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+#!/usr/bin/env python
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+# -*- coding: utf-8 -*-
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+# time: 2024/6/21 13:49
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+# file: turbine_cleaning.py
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+# author: David
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+# company: shenyang JY
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+import os
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+import pandas as pd
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+from datetime import timedelta
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+
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+
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+# ——————————————————————————机头风速-99和连续异常值清洗代码——————————————————————————————
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+def mark_abnormal_streaks(df, columns, min_streak):
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+ abnormal_mask = pd.Series(False, index=df.index)
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+ streak_start = None
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+ for i in range(len(df)):
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+ if i == 0 or any(df.at[i - 1, col] != df.at[i, col] for col in columns):
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+ streak_start = i
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+
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+ if i - streak_start >= min_streak - 1:
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+ abnormal_mask[i - min_streak + 1:i + 1] = True
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+
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+ return abnormal_mask
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+
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+
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+def remove_abnormal_values(df, N):
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+ # 标记C_ACTIVE_POWER为-99的行为异常值
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+ abnormal_mask1 = df['C_ACTIVE_POWER'] == -99
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+ count_abnormal1 = abnormal_mask1.sum()
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+
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+ # 标记C_WS, A, B连续5行不变的行为异常值
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+ columns = ['C_WS', 'C_WD', 'C_ACTIVE_POWER']
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+ abnormal_mask2 = mark_abnormal_streaks(df, columns, N)
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+ count_abnormal2 = abnormal_mask2.sum()
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+ # 获得所有异常值的布尔掩码
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+ abnormal_mask = abnormal_mask1 | abnormal_mask2
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+ # 获取连续异常值具体数值
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+ removed_continuous_values = {column: df.loc[abnormal_mask2, column].unique() for column in columns}
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+ # 剔除异常值
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+ df_clean = df[~abnormal_mask]
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+ total_removed = abnormal_mask.sum()
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+ return df_clean, count_abnormal1, count_abnormal2, total_removed, removed_continuous_values
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+
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+
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+def process_csv_files(input_dir, output_dir, turbines_id, M,N): # MBD:没有考虑时间重复
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+ if not os.path.exists(output_dir):
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+ os.makedirs(output_dir)
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+
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+ for i in turbines_id:
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+ input_file = os.path.join(input_dir, f"turbine-{i}.csv")
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+ output_file = os.path.join(output_dir, f"turbine-{i}.csv")
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+
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+ # 读取csv文件
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+ df = pd.read_csv(input_file)
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+
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+ # 剔除异常值，并获取异常值统计信息
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+ df_clean, count_abnormal1, count_abnormal2, total_removed, removed_continuous_values = remove_abnormal_values(df,N)
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+
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+ # 输出异常值统计信息
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+ print(f"处理文件：{input_file}")
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+ print(f"剔除 -99 点异常值数量：{count_abnormal1}")
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+ print(f"剔除连续异常值数量：{count_abnormal2}")
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+ print(f"总共剔除数据量：{total_removed}")
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+ print(f"剔除的连续异常值具体数值：{removed_continuous_values}\n")
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+
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+ # 保存处理过的CSV文件
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+ df_clean.to_csv(output_file, index=False)
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+
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+
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+# ——————————————————————————风机单机时间对齐——————————————————————————————
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+def TimeMerge(input_dir, output_dir, turbines_id, M):
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+ # 读取所有CSV文件
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+ files = [os.path.join(input_dir, f"turbine-{i}.csv") for i in turbines_id]
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+ dataframes = [pd.read_csv(f) for f in files]
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+
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+ # 获取C_TIME列的交集
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+ c_time_intersection = set(dataframes[0]["C_TIME"])
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+ for df in dataframes[1:]:
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+ c_time_intersection.intersection_update(df["C_TIME"])
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+
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+ # 只保留C_TIME交集中的数据
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+ filtered_dataframes = [df[df["C_TIME"].isin(c_time_intersection)] for df in dataframes]
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+
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+ # 将每个过滤后的DataFrame写入新的CSV文件
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+ os.makedirs(output_dir, exist_ok=True)
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+ turbines_all, names = [], ['C_TIME']
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+ for (filtered_df, i) in zip(filtered_dataframes, turbines_id):
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+ # if i == 144:
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+ # filtered_df['C_ACTIVE_POWER'] /= 1000
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+ filtered_df.to_csv(os.path.join(output_dir, f"turbine-{i}.csv"), index=False)
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+ names.append('C_ACTIVE_POWER_{}'.format(i))
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+ turbines_all.append(filtered_df['C_ACTIVE_POWER'].reset_index(drop=True))
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+ turbines_all.insert(0, filtered_dataframes[0]['C_TIME'].reset_index(drop=True))
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+ turbines_all = pd.concat(turbines_all, axis=1)
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+ turbines_all.columns = names
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+ turbines_all.to_csv(os.path.join(output_dir, f"turbines.csv"), index=False)
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+
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+
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+# ——————————————————————————风机缺失点处理——————————————————————————————
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+def MissingPointProcessing(input_dir,output_dir, turbines_id, M,N):
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+
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+ # 存储数据的列表
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+
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+ # 读取M个文件
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+ for k in turbines_id:
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+ file_name = input_dir + '/' + f"turbine-{k}.csv"
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+ # file_name = os.path.join(input_dir, f"turbine-{k}.csv")
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+ # 读取CSV文件
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+ data = pd.read_csv(file_name, parse_dates=['C_TIME'])
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+
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+ # 计算时间差
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+ data['time_diff'] = data['C_TIME'].diff().dt.total_seconds()
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+
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+ # 找出缺失的时间点
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+ missing_data_points = data[data['time_diff'] > 900]
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+
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+ # 存储填充的时间和值
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+ filled_data = []
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+
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+ # 输出缺失的开始时刻和数量
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+ print("缺失的开始时刻:")
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+ for index, row in missing_data_points.iterrows():
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+ missing_start = row['C_TIME'] - timedelta(seconds=row['time_diff'])
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+ missing_count = int(row['time_diff'] // 900) - 1
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+
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+ # 如果缺失的点数小于N个，则进行填充 MBD:填充代码比较啰嗦
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+ if missing_count <= N:
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+ prev_values = data.iloc[index - 1][['C_WS', 'C_WD', 'C_ACTIVE_POWER']]
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+ next_values = row[['C_WS', 'C_WD', 'C_ACTIVE_POWER']]
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+
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+ for i in range(1, missing_count + 1):
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+ t = i / (missing_count + 1)
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+ filled_time = missing_start + timedelta(minutes=15 * i)
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+
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+ filled_values = {
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+ 'C_TIME': filled_time,
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+ 'C_WS': prev_values['C_WS'] + (next_values['C_WS'] - prev_values['C_WS']) * t,
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+ 'C_WD': prev_values['C_WD']+(next_values['C_WD']-prev_values['C_WD'])*t,
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+ 'C_ACTIVE_POWER': prev_values['C_ACTIVE_POWER'] + (
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+ next_values['C_ACTIVE_POWER'] - prev_values['C_ACTIVE_POWER']) * t,
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+ }
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+
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+ # 将角度值限制在-180到180的范围内
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+ filled_values['C_WD'] = (filled_values['C_WD'] + 180) % 360 - 180
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+
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+ filled_data.append(filled_values)
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+ print(f"填充的时间: {filled_time}, 填充的值: {filled_values}")
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+
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+ print(f"{missing_start} - 缺失的点的数量: {missing_count}")
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+
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+ # 将填充的数据插入原始数据中
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+ filled_df = pd.DataFrame(filled_data)
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+ data = pd.concat([data, filled_df], ignore_index=True)
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+ # 对数据按时间排序并重置索引
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+ data = data.sort_values(by='C_TIME').reset_index(drop=True)
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+
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+ # 输出总缺失点数
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+ missing_data_points = data[data['time_diff'] > 900]
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+ print(f"总缺失点数: {int(missing_data_points['time_diff'].sum() // 900) - len(missing_data_points)}")
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+ data.drop(columns=['time_diff'], inplace=True)
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+ os.makedirs(output_dir, exist_ok=True)
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+ output_path_name = os.path.join(output_dir, f"turbine-{k}.csv")
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+ print(output_path_name)
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+ # 保存插值后的文件
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+ data.to_csv(output_path_name, index=False)
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liudawei
9 months ago