generatorSpeedPowerAnalyst.py 15 KB

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  1. import os
  2. from datetime import datetime
  3. import numpy as np
  4. import pandas as pd
  5. import plotly.express as px
  6. import plotly.graph_objects as go
  7. from algorithmContract.confBusiness import *
  8. from algorithmContract.contract import Contract
  9. from behavior.analystWithGoodPoint import AnalystWithGoodPoint
  10. class GeneratorSpeedPowerAnalyst(AnalystWithGoodPoint):
  11. """
  12. 风电机组发电机转速-有功功率分析
  13. """
  14. def typeAnalyst(self):
  15. return "speed_power"
  16. def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes):
  17. dictionary = self.processTurbineData(turbineCodes, conf, [
  18. Field_DeviceCode, Field_Time,Field_RotorSpeed,Field_GeneratorSpeed, Field_WindSpeed, Field_ActiverPower])
  19. dataFrameOfTurbines = self.userDataFrame(
  20. dictionary, conf.dataContract.configAnalysis, self)
  21. # 检查所需列是否存在
  22. required_columns = {Field_CodeOfTurbine,Field_RotorSpeed,Field_GeneratorSpeed,Field_ActiverPower}
  23. if not required_columns.issubset(dataFrameOfTurbines.columns):
  24. raise ValueError(f"DataFrame缺少必要的列。需要的列有: {required_columns}")
  25. turbrineInfos = self.common.getTurbineInfos(
  26. conf.dataContract.dataFilter.powerFarmID, turbineCodes, self.turbineInfo)
  27. groupedOfTurbineModel = turbrineInfos.groupby(Field_MillTypeCode)
  28. returnDatas = []
  29. for turbineModelCode, group in groupedOfTurbineModel:
  30. currTurbineCodes = group[Field_CodeOfTurbine].unique().tolist()
  31. currTurbineModeInfo = self.common.getTurbineModelByCode(
  32. turbineModelCode, self.turbineModelInfo)
  33. currDataFrameOfTurbines = dataFrameOfTurbines[dataFrameOfTurbines[Field_CodeOfTurbine].isin(
  34. currTurbineCodes)]
  35. # 将 currTurbineInfos 转换为字典
  36. currTurbineInfos_dict = turbrineInfos.set_index(Field_CodeOfTurbine)[Field_NameOfTurbine].to_dict()
  37. # 使用 map 函数来填充 Field_NameOfTurbine 列
  38. currDataFrameOfTurbines[Field_NameOfTurbine] = currDataFrameOfTurbines[Field_CodeOfTurbine].map(currTurbineInfos_dict).fillna("")
  39. result2D = self.drawScatter2DMonthly(
  40. currDataFrameOfTurbines, outputAnalysisDir, conf)
  41. returnDatas.extend(result2D)
  42. result3D = self.drawScatterGraph(
  43. currDataFrameOfTurbines, outputAnalysisDir, conf)
  44. returnDatas.extend(result3D)
  45. resultTotal = self.drawScatterGraphForTurbines(
  46. currDataFrameOfTurbines, outputAnalysisDir, conf, currTurbineModeInfo)
  47. returnDatas.extend(resultTotal)
  48. returnDataFrame = pd.DataFrame(returnDatas)
  49. return returnDataFrame
  50. def drawScatter2DMonthlyOfTurbine(self, dataFrame: pd.DataFrame, outputAnalysisDir: str, conf: Contract, turbineName: str):
  51. # 设置颜色条参数
  52. dataFrame = dataFrame.sort_values(by=Field_YearMonth)
  53. # 绘制 Plotly 散点图
  54. fig = go.Figure(data=go.Scatter(
  55. x=dataFrame[Field_GeneratorSpeed],
  56. y=dataFrame[Field_ActiverPower],
  57. mode='markers',
  58. marker=dict(
  59. color=dataFrame[Field_UnixYearMonth],
  60. colorscale='Rainbow',
  61. size=3,
  62. opacity=0.7,
  63. colorbar=dict(
  64. tickvals=np.linspace(
  65. dataFrame[Field_UnixYearMonth].min(), dataFrame[Field_UnixYearMonth].max(), 6),
  66. ticktext=[datetime.fromtimestamp(ts).strftime('%Y-%m') for ts in np.linspace(
  67. dataFrame[Field_UnixYearMonth].min(), dataFrame[Field_UnixYearMonth].max(), 6)],
  68. thickness=18,
  69. len=1, # 设置颜色条的长度,使其占据整个图的高度
  70. outlinecolor='rgba(255,255,255,0)'
  71. ),
  72. showscale=True
  73. ),
  74. showlegend=False
  75. ))
  76. # # 设置固定散点大小
  77. # fig.update_traces(marker=dict(size=3))
  78. # 如果需要颜色轴的刻度和标签
  79. # 以下是以比例方式进行色彩的可视化处理
  80. fig.update_layout(
  81. title={
  82. "text": f'月度发电机转速功率散点图: {turbineName}',
  83. # "x": 0.5
  84. },
  85. xaxis=dict(
  86. title='发电机转速',
  87. dtick=self.axisStepGeneratorSpeed,
  88. range=[self.axisLowerLimitGeneratorSpeed,
  89. self.axisUpperLimitGeneratorSpeed],
  90. tickangle=-45
  91. ),
  92. yaxis=dict(
  93. title='功率',
  94. dtick=self.axisStepActivePower,
  95. range=[self.axisLowerLimitActivePower,
  96. self.axisUpperLimitActivePower],
  97. ),
  98. coloraxis=dict(
  99. colorbar=dict(
  100. title="时间",
  101. ticks="outside",
  102. len=1, # 设置颜色条的长度,使其占据整个图的高度
  103. thickness=20, # 调整颜色条的宽度
  104. orientation='v', # 设置颜色条为垂直方向
  105. tickmode='array', # 确保刻度按顺序排列
  106. tickvals=dataFrame[Field_YearMonth].unique(
  107. ).tolist(), # 确保刻度为唯一的年月
  108. ticktext=dataFrame[Field_YearMonth].unique(
  109. ).tolist() # 以%Y-%m格式显示标签
  110. )
  111. )
  112. )
  113. # 保存图片
  114. outputFilePathPNG = os.path.join(
  115. outputAnalysisDir, f"{turbineName}.png")
  116. fig.write_image(outputFilePathPNG, width=800, height=600, scale=3)
  117. # 保存html
  118. outputFileHtml = os.path.join(outputAnalysisDir, f"{turbineName}.html")
  119. fig.write_html(outputFileHtml)
  120. result = []
  121. result.append({
  122. Field_Return_TypeAnalyst: self.typeAnalyst(),
  123. Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
  124. Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
  125. Field_CodeOfTurbine: dataFrame[Field_CodeOfTurbine].iloc[0],
  126. Field_Return_FilePath: outputFilePathPNG,
  127. Field_Return_IsSaveDatabase: False
  128. })
  129. result.append({
  130. Field_Return_TypeAnalyst: self.typeAnalyst(),
  131. Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
  132. Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
  133. Field_CodeOfTurbine: dataFrame[Field_CodeOfTurbine].iloc[0],
  134. Field_Return_FilePath: outputFileHtml,
  135. Field_Return_IsSaveDatabase: True
  136. })
  137. return result
  138. def drawScatterGraphOfTurbine(self, dataFrame: pd.DataFrame, outputAnalysisDir: str, conf: Contract, turbineName: str):
  139. # 创建3D散点图
  140. fig = px.scatter_3d(dataFrame,
  141. x=Field_GeneratorSpeed,
  142. y=Field_YearMonth,
  143. z=Field_ActiverPower,
  144. color=Field_YearMonth,
  145. labels={Field_GeneratorSpeed: '发电机转速',
  146. Field_YearMonth: '时间', Field_ActiverPower: '功率'}
  147. )
  148. # 设置固定散点大小
  149. fig.update_traces(marker=dict(size=1.5))
  150. # 更新图形的布局
  151. fig.update_layout(
  152. title={
  153. "text": f'月度发电机转速功率3D散点图: {turbineName}',
  154. # "x": 0.5
  155. },
  156. scene=dict(
  157. xaxis=dict(
  158. title='发电机转速',
  159. dtick=self.axisStepGeneratorSpeed, # 设置y轴刻度间隔
  160. range=[self.axisLowerLimitGeneratorSpeed,
  161. self.axisUpperLimitGeneratorSpeed], # 设置y轴的范围
  162. showgrid=True, # 显示网格线
  163. ),
  164. yaxis=dict(
  165. title='时间',
  166. tickformat='%Y-%m', # 日期格式,
  167. dtick='M1', # 每月一个刻度
  168. showgrid=True, # 显示网格线
  169. ),
  170. zaxis=dict(
  171. title='功率',
  172. dtick=self.axisStepActivePower,
  173. range=[self.axisLowerLimitActivePower,
  174. self.axisUpperLimitActivePower],
  175. showgrid=True, # 显示网格线
  176. )
  177. ),
  178. scene_camera=dict(
  179. up=dict(x=0, y=0, z=1), # 保持相机向上方向不变
  180. center=dict(x=0, y=0, z=0), # 保持相机中心位置不变
  181. eye=dict(x=-1.8, y=-1.8, z=1.2) # 调整eye属性以实现水平旋转180°
  182. ),
  183. # 设置图例标题
  184. # legend_title_text='Time',
  185. legend=dict(
  186. orientation="h",
  187. itemsizing="constant", # Use constant size for legend items
  188. itemwidth=80 # Set the width of legend items to 50 pixels
  189. )
  190. )
  191. # 保存图像
  192. outputFileHtml = os.path.join(
  193. outputAnalysisDir, "{}_3D.html".format(turbineName))
  194. fig.write_html(outputFileHtml)
  195. result = []
  196. result.append({
  197. Field_Return_TypeAnalyst: self.typeAnalyst(),
  198. Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
  199. Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
  200. Field_CodeOfTurbine: dataFrame[Field_CodeOfTurbine].iloc[0],
  201. Field_Return_FilePath: outputFileHtml,
  202. Field_Return_IsSaveDatabase: True
  203. })
  204. return result
  205. def drawScatter2DMonthly(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir, conf: Contract):
  206. """
  207. 生成每台风电机组二维有功功率、发电机转速散点图表
  208. """
  209. results = []
  210. grouped = dataFrameMerge.groupby(Field_NameOfTurbine)
  211. for groupKey, group in grouped:
  212. result = self.drawScatter2DMonthlyOfTurbine(
  213. group, outputAnalysisDir, conf, groupKey)
  214. results.extend(result)
  215. return results
  216. def drawScatterGraph(self, dataFrame: pd.DataFrame, outputAnalysisDir: str, conf: Contract):
  217. """
  218. 绘制风速-功率分布图并保存为文件。
  219. 参数:
  220. dataFrameMerge (pd.DataFrame): 包含数据的DataFrame,需要包含设备名、风速和功率列。
  221. outputAnalysisDir (str): 分析输出目录。
  222. confData (Contract): 配置
  223. """
  224. results = []
  225. dataFrame = dataFrame[(dataFrame[Field_ActiverPower] > 0)].sort_values(
  226. by=Field_YearMonth)
  227. grouped = dataFrame.groupby(Field_NameOfTurbine)
  228. # 遍历每个设备的数据
  229. for groupKey, group in grouped:
  230. if len(group[Field_YearMonth].unique()) > 1:
  231. result = self.drawScatterGraphOfTurbine(
  232. group, outputAnalysisDir, conf, groupKey)
  233. results.extend(result)
  234. return results
  235. def drawScatterGraphForTurbines(self, dataFrame: pd.DataFrame, outputAnalysisDir, conf: Contract, turbineModelInfo: pd.Series):
  236. """
  237. 绘制风速-功率分布图并保存为文件。 (须按照机型分组)
  238. 参数:
  239. dataFrameMerge (pd.DataFrame): 包含数据的DataFrame,需要包含设备名、风速和功率列。
  240. outputAnalysisDir (str): 分析输出目录。
  241. confData (Contract): 配置
  242. """
  243. dataFrame = dataFrame[(dataFrame[Field_ActiverPower] > 0)].sort_values(by=Field_NameOfTurbine)
  244. # 创建3D散点图
  245. fig = px.scatter_3d(dataFrame,
  246. x=Field_GeneratorSpeed,
  247. y=Field_NameOfTurbine,
  248. z=Field_ActiverPower,
  249. color=Field_NameOfTurbine,
  250. labels={Field_GeneratorSpeed: '发电机转速',
  251. Field_NameOfTurbine: '风机', Field_ActiverPower: '功率'},
  252. )
  253. # 设置固定散点大小
  254. fig.update_traces(marker=dict(size=1.5))
  255. # 更新图形的布局
  256. fig.update_layout(
  257. title={
  258. "text": f'风机发电机转速功率3D散点图-{turbineModelInfo[Field_MachineTypeCode]}',
  259. "x": 0.5
  260. },
  261. scene=dict(
  262. xaxis=dict(
  263. title='发电机转速',
  264. dtick=self.axisStepGeneratorSpeed, # 设置y轴刻度间隔
  265. range=[self.axisLowerLimitGeneratorSpeed,
  266. self.axisUpperLimitGeneratorSpeed], # 设置y轴的范围
  267. showgrid=True, # 显示网格线
  268. ),
  269. yaxis=dict(
  270. title='机组',
  271. showgrid=True, # 显示网格线
  272. ),
  273. zaxis=dict(
  274. title='功率',
  275. dtick=self.axisStepActivePower,
  276. range=[self.axisLowerLimitActivePower,
  277. self.axisUpperLimitActivePower],
  278. showgrid=True, # 显示网格线
  279. )
  280. ),
  281. scene_camera=dict(
  282. up=dict(x=0, y=0, z=1), # 保持相机向上方向不变
  283. center=dict(x=0, y=0, z=0), # 保持相机中心位置不变
  284. eye=dict(x=-1.8, y=-1.8, z=1.2) # 调整eye属性以实现水平旋转180°
  285. ),
  286. # 设置图例标题
  287. # legend_title_text='Turbine'
  288. legend=dict(
  289. orientation="h",
  290. itemsizing="constant", # Use constant size for legend items
  291. itemwidth=80 # Set the width of legend items to 50 pixels
  292. )
  293. )
  294. # 保存图像
  295. outputFileHtml = os.path.join(
  296. outputAnalysisDir, "{}-{}.html".format(self.typeAnalyst(),turbineModelInfo[Field_MillTypeCode]))
  297. fig.write_html(outputFileHtml)
  298. result = []
  299. result.append({
  300. Field_Return_TypeAnalyst: self.typeAnalyst(),
  301. Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
  302. Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
  303. Field_CodeOfTurbine: Const_Output_Total,
  304. Field_Return_FilePath: outputFileHtml,
  305. Field_Return_IsSaveDatabase: True
  306. })
  307. return result