利用CNN网络输电线路故障诊断(Python代码，压缩包带有数据集和代码，解压缩可直接运行)

 1.数据集介绍 

将故障区分为具体的不同类型：单相短路故障、两相接地短路故障、两相相间故障、三相相间短路故障。这里随意举出每种类别的两个样本进行展示。

G	C	B	A	Ia	Ib	Ic	Va	Vb	Vc
1	0	0	1	-151.2918124	-9.677451563	85.80016226	0.400749853	-0.132934945	-0.267814907
1	0	0	1	-336.1861826	-76.28326195	18.32889658	0.312731934	-0.123633156	-0.189098779

1	0	1	1	-343.4870147	104.5627513	3.794285309	0.272042501	0.011317575	-0.283360076
1	0	1	1	-339.1254001	105.4293167	-0.267241225	0.27782054	0.021756839	-0.299577378

0	1	1	0	19.38615173	-785.553797	768.7279081	-0.210406869	-0.0020112	0.212418069
0	1	1	0	18.47841651	-783.8619173	767.9410527	-0.217651204	-0.00260451	0.220255714

0	1	1	1	506.5917463	374.8825788	-879.344997	0.042029705	-0.025636401	-0.016393305
0	1	1	1	495.1384715	387.4159615	-880.4253096	0.042107683	-0.025103056	-0.017004627

2.模型：CNN网络，每类故障有1000个样本

3.效果（平均识别准确率为 98.053%）

混淆矩阵

 

 

 F1-score

4.代码

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

sns.set_style('darkgrid')
plt.rcParams['figure.figsize'] = (13, 9)
plt.rcParams['font.size'] = 20
warnings.filterwarnings('ignore')
plt.rcParams['font.family'] = 'SimHei'  # 设置字体为黑体
#代码关注：https://mbd.pub/o/bread/ZJuXmZpp