基于改进高斯过程回归的变电站直流蓄电池SOH估算
丁芃，谢昊含，司威，杨茹楠，刘明阳
(国网天津市电力公司滨海供电分公司，天津 300450)
摘 要 ：为了准确估算变电站直流蓄电池的健康状态(SOH)，辅助直流系统的运行决策，提出了一种基于改进高斯过程回归的蓄电池SOH估算方法，通过建立变电站蓄电池组在实际不同运行工况下的蓄电池健康特征指标(HF)，对高斯过程回归算法进行适应性改进，将变电站蓄电池实际历史运行数据与离线测试数据按比例混合制作训练集，实现变电站蓄电池HF与SOH之间的映射关系。实验结果表明，该方法针对于变电站这一特殊场景下的蓄电池具有良好的估算效果，可为直流系统运行维护提供理论依据。
关键词 ： 变电站 ；直流蓄电池 ；蓄电池健康状态 ；蓄电池运行工况 ；高斯过程回归 ；训练集
中图分类号 ：TM63 ；TM912 文献标识码 ：A 文章编号 ：1007-3175(2025)11-0014-07
SOH Estimation for DC Batteries in Substations Based on Improved Gaussian Process Regression
DING Peng, XIE Hao-han, SI Wei, YANG Ru-nan, LIU Ming-yang
（State Grid Tianjin Electric Power Company Binhai Power Supply Branch, Tianjin 300450, China）
Abstract: In order to accurately estimate the state of health (SOH) of DC batteries in substations and assist in the operation decision-making of DC systems, this paper proposes a battery SOH estimation method based on improved Gaussian process regression. By establishing the health of feature (HF) of battery packs in substations under different operating conditions, the Gaussian process regression algorithm is adaptively improved. The actual historical operating data of substation batteries is mixed with offline test data in proportion to create a training set, achieving the mapping relationship between HF and SOH of substation batteries. The experimental results show that this method has good estimation effect on batteries in this special scenario of substations and can provide theoretical basis for the operation and maintenance of DC systems.
Key words: substation; DC battery; state of health of battery; operating condition of battery; Gaussian process regression; training set
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