Monitoring Method for Capacitor Aging Indication Parameters in Energy Storage Converters Based on Adaptive Neuro-Fuzzy Inference and Feedback Data-Driven Approaches
Tian Yanjun, Zhai Haizhi, Qu Zhangshun, Liu Qing
Hebei Key Laboratory of Distributed Energy Storage and Micro-Grid School of Electrical and Electronic Engineering North China Electric Power University Baoding 071003 China
Abstract：Capacitors are critical components in power conversion systems (PCS) for energy storage, and their lifespan and reliability are key factors that limit the overall performance and safe operation of the converter. Electrolyte drying and dielectric aging, influenced by factors such as temperature and ripple current, lead to a decrease in capacitance and an increase in equivalent series resistance (ESR). Accurate online monitoring of these key state parameters is a prerequisite for aging assessment and life prediction. Traditional offline methods require disassembly and cannot reflect real operating conditions. Existing online methods, such as recursive least squares (RLS) or model-based observers, suffer from model dependence and noise sensitivity. Pure neural network approaches lack interpretability. This paper proposes a dual-stage method that integrates an adaptive neuro-fuzzy inference system (ANFIS) for parameter identification and a data-driven voltage-error feedback mechanism for validation and refinement. Firstly, based on the physical structure and application scenario, a simplified capacitor equivalent model, consisting of a series connection of capacitance (C) and ESR (R), is established. The dynamic voltage-current relationship forms the basis for parameter identification. Secondly, an ANFIS model is constructed for online identification. A hybrid learning algorithm combining least squares estimation (LSE) for consequent parameters and gradient descent for premise parameters is employed. The physical parameters (C and R) are extracted by analyzing the network output's gradient with respect to the input current. Thirdly, a novel data-driven evaluation mechanism based on voltage-differential-error propagation is introduced. The parameters identified by ANFIS are used to reconstruct the capacitor voltage. A differential-error model is derived to establish a direct mapping between parameter errors and voltage differential errors. By substituting multiple data points into this model, a set of lines is generated in the parameter error plane. The density distribution of their intersections indicates the most probable parameter errors, enabling quantitative assessment and refinement. Finally, simulation models in Matlab/Simulink and a 2 kW experimental platform are built. Gaussian white noise is added to simulate measurement inaccuracies, and a Kalman filter is used for signal pre-processing. Simulation results under ideal conditions demonstrate high accuracy, with ANFIS identification errors of 0.87% for capacitance and 0.83% for ESR. Under simulated noisy conditions (1% voltage noise, 10% current noise), the identification errors increase but remain within acceptable limits. The intersection density plot becomes more dispersed, reflecting the increased uncertainties, yet still indicates the most probable error region. The evaluation offsets for capacitance and ESR are within 5% and 25%, respectively, demonstrating robustness. Experimental results show that the ANFIS method identifies parameters that are close to those obtained from offline LCR bridge measurements. The subsequent error evaluation provides predicted ranges for the actual online parameters and the corresponding loss tangent (tanδ). The evaluated tanδ range includes the offline- measured value, confirming the predictive capability. The proposed ANFIS-based approach achieved higher identification accuracy than the RLS method. The following conclusions can be drawn. (1) The ANFIS-based identification model successfully integrates the interpretability of fuzzy systems with the learning ability of neural networks for high-precision online identification of capacitor parameters. (2) The voltage differential error propagation mechanism addresses the challenge of lacking a direct online validation benchmark for quantitative assessment of parameter errors. (3) By retaining physical meaning, the method overcomes the “black-box” limitation of pure neural networks, enhancing engineering applicability.
田艳军, 翟海智, 屈章顺, 刘青. 基于自适应神经模糊推理与反馈数据驱动储能变流器电容老化指征参数监测方法[J]. 电工技术学报, 2026, 41(14): 4960-4973.
Tian Yanjun, Zhai Haizhi, Qu Zhangshun, Liu Qing. Monitoring Method for Capacitor Aging Indication Parameters in Energy Storage Converters Based on Adaptive Neuro-Fuzzy Inference and Feedback Data-Driven Approaches. Transactions of China Electrotechnical Society, 2026, 41(14): 4960-4973.
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