IGBT Saturation Voltage Drop Monitoring Technology Based on Kalman Filter Data Optimization and Nonlinear Least Squares Method
Tian Yanjun1, Song Shaopeng1, Xu Xiaoqi1, Li Zhen2
1. Hebei Key Laboratory of Distributed Energy Storage and Micro-Grid School of Electrical and Electronic Engineering North China Electric Power University Baoding 071003 China; 2. State Grid Integrated Energy Service Group Company Beijing 100052 China
Abstract：Power semiconductor devices are core components in power conversion systems (PCS), and their operational state directly affects system safety and reliability. These devices are prone to aging, which leads to increased system losses, elevated temperatures, and potentially shutdowns or equipment damage. Therefore, it is essential to develop condition monitoring methods for accurate health assessment. The collector-emitter saturation voltage Vce of the insulated gate bipolar transistor (IGBT) is a key parameter reflecting device degradation, serving as an effective indicator for non-intrusive health monitoring. However, data-driven modeling methods are highly dependent on data quality and quantity. Insufficient data quality can result in significant model deviation. Therefore, this paper proposes a nonlinear least squares fitting algorithm combined with data preprocessing optimization. The algorithm utilizes sampled data from the closed-loop control system of practical energy storage converters to enable non-intrusive monitoring of the IGBT saturation voltage. First, a discretized mathematical model of the PCS is established. Based on the main circuit modeling, the effects of IGBT dead time and control system delay are considered, and the fourth-order Runge-Kutta method is employed for discretization to improve accuracy during operation. The proposed algorithm achieves accurate identification of IGBT saturation voltage. Second, a Kalman filter observer is introduced to preprocess the sampled data, improving the robustness of the monitoring system by suppressing noise and sampling deviations. Additionally, due to transient conditions and grid imbalance during PCS operation, identification errors in Vce may arise. A normal distribution model of the identification results is constructed to extract statistical features such as mean and variance. These features, combined with historical data trends, are used to correct current values, making the final output closer to the actual physical Vce. Finally, the proposed method is validated through Matlab/Simulink simulations and physical experiments. Results show that the developed discrete model closely matches the behavior of the physical system. The method effectively identifies IGBT saturation voltage under various operating conditions with errors below 5%, and reliably captures device aging trends. The influence of traditional particle swarm optimization on monitoring performance is also investigated, confirming the accuracy and superiority of the proposed approach.
田艳军, 宋少鹏, 徐小奇, 李振. 基于卡尔曼滤波数据优化与非线性最小二乘法的IGBT饱和压降监测技术[J]. 电工技术学报, 2026, 41(8): 2641-2657.
Tian Yanjun, Song Shaopeng, Xu Xiaoqi, Li Zhen. IGBT Saturation Voltage Drop Monitoring Technology Based on Kalman Filter Data Optimization and Nonlinear Least Squares Method. Transactions of China Electrotechnical Society, 2026, 41(8): 2641-2657.
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