tsrTrendAnalyst.py 5.8 KB

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  1. import os
  2. import pandas as pd
  3. import plotly.graph_objects as go
  4. from algorithmContract.confBusiness import *
  5. from algorithmContract.contract import Contract
  6. from behavior.analystWithGoodPoint import AnalystWithGoodPoint
  7. class TSRTrendAnalyst(AnalystWithGoodPoint):
  8. """
  9. 风电机组叶尖速比时序分析
  10. """
  11. def typeAnalyst(self):
  12. return "tsr_trend"
  13. def selectColumns(self):
  14. return [Field_DeviceCode,Field_Time,Field_WindSpeed,Field_ActiverPower,Field_RotorSpeed,Field_GeneratorSpeed]
  15. def processDateTime(self, dataFrame: pd.DataFrame, fieldTime:str = None):
  16. super().processDateTime(dataFrame,Field_YearMonthDay)
  17. def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes):
  18. dictionary = self.processTurbineData(turbineCodes,conf,self.selectColumns())
  19. dataFrameMerge = self.userDataFrame(dictionary,conf.dataContract.configAnalysis,self)
  20. return self.drawTSRTrend(dataFrameMerge, outputAnalysisDir, conf)
  21. def drawTSRTrend(self,dataFrameMerge:pd.DataFrame, outputAnalysisDir, conf: Contract):
  22. # 检查所需列是否存在
  23. required_columns = {Field_TSR, Field_YearMonthDay}
  24. if not required_columns.issubset(dataFrameMerge.columns):
  25. raise ValueError(f"DataFrame缺少必要的列。需要的列有: {required_columns}")
  26. # 按设备名分组数据
  27. grouped = dataFrameMerge.groupby([Field_NameOfTurbine, Field_CodeOfTurbine])
  28. result_rows = []
  29. for name, group in grouped:
  30. # 计算四分位数和IQR
  31. Q1 = group[Field_TSR].quantile(0.25)
  32. Q3 = group[Field_TSR].quantile(0.75)
  33. IQR = Q3 - Q1
  34. # 定义离群值的范围
  35. lower_bound = Q1 - 1.5 * IQR
  36. upper_bound = Q3 + 1.5 * IQR
  37. # 筛选掉离群值
  38. filtered_group = group[(group[Field_TSR] >= lower_bound) & (group[Field_TSR] <= upper_bound)]
  39. # 创建箱线图
  40. fig = go.Figure()
  41. fig.add_trace(go.Box(
  42. x=filtered_group[Field_YearMonthDay], # 设置x轴数据为日期
  43. y=filtered_group[Field_TSR], # 设置y轴数据为风能利用系数
  44. # boxpoints='outliers', # 显示异常值(偏离值),不显示数据的所有点(只显示异常值)
  45. boxpoints=False, # 不显示偏离值
  46. marker=dict(color='lightgoldenrodyellow', size=1), # 设置偏离值的颜色和大小
  47. line=dict(color='lightgray', width=2), # 设置箱线和须线的颜色为灰色,粗细为2
  48. fillcolor='rgba(200, 200, 200, 0.5)', # 设置箱体的填充颜色和透明度
  49. name='叶尖速比' # 图例名称
  50. ))
  51. # 对于每个箱线图的中位数,绘制一个蓝色点
  52. medians = filtered_group.groupby(filtered_group[Field_YearMonthDay])[Field_TSR].median()
  53. fig.add_trace(go.Scatter(
  54. x=medians.index,
  55. y=medians.values,
  56. mode='markers',
  57. marker=dict(color='orange', size=3),
  58. name='叶尖速比中位数' # 中位数标记的图例名称
  59. ))
  60. # 设置图表的标题和轴标签
  61. fig.update_layout(
  62. title={
  63. 'text': f'机组: {name[0]}',
  64. #'x':0.5,
  65. },
  66. xaxis_title='时间',
  67. yaxis_title='叶尖速比',
  68. xaxis=dict(
  69. tickmode='auto', # 自动设置x轴刻度,以适应日期数据
  70. tickformat='%Y-%m-%d', # 设置x轴时间格式
  71. showgrid=True, # 显示网格线
  72. gridcolor='lightgray', # setting y-axis gridline color to black
  73. tickangle=-45,
  74. linecolor='black', # 设置y轴坐标系线颜色为黑色
  75. ticklen=5, # 设置刻度线的长度
  76. ),
  77. yaxis=dict(
  78. range=[self.axisLowerLimitTSR, self.axisUpperLimitTSR],
  79. dtick=self.axisStepTSR,
  80. showgrid=True, # 显示网格线
  81. gridcolor='lightgray', # setting y-axis gridline color to black
  82. linecolor='black', # 设置y轴坐标系线颜色为黑色
  83. ticklen=5, # 设置刻度线的长度
  84. ),
  85. paper_bgcolor='white', # 设置纸张背景颜色为白色
  86. plot_bgcolor='white', # 设置图表背景颜色为白色
  87. margin=dict(t=50, b=10) # t为顶部(top)间距,b为底部(bottom)间距
  88. )
  89. # 保存图像
  90. filePathOfImage = os.path.join(outputAnalysisDir, f"{name[0]}.png")
  91. fig.write_image(filePathOfImage, scale=3)
  92. filePathOfHtml = os.path.join(outputAnalysisDir, f"{name[0]}.html")
  93. fig.write_html(filePathOfHtml)
  94. result_rows.append({
  95. Field_Return_TypeAnalyst: self.typeAnalyst(),
  96. Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
  97. Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
  98. Field_CodeOfTurbine: name[1],
  99. Field_Return_FilePath: filePathOfImage,
  100. Field_Return_IsSaveDatabase: False
  101. })
  102. result_rows.append({
  103. Field_Return_TypeAnalyst: self.typeAnalyst(),
  104. Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
  105. Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
  106. Field_CodeOfTurbine: name[1],
  107. Field_Return_FilePath: filePathOfHtml,
  108. Field_Return_IsSaveDatabase: True
  109. })
  110. result_df = pd.DataFrame(result_rows)
  111. return result_df