineCodes = group[Field_CodeOfTurbine].unique().tolist()
|
|
|
+ currTurbineModeInfo = self.common.getTurbineModelByCode(
|
|
|
+ turbineModelCode, self.turbineModelInfo)
|
|
|
+
|
|
|
+ currDataFrameOfTurbines = dataFrameOfTurbines[dataFrameOfTurbines[Field_CodeOfTurbine].isin(
|
|
|
+ currTurbineCodes)]
|
|
|
+
|
|
|
+ # 将 currTurbineInfos 转换为字典
|
|
|
+ currTurbineInfos_dict = turbrineInfos.set_index(Field_CodeOfTurbine)[Field_NameOfTurbine].to_dict()
|
|
|
+ # 使用 map 函数来填充 Field_NameOfTurbine 列
|
|
|
+ currDataFrameOfTurbines[Field_NameOfTurbine] = currDataFrameOfTurbines[Field_CodeOfTurbine].map(currTurbineInfos_dict).fillna("")
|
|
|
+
|
|
|
+ # 获取非全为空的列名
|
|
|
+ non_empty_cols = self.getUseColumns(currDataFrameOfTurbines, TemperatureColumns)
|
|
|
+ dataFrame = self.dataReprocess(currDataFrameOfTurbines, non_empty_cols)
|
|
|
+ # non_empty_cols.remove(Field_Time)
|
|
|
+ non_empty_cols.remove(Field_ActiverPower)
|
|
|
+ non_empty_cols.remove(Field_EnvTemp)
|
|
|
+ non_empty_cols.remove(Field_NacTemp)
|
|
|
+
|
|
|
+ returnData= self.drawTemperatureGraph(dataFrame, outputAnalysisDir, conf, non_empty_cols,currTurbineModeInfo)
|
|
|
+
|
|
|
+ returnDatas.append(returnData)
|
|
|
+
|
|
|
+ returnResult = pd.concat(returnDatas, ignore_index=True)
|
|
|
+
|
|
|
+ return returnResult
|
|
|
+
|
|
|
+
|
|
|
+ def drawTemperatureGraph(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir, conf: Contract, temperatureCols: list, turbineModelInfo: pd.Series):
|
|
|
+ """
|
|
|
+ 大部件温度传感器分析
|
|
|
+ """
|
|
|
+ y_name = '温差(℃)'
|
|
|
+
|
|
|
+ outputDir = os.path.join(outputAnalysisDir, "GeneratorTemperature")
|
|
|
+ dir.create_directory(outputDir)
|
|
|
+ # 按设备名分组数据
|
|
|
+ grouped = dataFrameMerge.groupby(Field_CodeOfTurbine)
|
|
|
+
|
|
|
+ result_rows = []
|
|
|
+ # Create output directories if they don't exist
|
|
|
+ for column in temperatureCols:
|
|
|
+ if not column in dataFrameMerge.columns:
|
|
|
+ continue
|
|
|
+
|
|
|
+ # 判断是否是机舱温度本身
|
|
|
+ is_nac_temp = (column == Field_EnvTemp)
|
|
|
+
|
|
|
+ columnZH = TemperatureColumns.get(column)
|
|
|
+
|
|
|
+ if not is_nac_temp:
|
|
|
+ columnZH = f"{columnZH}温差"
|
|
|
+
|
|
|
+ outputPath = os.path.join(outputAnalysisDir, column)
|
|
|
+ dir.create_directory(outputPath)
|
|
|
+
|
|
|
+ fig = go.Figure()
|
|
|
+
|
|
|
+ # 获取 Plotly Express 中的颜色序列
|
|
|
+ colors = px.colors.sequential.Turbo
|
|
|
+
|
|
|
+ # 创建一个列表来存储各个风电机组的数据
|
|
|
+ turbine_data_list = []
|
|
|
+
|
|
|
+ # Add traces for each turbine
|
|
|
+ for idx, (name, group) in enumerate(grouped):
|
|
|
+ currTurbineInfo_group = self.common.getTurbineInfo(
|
|
|
+ conf.dataContract.dataFilter.powerFarmID, name, self.turbineInfo)
|
|
|
+
|
|
|
+ # --- 数据计算逻辑 ---
|
|
|
+ x_data = group[Field_PowerFloor]
|
|
|
+ y_data = group[column]
|
|
|
+
|
|
|
+ # 如果不是机舱温度本身，且存在机舱温度数据，则计算温差（部件温度 - 机舱温度）
|
|
|
+ if not is_nac_temp and Field_EnvTemp in group.columns:
|
|
|
+ y_data = y_data - group[Field_EnvTemp]
|
|
|
+ # ------------------
|
|
|
+
|
|
|
+ fig.add_trace(go.Scatter(
|
|
|
+ x=group[Field_PowerFloor],
|
|
|
+ y=group[column],
|
|
|
+ mode='lines',
|
|
|
+ name=currTurbineInfo_group[Field_NameOfTurbine],
|
|
|
+ # 从 'Rainbow' 色组中循环选择颜色
|
|
|
+ line=dict(color=colors[idx % len(colors)])
|
|
|
+ ))
|
|
|
+ # 提取数据
|
|
|
+ turbine_data_total = {
|
|
|
+ "engineName": currTurbineInfo_group[Field_NameOfTurbine],
|
|
|
+ "engineCode": name,
|
|
|
+ "xData": group[Field_PowerFloor].tolist(),
|
|
|
+ "yData": y_data.tolist(),
|
|
|
+ "color": colors[idx % len(colors)]
|
|
|
+ }
|
|
|
+ turbine_data_list.append(turbine_data_total)
|
|
|
+
|
|
|
+ # Update layout and axes
|
|
|
+ fig.update_layout(
|
|
|
+ title={'text': f'{columnZH}分布-{turbineModelInfo[Field_MachineTypeCode]}', 'x': 0.5},
|
|
|
+ xaxis_title='功率',
|
|
|
+ yaxis_title=y_name,
|
|
|
+ xaxis=dict(
|
|
|
+ range=[
|
|
|
+ self.axisLowerLimitActivePower,
|
|
|
+ self.axisUpperLimitActivePower
|
|
|
+ ],
|
|
|
+ dtick=self.axisStepActivePower
|
|
|
+ ),
|
|
|
+ yaxis=dict(
|
|
|
+ range=[0, 100],
|
|
|
+ dtick=20
|
|
|
+ ),
|
|
|
+ # legend_title_text='Turbine'，
|
|
|
+ legend=dict(
|
|
|
+ orientation="h", # Horizontal orientation
|
|
|
+ xanchor="center", # Anchor the legend to the center
|
|
|
+ x=0.5, # Position legend at the center of the x-axis
|
|
|
+ y=-0.2, # Position legend below the x-axis
|
|
|
+ # itemsizing='constant', # Keep the size of the legend entries constant
|
|
|
+ # # Set the width of the legend items (in pixels)
|
|
|
+ # itemwidth=50
|
|
|
+ )
|
|
|
+ )
|
|
|
+
|
|
|
+ # Save the plot as a PNG/HTML file
|
|
|
+ # filePathOfImage = os.path.join(outputPath, f"{columnZH}-{turbineModelInfo[Field_MillTypeCode]}.png")
|
|
|
+ # fig.write_image(filePathOfImage, scale=3)
|
|
|
+ # filePathOfHtml = os.path.join(outputPath, f"{columnZH}-{turbineModelInfo[Field_MillTypeCode]}.html")
|
|
|
+ #fig.write_html(filePathOfHtml)
|
|
|
+
|
|
|
+ engineTypeCode = turbineModelInfo.get(Field_MillTypeCode, "")
|
|
|
+ if isinstance(engineTypeCode, pd.Series):
|
|
|
+ engineTypeCode = engineTypeCode.iloc[0]
|
|
|
+
|
|
|
+ engineTypeName = turbineModelInfo.get(Field_MachineTypeCode, "")
|
|
|
+ if isinstance(engineTypeName, pd.Series):
|
|
|
+ engineTypeName = engineTypeName.iloc[0]
|
|
|
+ # 构建最终的JSON对象
|
|
|
+ json_output = {
|
|
|
+ "analysisTypeCode": "大部件温度传感器分析",
|
|
|
+ "typecode": turbineModelInfo[Field_MillTypeCode],
|
|
|
+ "engineCode": engineTypeCode,
|
|
|
+ "engineTypeName": engineTypeName,
|
|
|
+ "title": f'{columnZH}分布-{turbineModelInfo[Field_MachineTypeCode]}',
|
|
|
+ "xaixs": "功率(kW)",
|
|
|
+ "yaixs": y_name,
|
|
|
+ "data": turbine_data_list
|
|
|
+ }
|
|
|
+
|
|
|
+ # 将JSON对象保存到文件
|
|
|
+ output_json_path = os.path.join(outputPath,f"{column}_{turbineModelInfo[Field_MillTypeCode]}.json")
|
|
|
+ with open(output_json_path, 'w', encoding='utf-8') as f:
|
|
|
+ import json
|
|
|
+ json.dump(json_output, f, ensure_ascii=False, indent=4)
|
|
|
+
|
|
|
+ # result_rows.append({
|
|
|
+ # Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
+ # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
+ # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
+ # Field_CodeOfTurbine: Const_Output_Total,
|
|
|
+ # Field_Return_FilePath: filePathOfImage,
|
|
|
+ # Field_Return_IsSaveDatabase: False
|
|
|
+ # })
|
|
|
+
|
|
|
+ # result_rows.append({
|
|
|
+ # Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
+ # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
+ # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
+ # Field_CodeOfTurbine: Const_Output_Total,
|
|
|
+ # Field_Return_FilePath: filePathOfHtml,
|
|
|
+ # Field_Return_IsSaveDatabase: True
|
|
|
+ # })
|
|
|
+
|
|
|
+ # 如果需要返回DataFrame，可以包含文件路径
|
|
|
+ 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: f'{column}_{turbineModelInfo[Field_MillTypeCode]}',
|
|
|
+ Field_Return_FilePath: output_json_path,
|
|
|
+ Field_Return_IsSaveDatabase: True
|
|
|
+ })
|
|
|
+
|
|
|
+ # Create individual plots with specific turbine highlighted
|
|
|
+ # for name, group in grouped:
|
|
|
+ for idx, (name, group) in enumerate(grouped):
|
|
|
+ single_fig = go.Figure()
|
|
|
+ currTurbineInfo_each = self.common.getTurbineInfo(
|
|
|
+ conf.dataContract.dataFilter.powerFarmID, name, self.turbineInfo)
|
|
|
+
|
|
|
+ # 创建一个列表来存储各个风电机组的数据
|
|
|
+ turbine_data_list_each = []
|
|
|
+
|
|
|
+ # Add all other turbines in grey first
|
|
|
+ # for other_name, other_group in grouped:
|
|
|
+ for idx, (other_name, other_group) in enumerate(grouped):
|
|
|
+ if other_name != name:
|
|
|
+ tempTurbineInfo = self.common.getTurbineInfo(
|
|
|
+ conf.dataContract.dataFilter.powerFarmID, other_name, self.turbineInfo)
|
|
|
+
|
|
|
+ # --- 数据计算 ---
|
|
|
+ ox_data = other_group[Field_PowerFloor]
|
|
|
+ oy_data = other_group[column]
|
|
|
+ if not is_nac_temp and Field_EnvTemp in other_group.columns:
|
|
|
+ oy_data = oy_data - other_group[Field_EnvTemp]
|
|
|
+ # ---------------
|
|
|
+
|
|
|
+ single_fig.add_trace(go.Scatter(
|
|
|
+ x=ox_data,
|
|
|
+ y=oy_data,
|
|
|
+ mode='lines',
|
|
|
+ name=tempTurbineInfo[Field_NameOfTurbine],
|
|
|
+ line=dict(color='lightgrey', width=1),
|
|
|
+ showlegend=False
|
|
|
+ ))
|
|
|
+ # 提取数据
|
|
|
+ turbine_data_other_each = {
|
|
|
+ "engineName": tempTurbineInfo[Field_NameOfTurbine],
|
|
|
+ "engineCode": other_name,
|
|
|
+ "xData": other_group[Field_PowerFloor].tolist(),
|
|
|
+ "yData": oy_data.tolist(),
|
|
|
+ }
|
|
|
+ turbine_data_list_each.append(turbine_data_other_each)
|
|
|
+
|
|
|
+ # Add the turbine of interest in dark blue
|
|
|
+ # --- 数据计算 ---
|
|
|
+ gx_data = group[Field_PowerFloor]
|
|
|
+ gy_data = group[column]
|
|
|
+ if not is_nac_temp and Field_EnvTemp in group.columns:
|
|
|
+ gy_data = gy_data - group[Field_EnvTemp]
|
|
|
+ # ---------------
|
|
|
+
|
|
|
+ single_fig.add_trace(go.Scatter(
|
|
|
+ x=gx_data,
|
|
|
+ y=gy_data,
|
|
|
+ mode='lines',
|
|
|
+ # Make it slightly thicker for visibility
|
|
|
+ line=dict(color='darkblue', width=2),
|
|
|
+ showlegend=False # Disable legend for cleaner look
|
|
|
+ ))
|
|
|
+
|
|
|
+ turbine_data_curr = {
|
|
|
+ "engineName": currTurbineInfo_each[Field_NameOfTurbine],
|
|
|
+ "engineCode": currTurbineInfo_each[Field_CodeOfTurbine],
|
|
|
+ "xData": group[Field_PowerFloor].tolist(),
|
|
|
+ "yData": gy_data.tolist(),
|
|
|
+ }
|
|
|
+ turbine_data_list_each.append(turbine_data_curr)
|
|
|
+
|
|
|
+ # Update layout and axes for the individual plot
|
|
|
+ single_fig.update_layout(
|
|
|
+ # title={'text': f'Turbine: {name}', 'x': 0.5},
|
|
|
+ title={'text': f'{columnZH}分布: {currTurbineInfo_each[Field_NameOfTurbine]}', 'x': 0.5},
|
|
|
+ xaxis_title='功率',
|
|
|
+ yaxis_title=y_name,
|
|
|
+ xaxis=dict(
|
|
|
+ range=[0, Field_RatedPower],
|
|
|
+ dtick=200
|
|
|
+ ),
|
|
|
+ yaxis=dict(
|
|
|
+ range=[0, 100],
|
|
|
+ dtick=20
|
|
|
+ )
|
|
|
+ )
|
|
|
+
|
|
|
+ engineTypeCode = turbineModelInfo.get(Field_MillTypeCode, "")
|
|
|
+ if isinstance(engineTypeCode, pd.Series):
|
|
|
+ engineTypeCode = engineTypeCode.iloc[0]
|
|
|
+
|
|
|
+ engineTypeName = turbineModelInfo.get(Field_MachineTypeCode, "")
|
|
|
+ if isinstance(engineTypeName, pd.Series):
|
|
|
+ engineTypeName = engineTypeName.iloc[0]
|
|
|
+ # 构建最终的JSON对象
|
|
|
+ json_output = {
|
|
|
+ "analysisTypeCode": "大部件温度传感器分析",
|
|
|
+ "typecode": turbineModelInfo[Field_MillTypeCode],
|
|
|
+ "engineCode": engineTypeCode,
|
|
|
+ "engineTypeName": engineTypeName,
|
|
|
+ "title": f'{columnZH}分布: {currTurbineInfo_each[Field_NameOfTurbine]}',
|
|
|
+ "xaixs": "功率(kW)",
|
|
|
+ "yaixs": y_name,
|
|
|
+ "data": turbine_data_list_each
|
|
|
+ }
|
|
|
+
|
|
|
+ # 将JSON对象保存到文件
|
|
|
+ output_json_path_each = os.path.join(outputPath, f"{currTurbineInfo_each[Field_NameOfTurbine]}.json")
|
|
|
+ with open(output_json_path_each, 'w', encoding='utf-8') as f:
|
|
|
+ import json
|
|
|
+ json.dump(json_output, f, ensure_ascii=False, indent=4)
|
|
|
+
|
|
|
+ # filePathOfImage = os.path.join(
|
|
|
+ # outputPath, f"{currTurbineInfo_each[Field_NameOfTurbine]}.png")
|
|
|
+ # single_fig.write_image(filePathOfImage, scale=3)
|
|
|
+ # filePathOfHtml = os.path.join(
|
|
|
+ # outputPath, f"{name}.html")
|
|
|
+ # single_fig.write_html(filePathOfHtml)
|
|
|
+
|
|
|
+ # result_rows.append({
|
|
|
+ # Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
+ # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
+ # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
+ # Field_CodeOfTurbine: group[Field_CodeOfTurbine].iloc[0],
|
|
|
+ # Field_Return_FilePath: filePathOfImage,
|
|
|
+ # Field_Return_IsSaveDatabase: False
|
|
|
+ # })
|
|
|
+
|
|
|
+ # 如果需要返回DataFrame，可以包含文件路径
|
|
|
+ result_rows.append({
|
|
|
+ Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
+ Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
+ Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
+ Field_CodeOfTurbine: group[Field_CodeOfTurbine].iloc[0],
|
|
|
+ Field_Return_FilePath: output_json_path_each,
|
|
|
+ Field_Return_IsSaveDatabase: True
|
|
|
+ })
|
|
|
+
|
|
|
+ # result_rows.append({
|
|
|
+ # Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
+ # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
+ # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
+ # Field_CodeOfTurbine: group[Field_CodeOfTurbine].iloc[0],
|
|
|
+ # Field_Return_FilePath: filePathOfHtml,
|
|
|
+ # Field_Return_IsSaveDatabase: True
|
|
|
+ # })
|
|
|
+
|
|
|
+ for idx, (name, group) in enumerate(grouped):
|
|
|
+ # 绘制每台机组发电机的，驱动轴承温度、非驱动轴承温度、发电机轴承温度BIAS、发电机轴承温度和机舱温度BIAS 均与有功功率的折线图
|
|
|
+ if not Field_GeneratorDE in group.columns or not Field_GeneratorNDE in group.columns or group[Field_GeneratorDE].isna().all() or group[Field_GeneratorNDE].isna().all():
|
|
|
+ self.logger.warning(f"{name} 不具备发电机温度测点，或测点值全为空")
|
|
|
+ continue
|
|
|
+ refFieldTemperature = Field_NacTemp if group[Field_EnvTemp].isna(
|
|
|
+ ).all() else Field_EnvTemp
|
|
|
+ result_rows1 = self.drawGeneratorTemperature(
|
|
|
+ group, conf, Field_GeneratorDE, Field_GeneratorNDE, refFieldTemperature, Field_PowerFloor, name, outputDir)
|
|
|
+ result_rows.extend(result_rows1)
|
|
|
+
|
|
|
+ result_df = pd.DataFrame(result_rows)
|
|
|
+
|
|
|
+ return result_df
|
|
|
+
|
|
|
+ def drawGeneratorTemperature(self, dataFrame: pd.DataFrame, conf: Contract, yAxisDE, yAxisNDE, diffTemperature, xAxis, turbineCode, outputDir):
|
|
|
+ tempTurbineInfo1 = self.common.getTurbineInfo(
|
|
|
+ conf.dataContract.dataFilter.powerFarmID, turbineCode, self.turbineInfo)
|
|
|
+ turbineName = tempTurbineInfo1[Field_NameOfTurbine]
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+ # 发电机驱动轴承温度 和 发电机非驱动轴承 温差
|
|
|
+ fieldBIAS_DE_NDE = 'BIAS_DE-NDE'
|
|
|
+ fieldBIAS_DE = 'BIAS_DE'
|
|
|
+ fieldBIAS_NDE = 'BIAS_NDE'
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|
|
+
|
|
|
+ # Prepare Data
|
|
|
+ dataFrame[fieldBIAS_DE] = dataFrame[yAxisDE] - \
|
|
|
+ dataFrame[diffTemperature]
|
|
|
+ dataFrame[fieldBIAS_NDE] = dataFrame[yAxisNDE] - \
|
|
|
+ dataFrame[diffTemperature]
|
|
|
+ dataFrame[fieldBIAS_DE_NDE] = dataFrame[yAxisDE] - dataFrame[yAxisNDE]
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|
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+
|
|
|
+ # Create a plot with dual y-axes
|
|
|
+ fig = make_subplots(specs=[[{"secondary_y": True}]])
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|
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+
|
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|
+ plot_data_list_each = []
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|
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+
|
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+ # Plot DE Bearing Temperature
|
|
|
+ fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[yAxisDE], name='驱动端轴承温度', line=dict(
|
|
|
+ color='blue')), secondary_y=False)
|
|
|
+
|
|
|
+ plot_data_curr = {
|
|
|
+ "Name": '驱动端轴承温度',
|
|
|
+ "xData": dataFrame[xAxis].tolist(),
|
|
|
+ "yData": dataFrame[yAxisDE].tolist(),
|
|
|
+ "color": 'blue',
|
|
|
+ }
|
|
|
+ plot_data_list_each.append(plot_data_curr)
|
|
|
+
|
|
|
+ # Plot NDE Bearing Temperature
|
|
|
+ fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[yAxisNDE], name='非驱动端轴承温度', line=dict(
|
|
|
+ color='green')), secondary_y=False)
|
|
|
+
|
|
|
+ plot_data_curr = {
|
|
|
+ "Name": '非驱动端轴承温度',
|
|
|
+ "xData": dataFrame[xAxis].tolist(),
|
|
|
+ "yData": dataFrame[yAxisNDE].tolist(),
|
|
|
+ "color": 'green',
|
|
|
+ }
|
|
|
+ plot_data_list_each.append(plot_data_curr)
|
|
|
+
|
|
|
+ # Plot Temperature Differences
|
|
|
+ fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[fieldBIAS_DE], name='驱动端轴承温度与机舱温度偏差', line=dict(
|
|
|
+ color='blue', dash='dot')), secondary_y=False)
|
|
|
+ plot_data_curr = {
|
|
|
+ "Name": '驱动端轴承温度与机舱温度偏差',
|
|
|
+ "xData": dataFrame[xAxis].tolist(),
|
|
|
+ "yData": dataFrame[fieldBIAS_DE].tolist(),
|
|
|
+ "color": 'blue',
|
|
|
+ }
|
|
|
+ plot_data_list_each.append(plot_data_curr)
|
|
|
+
|
|
|
+ fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[fieldBIAS_NDE], name='非驱动端轴承温度与机舱温度偏差', line=dict(
|
|
|
+ color='green', dash='dot')), secondary_y=False)
|
|
|
+ plot_data_curr = {
|
|
|
+ "Name": '非驱动端轴承温度与机舱温度偏差',
|
|
|
+ "xData": dataFrame[xAxis].tolist(),
|
|
|
+ "yData": dataFrame[fieldBIAS_NDE].tolist(),
|
|
|
+ "color": 'green',
|
|
|
+ }
|
|
|
+ plot_data_list_each.append(plot_data_curr)
|
|
|
+
|
|
|
+ fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[fieldBIAS_DE_NDE],
|
|
|
+ name='驱动端轴承与非驱动端轴承温度偏差', line=dict(color='black', dash='dash')), secondary_y=False)
|
|
|
+ plot_data_curr = {
|
|
|
+ "Name": '驱动端轴承与非驱动端轴承温度偏差',
|
|
|
+ "xData": dataFrame[xAxis].tolist(),
|
|
|
+ "yData": dataFrame[fieldBIAS_DE_NDE].tolist(),
|
|
|
+ "color": 'black',
|
|
|
+ }
|
|
|
+ plot_data_list_each.append(plot_data_curr)
|
|
|
+
|
|
|
+ # Plot Nacelle Temperature
|
|
|
+ fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[diffTemperature],
|
|
|
+ name='机舱温度', line=dict(color='orange')), secondary_y=False)
|
|
|
+ plot_data_curr = {
|
|
|
+ "Name": '机舱温度',
|
|
|
+ "xData": dataFrame[xAxis].tolist(),
|
|
|
+ "yData": dataFrame[diffTemperature].tolist(),
|
|
|
+ "color": 'orange',
|
|
|
+ }
|
|
|
+ plot_data_list_each.append(plot_data_curr)
|
|
|
+
|
|
|
+ # Add horizontal reference lines
|
|
|
+ fig.add_hline(y=5, line_dash="dot", line_color="#FFDB58")
|
|
|
+ fig.add_hline(y=-5, line_dash="dot", line_color="#FFDB58")
|
|
|
+
|
|
|
+ fig.add_hline(y=15, line_dash="dot", line_color="red")
|
|
|
+ fig.add_hline(y=-15, line_dash="dot", line_color="red")
|
|
|
+
|
|
|
+ # Update layout
|
|
|
+ fig.update_layout(
|
|
|
+ title={'text': f'发电机温度偏差: {turbineName}'},
|
|
|
+ xaxis_title="功率",
|
|
|
+ yaxis_title='轴承温度 & 偏差',
|
|
|
+ legend_title='温度 & 偏差',
|
|
|
+ legend=dict(x=1.1, y=0.5, bgcolor='rgba(255, 255, 255, 0.5)'),
|
|
|
+ margin=dict(r=200) # Adjust margin to fit legend
|
|
|
+ )
|
|
|
+
|
|
|
+ fig.update_yaxes(range=[-20, 100], secondary_y=False)
|
|
|
+
|
|
|
+
|
|
|
+ # 构建最终的JSON对象
|
|
|
+ json_output = {
|
|
|
+ "analysisTypeCode": "发电机温度传感器分析",
|
|
|
+ "turbineName": tempTurbineInfo1[Field_NameOfTurbine],
|
|
|
+ "turbineCode": tempTurbineInfo1[Field_CodeOfTurbine],
|
|
|
+ "title": f'发电机温度偏差: {turbineName}',
|
|
|
+ "xaixs": "功率(kW)",
|
|
|
+ "yaixs": '轴承温度 & 偏差(℃)',
|
|
|
+ "data": plot_data_list_each
|
|
|
+ }
|
|
|
+
|
|
|
+ # 将JSON对象保存到文件
|
|
|
+ output_json_path_each = os.path.join(outputDir, f"{turbineName}.json")
|
|
|
+ with open(output_json_path_each, 'w', encoding='utf-8') as f:
|
|
|
+ import json
|
|
|
+ json.dump(json_output, f, ensure_ascii=False, indent=4)
|
|
|
+ # Save the plot as a PNG/HTML file
|
|
|
+ # filePathOfImage = os.path.join(outputDir, f"{turbineName}.png")
|
|
|
+ # fig.write_image(filePathOfImage, width=800, height=600, scale=3)
|
|
|
+ # filePathOfHtml = os.path.join(outputDir, f"{turbineName}.html")
|
|
|
+ # fig.write_html(filePathOfHtml)
|
|
|
+
|
|
|
+ result_rows1 = []
|
|
|
+ # result_rows1.append({
|
|
|
+ # Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
+ # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
+ # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
+ # Field_CodeOfTurbine: dataFrame[Field_CodeOfTurbine].iloc[0],
|
|
|
+ # Field_Return_FilePath: filePathOfImage,
|
|
|
+ # Field_Return_IsSaveDatabase: False
|
|
|
+ # })
|
|
|
+
|
|
|
+ # 如果需要返回DataFrame，可以包含文件路径
|
|
|
+ result_rows1.append({
|
|
|
+ Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
+ Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
+ Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
+ Field_CodeOfTurbine: dataFrame[Field_CodeOfTurbine].iloc[0],
|
|
|
+ Field_Return_FilePath: output_json_path_each,
|
|
|
+ Field_Return_IsSaveDatabase: True
|
|
|
+ })
|
|
|
+
|
|
|
+ # result_rows1.append({
|
|
|
+ # Field_Return_TypeAnalyst: self.typeAnalyst(),
|
|
|
+ # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
|
|
|
+ # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
|
|
|
+ # Field_CodeOfTurbine: dataFrame[Field_CodeOfTurbine].iloc[0],
|
|
|
+ # Field_Return_FilePath: filePathOfHtml,
|
|
|
+ # Field_Return_IsSaveDatabase: True
|
|
|
+ # })
|
|
|
+
|
|
|
+ return result_rows1
|
Xmia