电力系统保护与控制2026，Vol.54Issue(6)：34-44,11.DOI:10.19783/j.cnki.pspc.250681
基于贝叶斯神经网络与H5N1优化算法的电流互感器J-A模型磁滞参数高效识别
Efficient identification of J-A model hysteresis parameters for current transformers based on Bayesian neural network and H5N1 optimization algorithm
摘要
Abstract
Accurate identification of hysteresis parameters in the Jiles-Atherton(J-A)model of current transformers is crucial for power system measurement and protection.However,in practice,problems such as measurement noise and insufficient data acquisition often degrade parameter identification accuracy.To address these challenges,a J-A model hysteresis parameter identification strategy for current transformers based on a Bayesian neural network(BNN)and the H5N1 optimization algorithm is proposed.BNN is used for data preprocessing,including denoising and prediction,thereby improving data quality.The H5N1 optimization algorithm is used for identifying hysteresis parameters of the J-A model.Meanwhile,multiple metaheuristic algorithms are selected for comparative validation.Simulation results show that the combination of BNN based data preprocessing and H5N1 optimization algorithm can significantly improve the accuracy and stability of hysteresis parameter identification compared to approaches without data preprocessing,providing a more efficient and accurate method for parameter identification of current transformer J-A model.For example,under denoising data,the identification accuracy increases by 22.90%,with an error of 1.2386;under predicted data conditions,the accuracy is improved by 89.33%,with an error of 0.7267.关键词
电流互感器/J-A模型/H5N1优化算法/贝叶斯神经网络/参数识别Key words
current transformer/J-A model/H5N1 optimization algorithm/Bayesian neural network/parameter identification引用本文复制引用
张鹏,张敏,黄伟,阮璇,龚新勇,杨鹏杰,杨博..基于贝叶斯神经网络与H5N1优化算法的电流互感器J-A模型磁滞参数高效识别[J].电力系统保护与控制,2026,54(6):34-44,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.62263014). 国家自然科学基金项目资助(62263014) （No.62263014）
云南电网有限责任公司科技项目资助(YNKJXM20240333)"基于图形化的电网事故重构推演关键技术研究及应用" （YNKJXM20240333）
