import pandas as pd import configparser import numpy as np def calculate_yaw_error(filtered_data, wind_speed_range, wind_speed_step, wind_direct_step, cabin_temp_step): # 计算风向、机舱角度 分别和360取模运算 filtered_data['风向'] = np.abs((filtered_data['风向'] % 360)) filtered_data['机舱角度'] = np.abs((filtered_data['机舱角度'] % 360)) # 计算偏航误差 filtered_data['偏航误差'] = filtered_data['风向'] - filtered_data['机舱角度'] # 定义风速和舱内温度的分组范围 # wind_speed_bins = np.arange(wind_speed_range[0], wind_speed_range[1] + wind_speed_step, wind_speed_step) wind_speed_bins = np.arange(filtered_data['风速'].min( ), filtered_data['风速'].max() + wind_speed_step, wind_speed_step) wind_direct_bins = np.arange(filtered_data['风向'].min( ), filtered_data['风向'].max() + wind_direct_step, wind_direct_step) cabin_temp_bins = np.arange(filtered_data['舱内温度'].min( ), filtered_data['舱内温度'].max() + cabin_temp_step, cabin_temp_step) # 按风速和舱内温度分组 grouped = filtered_data.groupby([pd.cut(filtered_data['风速'], wind_speed_bins), pd.cut( filtered_data['风向'], wind_direct_bins), pd.cut(filtered_data['舱内温度'], cabin_temp_bins)]) # 从每个分组中找出有功功率最大的行 max_power_rows = grouped.apply(lambda x: x[x['有功功率'] == x['有功功率'].max()]) # 计算每个分组的偏航误差平均值 yaw_error_avg = grouped['偏航误差'].mean().dropna() return grouped, max_power_rows, yaw_error_avg def calculate_yaw_error2(data, wind_speed_step, wind_direct_step, cabin_temp_step): # 按风速和舱内温度分组 data['风向分组'] = np.floor(data['风向'] / wind_direct_step) * wind_direct_step data['风速分组'] = np.floor(data['风速'] / wind_speed_step) * wind_speed_step data['舱内温度分组'] = np.floor(data['舱内温度'] / cabin_temp_step) * cabin_temp_step # 计算每个分组的偏航误差平均值 grouped = data.groupby(['风向分组', '风速分组', '舱内温度分组']) # 从每个分组中找出有功功率最大的行 # grouped.apply(lambda x: x[x['有功功率'] == x['有功功率'].max()]) idx = grouped["有功功率"].idxmax() max_power_rows = data.loc[idx] yaw_error_avg = grouped['偏航误差'].mean().dropna() return grouped, max_power_rows, yaw_error_avg def calculate_angle_deviations(array1, array2): """ 计算两个相同长度角度数组中两两对应角度值的偏差。 结果限制在-90°到+90°之间，并保留两位小数。 参数: array1 (list): 第一个角度数组 array2 (list): 第二个角度数组 返回: list: 两两对应角度的偏差列表 """ deviations = [] for angle1, angle2 in zip(array1, array2): # 计算原始偏差 deviation = angle1 - angle2 # 调整偏差，使其位于-180°到+180°范围内 if deviation == 0.0: deviation = 0.0 else: deviation = (deviation + 180) % 360 - 180 # 将偏差限制在-90°到+90°范围内 if deviation > 90: deviation -= 180 elif deviation < -90: deviation += 180 # 保留两位小数 deviations.append(round(deviation, 2)) return deviations def recalculation(data): # 计算风向、机舱角度 分别和360取模运算 data['风向'] = np.abs(data['风向'] % 360) data['机舱角度'] = np.abs(data['机舱角度'] % 360) # 计算偏航误差 # data['偏航误差'] =np.where(data['风向'] < data['机舱角度'], # (data['风向'] - data['机舱角度'])%360, # -(data['风向'] - data['机舱角度']) ) data['偏航误差'] = calculate_angle_deviations( data['风向'], data['机舱角度']) return data def load_data(file_path, encoding='utf-8'): """ Filters data from a CSV file based on specified criteria. Parameters: file_path (str): Path to the CSV file. encoding (str): Character encoding of the CSV file. Default is 'utf-8'. Returns: pd.DataFrame: Filtered DataFrame. """ # Load the data data = pd.read_csv(file_path, encoding=encoding) return data def filter_data(data, wind_speed_range, active_power_range): # Filter criteria: # 1. "风机状态" (wind turbine status) must be 5 # 2. No empty (null) values in the row filtered_data = data[(data['风机状态'] == 5) & (data["风速"] >= wind_speed_range[0]) & (data["风速"] <= wind_speed_range[1]) & (data['有功功率'] > 20) & (data['有功功率'] >= active_power_range[0]) & (data['有功功率'] <= active_power_range[1])].dropna() filtered_data["时间"] = pd.to_datetime(filtered_data['时间']) """ # 将时间列转换为13位长整型（Unix时间戳） # Excel 13位长整型转为时间 公式： =TEXT((K2/1000+8*3600)/86400+70*365+19,"yyyy-mm-dd hh:mm:ss") filtered_data["时间Unix"] = filtered_data['时间'].astype('int64') // 10**6 """ # 将时间列转换为10位长整型（Unix时间戳） # Excel 10位长整型转为时间 公式： =TEXT((A1+8*3600)/86400+70*365+19,"yyyy-mm-dd hh:mm:ss") filtered_data["时间Unix"] = filtered_data['时间'].astype('int64') // 10**9 return filtered_data def read_config(config_file): """ Reads configuration settings from an INI file. Parameters: config_file (str): Path to the INI configuration file. Returns: dict: Configuration settings. """ config = configparser.ConfigParser() config.read(config_file) return config['DEFAULT'] # Example usage # Replace with your file path file_path = 'E:\WorkSpace\Resource\Manage\项目\大唐\风电机组功率曲线异常检测分析服务项目\收资\data_process\data_second_scada_test.csv' def main(): config_settings = read_config('config.ini') encoding = config_settings.get('Encoding', 'utf-8') wind_direct_step = float(config_settings.get('WindDirectStep', '1')) wind_speed_range = [float(x) for x in config_settings.get( 'WindSpeedRange', '3, 20').split(',')] wind_speed_step = float(config_settings.get('WindSpeedStep', '0.1')) cabin_temp_step = float(config_settings.get('CabinTempStep', '0.5')) active_power_range = [float(x) for x in config_settings.get( 'ActivePowerRange', '21, 1500').split(',')] raw_data = load_data(file_path, encoding) data = filter_data(raw_data, wind_speed_range, active_power_range) data=recalculation(data) # grouped,max_power_rows,yaw_error_avg = calculate_yaw_error(data, wind_speed_range,wind_speed_step, wind_direct_step,cabin_temp_step) grouped, max_power_rows, yaw_error_avg = calculate_yaw_error2( data, wind_speed_step, wind_direct_step, cabin_temp_step) print(" grouped by 风速、风向、舱内温度 ") # 将分组对象转换回 DataFrame grouped_df = grouped.apply(lambda x: x) # grouped_df=grouped.reset_index(drop=True) grouped_df.to_csv("./output/yaw_miss_grouped.csv", index=False, encoding="ansi") print(" max power rows ") print(max_power_rows) max_power_rows.to_csv("./output/yaw_miss_max_power_rows.csv", index=False, encoding="ansi") print(" yaw error avg dataframe") print(yaw_error_avg) print(" yaw miss avg: " ) # print(data.head()) print (data["偏航误差"].mean()) if __name__ == "__main__": main()