ClassIdentifier.py 16 KB

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  1. import datetime
  2. import numpy as np
  3. from pandas import DataFrame
  4. from utils.file.trans_methods import read_file_to_df
  5. from utils.log.trans_log import trans_print
  6. class ClassIdentifier(object):
  7. """
  8. 分类标识 -1:停机 0:好点 1:欠发功率点;2:超发功率点;3:额定风速以上的超发功率点 4: 限电
  9. """
  10. def __init__(self, wind_turbine_number="", origin_df: DataFrame = None,
  11. wind_velocity='wind_velocity',
  12. active_power='active_power',
  13. pitch_angle_blade='pitch_angle_blade_1',
  14. rated_power=1500, cut_out_speed=20, file_path: str = None):
  15. """
  16. :param file_path: The file path of the input data.
  17. :param origin_df: The pandas DataFrame containing the input data.
  18. :param wind_velocity: 风速字段
  19. :param active_power: 有功功率字段
  20. :param pitch_angle_blade: 桨距角
  21. :param rated_power: 额定功率
  22. :param cut_out_speed: 切出风速
  23. """
  24. self.wind_turbine_number = wind_turbine_number
  25. self.wind_velocity = wind_velocity
  26. self.active_power = active_power
  27. self.pitch_angle_blade = pitch_angle_blade
  28. self.rated_power = rated_power # 额定功率1500kw,可改为2000kw
  29. self.cut_out_speed = cut_out_speed
  30. if self.rated_power is None:
  31. trans_print(wind_turbine_number, "WARNING:rated_power配置为空的")
  32. self.rated_power = 1500
  33. if self.cut_out_speed is None:
  34. trans_print(cut_out_speed, "WARNING:cut_out_speed配置为空的")
  35. self.cut_out_speed = 20
  36. if file_path is None and origin_df is None:
  37. raise ValueError("Either file_path or origin_df should be provided.")
  38. if file_path:
  39. self.df = read_file_to_df(file_path)
  40. else:
  41. self.df = origin_df
  42. def identifier(self):
  43. # 风速 和 有功功率 df
  44. # self.df = self.df[[self.wind_velocity, self.active_power, "pitch_angle_blade_1"]]
  45. self.df.reset_index(inplace=True)
  46. wind_and_power_df_count = self.df.shape[0]
  47. power_max = self.df[self.active_power].max()
  48. power_rated = np.ceil(power_max / 100) * 100
  49. v_cut_out = self.cut_out_speed
  50. # 网格法确定风速风向分区数量,功率方向分区数量,
  51. power_bin_count = int(np.ceil(power_rated / 25)) # 功率分区间隔25kW
  52. velocity_bin_count = int(np.ceil(v_cut_out / 0.25)) # 风速分区间隔0.25m/s
  53. # 存储功率大于零的运行数据
  54. power_gt_zero_array = np.zeros([wind_and_power_df_count, 2], dtype=float)
  55. power_gt_zero_array_count = 0
  56. for i in range(wind_and_power_df_count):
  57. if self.df.loc[i, self.active_power] > 0:
  58. power_gt_zero_array[power_gt_zero_array_count, 0] = self.df.loc[i, self.wind_velocity]
  59. power_gt_zero_array[power_gt_zero_array_count, 1] = self.df.loc[i, self.active_power]
  60. power_gt_zero_array_count = power_gt_zero_array_count + 1
  61. # 统计各网格落入的散点个数
  62. x_box_number = np.zeros([power_bin_count, velocity_bin_count], dtype=int)
  63. n_which_p = -1
  64. n_which_v = -1
  65. for i in range(power_gt_zero_array_count):
  66. for m in range(power_bin_count):
  67. if m * 25 < power_gt_zero_array[i, 1] <= (m + 1) * 25:
  68. n_which_p = m
  69. break
  70. # todo 风速不太懂为什么区间用 0.125 - 0.375 而不是 0 - 0.25 区间
  71. for n in range(velocity_bin_count):
  72. if (n * 0.25 + 0.125) < power_gt_zero_array[i, 0] <= ((n + 1) * 0.25 + 0.125):
  73. n_which_v = n
  74. break
  75. if n_which_p > -1 and n_which_v > -1:
  76. x_box_number[n_which_p, n_which_v] = x_box_number[n_which_p, n_which_v] + 1
  77. # 在功率方向将网格内散点绝对个数转换为相对百分比,备用
  78. power_box_percent = np.zeros([power_bin_count, velocity_bin_count], dtype=float)
  79. # 功率方向统计
  80. power_bin_sum = np.zeros(power_bin_count, dtype=int)
  81. for i in range(power_bin_count):
  82. power_bin_sum[i] = sum(x_box_number[i, :])
  83. # for m in range(velocity_bin_count):
  84. # power_bin_sum[i] = power_bin_sum[i] + x_box_number[i, m]
  85. for m in range(velocity_bin_count):
  86. if power_bin_sum[i] > 0:
  87. power_box_percent[i, m] = x_box_number[i, m] / power_bin_sum[i] * 100
  88. # 在风速方向将网格内散点绝对个数转换为相对百分比,备用
  89. v_box_percent = np.zeros([power_bin_count, velocity_bin_count], dtype=float)
  90. v_bin_sum = np.zeros(velocity_bin_count, dtype=int)
  91. for i in range(velocity_bin_count):
  92. v_bin_sum[i] = sum(x_box_number[:, i])
  93. # for m in range(power_bin_count):
  94. # v_bin_sum[i] = v_bin_sum[i] + x_box_number[m, i]
  95. for m in range(power_bin_count):
  96. if v_bin_sum[i] > 0:
  97. v_box_percent[m, i] = x_box_number[m, i] / v_bin_sum[i] * 100
  98. # 以水平功率带方向为准,分析每个水平功率带中,功率主带中心,即找百分比最大的网格位置。
  99. p_box_max_index = np.zeros(power_bin_count, dtype=int) # 水平功率带最大网格位置索引
  100. p_box_max_p = np.zeros(power_bin_count, dtype=int) # 水平功率带最大网格百分比
  101. for m in range(power_bin_count):
  102. # 确定每一水平功率带的最大网格位置索引即百分比值
  103. p_box_max_p[m], p_box_max_index[m] = power_box_percent[m, :].max(), power_box_percent[m, :].argmax()
  104. # 切入风速特殊处理,如果切入风速过于偏右,向左拉回
  105. # todo 为什么第一行数据的索引值 > 14个就要往左拉回,还有是不是不叫切入风速,这个是 落入这个区间功率最多的个数的索引值
  106. if p_box_max_index[0] > 14:
  107. p_box_max_index[0] = 9
  108. # 以水平功率带方向为基准,进行分析
  109. dot_dense_left_right = np.zeros([power_bin_count, 2], dtype=int) # 存储每一水平功率带的功率主带以最大网格为中心,向向左,向右扩展的网格数
  110. dot_valve = 90 # 从中心向左右对称扩展网格的散点百分比和的阈值。
  111. for i in range(power_bin_count - 6): # 从最下层水平功率带1开始,向上到第PNum-6个水平功率带(额定功率一下水平功率带),逐一分析
  112. p_dot_dense_sum = p_box_max_p[i] # 以中心最大水平功率带为基准,向左向右对称扩展网格,累加各网格散点百分比
  113. i_spread_right = 1
  114. i_spread_left = 1
  115. while p_dot_dense_sum < dot_valve:
  116. if (p_box_max_index[i] + i_spread_right) < velocity_bin_count - 1:
  117. # 向右侧扩展
  118. p_dot_dense_sum = p_dot_dense_sum + power_box_percent[i, p_box_max_index[i] + i_spread_right]
  119. i_spread_right = i_spread_right + 1
  120. if (p_box_max_index[i] + i_spread_right) > velocity_bin_count - 1:
  121. break
  122. if (p_box_max_index[i] - i_spread_left) > 0:
  123. # 向左侧扩展
  124. p_dot_dense_sum = p_dot_dense_sum + power_box_percent[i, p_box_max_index[i] - i_spread_left]
  125. i_spread_left = i_spread_left + 1
  126. if (p_box_max_index[i] - i_spread_left) <= 0:
  127. break
  128. i_spread_right = i_spread_right - 1
  129. i_spread_left = i_spread_left - 1
  130. # 向左右对称扩展完毕
  131. dot_dense_left_right[i, 0] = i_spread_left
  132. dot_dense_left_right[i, 1] = i_spread_right
  133. main_band_right = np.median(dot_dense_left_right[:, 1])
  134. # 散点向右显著延展分布的水平功率带为限功率水平带
  135. # 各水平功率带是否为限功率标识,==1:是;==0:不是
  136. power_limit = np.zeros([power_bin_count, 1], dtype=int)
  137. width_average = 0 # 功率主带平均宽度
  138. # todo 限功率主带判别阈值为什么要加3
  139. power_limit_valve = np.ceil(main_band_right) + 3 # 限功率主带判别阈值
  140. n_counter = 0
  141. for i in range(power_bin_count - 6):
  142. # 如果向右扩展网格数大于阈值,且该水平功率带点总数>20,是限功率
  143. if dot_dense_left_right[i, 1] > power_limit_valve and power_bin_sum[i] > 20:
  144. power_limit[i] = 1
  145. if dot_dense_left_right[i, 1] <= power_limit_valve:
  146. # 统计正常水平功率带右侧宽度
  147. width_average = width_average + dot_dense_left_right[i, 1]
  148. n_counter = n_counter + 1
  149. width_average = width_average / n_counter # 功率主带平均宽度
  150. # 对限负荷水平功率带的最大网格较下面相邻层显著偏右,拉回
  151. for i in range(1, power_bin_count - 6):
  152. if power_limit[i] == 1 and abs(p_box_max_index[i] - p_box_max_index[i - 1]) > 5:
  153. p_box_max_index[i] = p_box_max_index[i - 1] + 1
  154. # 功率主带的右边界
  155. curve_width = int(np.ceil(width_average) + 2)
  156. # 数据异常需要剔除的网格标识,标识1:功率主带右侧的欠发网格;2:功率主带左侧的超发网格 3:额定功率以上的超发点
  157. b_box_remove = np.zeros([power_bin_count, velocity_bin_count], dtype=int)
  158. for m in range(power_bin_count - 6):
  159. for n in range(p_box_max_index[m] + curve_width, velocity_bin_count):
  160. b_box_remove[m, n] = 1
  161. for n in range(p_box_max_index[m] - curve_width, -1, -1):
  162. b_box_remove[m, n] = 2
  163. # 确定功率主带的左上拐点,即额定风速位置的网格索引
  164. curve_top = np.zeros(2, dtype=int)
  165. curve_top_valve = 3 # 网格的百分比阈值
  166. b_top_find = False
  167. for m in range(power_bin_count - 5, -1, -1):
  168. for n in range(velocity_bin_count):
  169. # 如左上角网格的百分比和散点个数大于阈值。
  170. if v_box_percent[m, n] > curve_top_valve and x_box_number[m, n] >= 10:
  171. curve_top[0] = m
  172. curve_top[1] = n
  173. b_top_find = True
  174. break
  175. if b_top_find:
  176. break
  177. isolate_valve = 3
  178. for m in range(power_bin_count - 6):
  179. for n in range(p_box_max_index[m] + curve_width, velocity_bin_count):
  180. if power_box_percent[m, n] < isolate_valve:
  181. b_box_remove[m, n] = 1
  182. # 功率主带顶部宽度
  183. curve_width_t = 2
  184. for m in range(power_bin_count - curve_width_t - 1, power_bin_count):
  185. for n in range(velocity_bin_count):
  186. b_box_remove[m, n] = 3 # 网格为额定功率以上的超发点
  187. # 功率主带拐点左侧的欠发网格标识
  188. for m in range(power_bin_count - 5 - 1, power_bin_count):
  189. for n in range(curve_top[1] - 1):
  190. b_box_remove[m, n] = 2
  191. # 以网格的标识,决定该网格内数据的标识。dzwind_and_power_sel。散点在哪个网格,此网格的标识即为该点的标识
  192. # -1:停机 0:好点 1:欠发功率点;2:超发功率点;3:额定风速以上的超发功率点 4: 限电
  193. dzwind_and_power_sel = np.zeros(power_gt_zero_array_count, dtype=int)
  194. n_which_p = -1
  195. n_which_v = -1
  196. for i in range(power_gt_zero_array_count):
  197. for m in range(power_bin_count):
  198. if m * 25 < power_gt_zero_array[i, 1] <= (m + 1) * 25:
  199. n_which_p = m
  200. break
  201. for n in range(velocity_bin_count):
  202. if (n * 0.25 + 0.125) < power_gt_zero_array[i, 0] <= ((n + 1) * 0.25 + 0.125):
  203. n_which_v = n
  204. break
  205. if n_which_p > -1 and n_which_v > -1:
  206. if b_box_remove[n_which_p, n_which_v] == 1:
  207. dzwind_and_power_sel[i] = 1
  208. if b_box_remove[n_which_p, n_which_v] == 2:
  209. dzwind_and_power_sel[i] = 2
  210. if b_box_remove[n_which_p, n_which_v] == 3:
  211. dzwind_and_power_sel[i] = 0 # 3 # 额定风速以上的超发功率点认为是正常点,不再标识。
  212. # 限负荷数据标识方法2:把数据切割为若干个窗口。对每一窗口,以第一个点为基准,连续nWindowLength个数据的功率在方差范围内,呈现显著水平分布的点
  213. n_window_length = 3
  214. limit_window = np.zeros(n_window_length, dtype=float)
  215. power_std = 15 # 功率波动方差
  216. n_window_num = int(np.floor(power_gt_zero_array_count / n_window_length))
  217. power_limit_up = self.rated_power - 300
  218. power_limit_low = 200
  219. for i in range(n_window_num):
  220. for j in range(n_window_length):
  221. limit_window[j] = power_gt_zero_array[i * n_window_length + j, 1]
  222. b_all_in_areas = 1
  223. for j in range(n_window_length):
  224. if limit_window[j] < power_limit_low or limit_window[j] > power_limit_up:
  225. b_all_in_areas = 0
  226. if b_all_in_areas == 0:
  227. continue
  228. up_limit = limit_window[0] + power_std
  229. low_limit = limit_window[0] - power_std
  230. b_all_in_up_low = 1
  231. for j in range(1, n_window_length):
  232. if limit_window[j] < low_limit or limit_window[j] > up_limit:
  233. b_all_in_up_low = 0
  234. if b_all_in_up_low == 1:
  235. for j in range(n_window_length):
  236. dzwind_and_power_sel[i * n_window_length + j] = 4 # 标识窗口内的数据为限负荷数据
  237. for i in range(power_bin_count - 6):
  238. pv_left_down = np.zeros(2, dtype=float)
  239. pv_right_up = np.zeros(2, dtype=float)
  240. if (p_box_max_index[i + 1] - p_box_max_index[i]) >= 1:
  241. pv_left_down[0] = (p_box_max_index[i] + curve_width) * 0.25 + 0.125
  242. pv_left_down[1] = i * 25
  243. pv_right_up[0] = (p_box_max_index[i + 1] + curve_width) * 0.25 + 0.125
  244. pv_right_up[1] = (i + 1) * 25
  245. for m in range(power_gt_zero_array_count):
  246. if pv_left_down[0] < power_gt_zero_array[m, 0] < pv_right_up[0] and \
  247. pv_left_down[1] < power_gt_zero_array[m, 1] < pv_right_up[1]: # 在该锯齿中
  248. if (power_gt_zero_array[m, 1] - pv_left_down[1]) / (
  249. power_gt_zero_array[m, 0] - pv_left_down[0]) > (
  250. pv_right_up[1] - pv_left_down[1]) / (
  251. pv_right_up[0] - pv_left_down[0]): # 斜率大于对角连线,则在锯齿左上三角形中,选中
  252. dzwind_and_power_sel[m] = 0
  253. self.df.loc[:, 'lab'] = -1
  254. self.df.loc[
  255. self.df[self.df[self.active_power] > 0].index, 'lab'] = dzwind_and_power_sel
  256. # 把部分欠发的优化为限电
  257. # 构建条件表达式
  258. cond1 = (self.df['lab'] == 1) & (
  259. (self.df[self.active_power] < self.rated_power * 0.75) &
  260. (self.df[self.pitch_angle_blade] > 0.5)
  261. )
  262. cond2 = (self.df['lab'] == 1) & (
  263. (self.df[self.active_power] < self.rated_power * 0.85) &
  264. (self.df[self.pitch_angle_blade] > 1.5)
  265. )
  266. cond3 = (self.df['lab'] == 1) & (
  267. (self.df[self.active_power] < self.rated_power * 0.9) &
  268. (self.df[self.pitch_angle_blade] > 2.5)
  269. )
  270. # 使用逻辑或操作符|合并条件
  271. combined_condition = cond1 | cond2 | cond3
  272. self.df.loc[combined_condition, 'lab'] = 4
  273. self.df.loc[self.df[self.active_power] <= 0, 'lab'] = -1
  274. self.df.reset_index(drop=True, inplace=True)
  275. if 'index' in self.df.columns:
  276. del self.df['index']
  277. return self.df
  278. def run(self):
  279. # Implement your class identification logic here
  280. begin = datetime.datetime.now()
  281. trans_print("打标签开始,风机号:", self.wind_turbine_number, self.df.shape)
  282. df = self.identifier()
  283. trans_print("打标签结束,", df.shape, ",耗时:", datetime.datetime.now() - begin)
  284. return df