基于改進高斯過程回歸的變電站直流蓄電池SOH估算
丁芃，謝昊含，司威，楊茹楠，劉明陽
(國網(wǎng)天津市電力公司濱海供電分公司，天津 300450)
摘 要 ：為了準確估算變電站直流蓄電池的健康狀態(tài)(SOH)，輔助直流系統(tǒng)的運行決策，提出了一種基于改進高斯過程回歸的蓄電池SOH估算方法，通過建立變電站蓄電池組在實際不同運行工況下的蓄電池健康特征指標(biāo)(HF)，對高斯過程回歸算法進行適應(yīng)性改進，將變電站蓄電池實際歷史運行數(shù)據(jù)與離線測試數(shù)據(jù)按比例混合制作訓(xùn)練集，實現(xiàn)變電站蓄電池HF與SOH之間的映射關(guān)系。實驗結(jié)果表明，該方法針對于變電站這一特殊場景下的蓄電池具有良好的估算效果，可為直流系統(tǒng)運行維護提供理論依據(jù)。
關(guān)鍵詞 ： 變電站 ；直流蓄電池 ；蓄電池健康狀態(tài) ；蓄電池運行工況 ；高斯過程回歸 ；訓(xùn)練集
中圖分類號 ：TM63 ；TM912 文獻標(biāo)識碼 ：A 文章編號 ：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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