融合随机森林和神经网络的电能质量分析算法
Power Quality Analysis Algorithm Based on Random Forest and Neural Network
郑曼 1周炫羽 1王钢 1程书绚1
作者信息
- 1. 湖北中烟卷烟材料厂,湖北 武汉 430000
- 折叠
摘要
提出了一种融合随机森林(RF)和神经网络(NN)的电能质量分析算法.首先利用 RF对电能质量信号进行特征提取和降维,然后利用 NN对提取的特征进行分类和识别,最后通过实验验证了该算法的有效性,并与其他常用的电能质量分析方法进行了比较.实验结果表明,该算法具有较高的准确率、召回率和 F1 值,以及较快的运行速度和较低的计算复杂度.
Abstract
A power quality analysis algorithm based on random forest(RF)and neural network(NN)is proposed.First,RF is used for feature extraction and dimensionality reduction of power quality sig-nals,and then NN is used to classify and identify the extracted features.Finally,the effectiveness of the proposed algorithm is verified by experiment and compared with other commonly used power quality analysis methods.The experimental results show that the proposed algorithm has higher accuracy,re-call rate and F1 value.Meanwhile,the proposed algorithm has faster running speed and lower computa-tional complexity.
关键词
随机森林/神经网络/电能质量/扰动分析Key words
Random forest/Neural network/Power quality/Disturbance analysis引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据