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docs/清洗完后直接生成功率曲线.py

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