cloud
149 lines
5.2 KiB
Python
149 lines
5.2 KiB
Python
import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from pathlib import Path
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# ----------------------
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# 配置参数
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# ----------------------
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RAW_DATA_PATH = Path(r'H:\尽调项目\和展清云洛阳项目\6#\6 处理后.xlsx')
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RESULT_TABLE_PATH = Path(r'H:\尽调项目\和展清云洛阳项目\6#\6风速区间功率均值表.xlsx')
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RESULT_PLOT_PATH = Path(r'H:\尽调项目\和展清云洛阳项目\6#\6风速功率曲线.png')
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RATED_POWER = 6700 # 额定功率
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RATED_WIND_SPEED = 18 # 额定风速
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POWER_STEP = 5
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WIND_SPEED_STEP = 0.5 # 风速区间步长
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WIND_SPEED_CHANGE_THRESHOLD = 1
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#TIME_THRESHOLD = pd.Timedelta(hours=2)
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IQR_MULTIPLIER = 1.5
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MIN_DATA_COUNT = 3
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def load_data(file_path):
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try:
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if not file_path.exists():
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raise FileNotFoundError(f"文件不存在: {file_path}")
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data = pd.read_excel(file_path, engine='openpyxl')
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print(f"成功读取数据，共{len(data)}条记录")
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return data
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except Exception as e:
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print(f"数据读取错误: {str(e)}")
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return None
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def clean_scada_data(raw_data):
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if raw_data is None or raw_data.empty:
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print("无数据可清洗")
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return None
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data = raw_data.copy()
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# ----------------------
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# 区间功率均值计算与可视化（核心修改部分）
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# ----------------------
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def calculate_interval_power_mean(raw_data, wind_speed_step=0.5):
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"""按风速区间计算功率平均值，区间采用左开右闭形式 (start, end]"""
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if raw_data is None or raw_data.empty:
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print("无清洗后的数据可计算均值")
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return None
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# 确定风速区间范围
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wind_min = 1 - wind_speed_step*0.5
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wind_max = 25 + wind_speed_step*0.5
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# 生成区间起点
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wind_intervals = np.arange(wind_min, wind_max, wind_speed_step)
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# 计算每个区间的平均功率（核心修改：使用左开右闭区间 (start, end]）
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result = []
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for interval_start in wind_intervals:
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interval_end = interval_start + wind_speed_step
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# 关键修改：左边界>，右边界≤
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mask = (raw_data['平均风速'] > interval_start) & (raw_data['平均风速'] <= interval_end)
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interval_data = raw_data[mask]
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if not interval_data.empty:
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wind_mid = (interval_start + interval_end) / 2 # 区间中点
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power_mean = interval_data['平均有功功率'].mean()
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result.append({
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'风速区间起点': interval_start,
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'风速区间终点': interval_end,
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'风速': wind_mid,
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'实际功率': power_mean,
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'区间数据量': len(interval_data),
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})
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# 转换为DataFrame并按风速排序
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result_df = pd.DataFrame(result).sort_values('风速').reset_index(drop=True)
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print(f"已计算{len(result_df)}个风速区间的功率平均值（左开右闭区间）")
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return result_df
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def plot_power_curve(result_df, rated_power, rated_wind_speed, save_path):
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"""绘制风速-功率曲线并保存图片"""
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if result_df is None or result_df.empty:
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print("无数据可绘制曲线")
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return False
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plt.style.use('seaborn-v0_8-talk')
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fig, ax = plt.subplots(figsize=(12, 6))
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# 绘制功率曲线
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ax.plot(result_df['风速'], result_df['实际功率'],
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color='#2c7fb8', linewidth=2.5, marker='o', markersize=5,
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label='实际功率曲线')
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# 绘制额定功率参考线
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ax.axhline(y=rated_power, color='#e41a1c', linestyle='--', linewidth=1.5,
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label=f'额定功率 ({rated_power}kW)')
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# 绘制额定风速参考线
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ax.axvline(x=rated_wind_speed, color='#4daf4a', linestyle='-.', linewidth=1.5,
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label=f'额定风速 ({rated_wind_speed}m/s)')
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# 设置坐标轴标签和标题
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ax.set_xlabel('风速 (m/s)', fontsize=12)
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ax.set_ylabel('功率 (kW)', fontsize=12)
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ax.set_title('风速-功率曲线（左开右闭区间平均值）', fontsize=14, pad=20)
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# 添加网格和图例
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ax.grid(alpha=0.3)
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ax.legend(fontsize=10)
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# 调整布局并保存
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plt.tight_layout()
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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plt.close()
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print(f"功率曲线已保存至 {save_path}")
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return True
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# ----------------------
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# 主程序执行
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# ----------------------
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if __name__ == "__main__":
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# 1. 读取并清洗数据
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raw_data = load_data(RAW_DATA_PATH)
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# 2. 计算风速区间功率平均值（左开右闭区间）
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interval_power_df = calculate_interval_power_mean(raw_data, wind_speed_step=WIND_SPEED_STEP)
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# 3. 保存结果表格
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if interval_power_df is not None:
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try:
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RESULT_TABLE_PATH.parent.mkdir(parents=True, exist_ok=True)
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interval_power_df.to_excel(RESULT_TABLE_PATH, index=False)
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print(f"风速区间功率均值表已保存至 {RESULT_TABLE_PATH}")
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except Exception as e:
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print(f"保存表格失败：{str(e)}")
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# 4. 绘制并保存功率曲线图片
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if interval_power_df is not None:
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plot_power_curve(interval_power_df, RATED_POWER, RATED_WIND_SPEED, RESULT_PLOT_PATH)
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