| 引用本文: | 张 鹏,张 敏,黄 伟,等.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(06):34-44. |
| ZHANG Peng,ZHANG Min,HUANG Wei,et al.Efficient identification of J-A model hysteresis parameters for current transformers based on Bayesian neural network and H5N1 optimization algorithm[J].Power System Protection and Control,2026,54(06):34-44 |
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| 摘要: |
| 电流互感器Jiles-Atherton模型磁滞参数的精准识别对电力系统测量与保护至关重要，但实际中测量噪声、数据采集不充分等问题会降低参数识别精度。为此，提出一种基于贝叶斯神经网络(Bayesian neural network, BNN)与H5N1优化算法的电流互感器J-A模型磁滞参数识别策略。BNN用于数据预处理，包括数据去噪与预测，以此提升数据质量。H5N1优化算法则用于J-A模型磁滞参数的识别。同时，选取其他多种元启发式算法进行对比验证。仿真结果表明，基于BNN的数据预处理结合H5N1优化算法，相较于未进行数据预处理的情况，能显著提高磁滞参数识别的精度与稳定性，为电流互感器J-A模型参数识别提供了更高效准确的方法。例如，在去噪数据下，准确率提高了22.90%，误差为1.2386；在预测数据下，准确性提高了89.33%，误差为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 |
| DOI：10.19783/j.cnki.pspc.250681 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(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” |
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| Efficient identification of J-A model hysteresis parameters for current transformers based on Bayesian neural network and H5N1 optimization algorithm |
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ZHANG Peng1,ZHANG Min2,HUANG Wei1,RUAN Xuan1,GONG Xinyong1,YANG Pengjie1,YANG Bo3
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1. Kunming Power Supply Bureau, Yunnan Power Grid Co., Ltd., Kunming 650000, China; 2. Yunnan Power Dispatching
and Control Center, Kunming 650000, China; 3. Faculty of Electric Power Engineering, Kunming University of
Science and Technology, Kunming 650000, China
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| 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. |
| Key words: current transformer J-A model H5N1 optimization algorithm Bayesian neural network parameter identification |
