随机森林算法在电能质量自动监测中的应用研究

Application of random forest algorithm in power quality automatic monitoring

社会的进步带动电力发展,随之出现的是各种电能质量问题,而现代科技能够实现对电能质量的实时监测。针对当今社会生产活动中易出现的各种电能质量问题,提出一种基于随机森林算法的电能质量自动监测模型。该模型能够实现电能质量的实时自动监测。对提出的随机森林算法利用贝叶斯算法进行参数优化以提高整体模型的分类准确率。最后经实验验证分析,优化后的随机森林模型对比传统模型在分类准确率方面确实有所提升,构建的电能质量监测模型具有较好的应用前景。

Social progress drives the development of electric power,with which various power quality problems appear.Modern science and technology can realize real-time monitoring of power quality.In view of various power quality problems that are easy to occur in today's social production activities,an automatic power quality monitoring model is proposesed based on stochastic forest algorithm.The model can realize real-time automatic monitoring of power quality.The paper also uses Bayesian algorithm to optimize the parameters of the proposed random forest algorithm to improve the classification accuracy of the overall model.Finally,through experimental verification and analysis,the classification accuracy of the optimized random forest model is indeed improved compared with the traditional model,and the constructed power quality monitoring model has a good application prospect.