摘要
针对变压器热点温度预测精度问题,提出一种蚁群算法(ant colony algorithm,ACO)结合改进主成分分析法(improved principal component analysis,IPCA)优化BP神经网络的热点温度预测模型。首先采用IPCA去除数据冗余信息,并解决参数间相关性问题,提高网络泛化能力。为了避免BP神经网络容易陷入局部最优和收敛速度慢,利用ACO优化网络权值和与阈值,加快算法速率,提高预测精度。通过变压器温度实测数据验证,预测结果中的mae、mse、mape指标分别为0.0657、0.0067、0.44%,预测精度和网络性能优于IEEE、BP、IPCA-BP模型,从而验证所提模型的有效性和可行性。
Aiming at the prediction accuracy of transformer hot spot temperature,the ant colony algorithm(ACO)combined with improved principal component analysis(IPCA)was proposed to optimize BP neural network model to predict hot spot temperature.Firstly,IPCA is used to remove data redundancy information and solve the correlation between parameters to improve the ability of network generalization.In order to avoid BP neural network that is easily falling into local optimum and slow convergence speed,ACO was used to optimize the weights and thresholds of the network to speed up the algorithm and improve the prediction accuracy.Verified by the measured transformer temperature data,the mae,mse and mape indexes in the predicted results are 0.0657,0.0067 and 0.44%,respectively.The prediction accuracy and network performance are better than those of IEEE,BP and IPCA-BP models,thus verifying the validity and feasibility of the proposed model.
作者
江兵
杨春
杨雨亭
巢一帆
Jiang Bing;Yang Chun;Yang Yuting;Chao Yifan(College of Automation,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2022年第10期235-242,共8页
Journal of Electronic Measurement and Instrumentation
作者简介
通信作者:江兵,2007年于中国科学技术大学获博士学位,现为南京邮电大学副教授,主要研究方向为智能仪器与测控系统。E-mail:jiangb@njupt.edu.cn
