分布式电源对配电网故障定位的影响（Python代码实现）

import numpy as np
import pandas as pd
from scipy.optimize import linear_sum_assignment
 
# 示例数据
data = {
    'bus': ['Bus1', 'Bus2', 'Bus3', 'Bus4', 'Bus5'],
    'Pd_mean': [100, 150, 120, 130, 140],
    'Qd_mean': [50, 60, -10, 70, -30]
}
df = pd.DataFrame(data)
df['Pd_mean'] = df['Pd_mean'].astype(float)
df['Qd_mean'] = df['Qd_mean'].astype(float)
 
# 计算电压偏差
df['delta_V'] = np.sqrt(df['Pd_mean']**2 + df['Qd_mean']**2)
 
# 计算电压偏差排序
df['rank_V'] = df['delta_V'].rank(method='min', ascending=False).astype(int)
 
# 构造电压偏差-电网开关数量的成本矩阵
cost_matrix = np.zeros((df.shape[0], df.shape[0]))
for i in range(df.shape[0]):
    for j in range(df.shape[0]):
        if df.iloc[i]['rank_V'] < df.iloc[j]['rank_V']:
            cost_matrix[i, j] = 1
 
# 使用Kuhn-Munkres算法求解成本矩阵的最优匹配
from scipy.optimize import linear_sum_assignment
row_ind, col_ind = linear_sum_assignment(cost_matrix)
 
# 输出最优匹配结果
print(f"最优匹配数量: {len(row_ind)}")
for i in range(len(row_ind)):
    print(f"bus {df.iloc[row_ind[i]]['bus']} 切换至 bus {df.iloc[col_ind[i]]['bus']}")

这段代码首先根据每个电网节点的Pd\_mean和Qd\_mean计算电压偏差，然后根据电压偏差进行排序，并构建成本矩阵。接着使用KM算法求解成本矩阵的最优匹配，最后输出最优匹配结果。这个过程可以帮助分析在配电网络中发生故障时，通过切换哪些节点可以最大程度上减少电网中的电压不平衡。