Journal of Applied Science and Engineering

Published by Tamkang University Press

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Research on prediction and estimation of power electronic equipment operation state based on discrete Kalman filter

 2026

 Volume 34

Junjie Liu1,2 and Pingping Zhang1,2

1School of Hebi Institute of Engineering and Technology, Henan Polytechnic University, Hebi 458000, Henan Province, China

2School of Electronics And Information, Henan Institute of Information Science and Technology, Hebi 458000, Henan Province, China

Received: April 25, 2026
Accepted: July 05, 2026
Publication Date: August 05, 2026

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Thermal and Electrical Stress Factors  Affecting Power Electronic  Equipment Reliability  

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation:  BibTeX | http://dx.doi.org/10.6180/jase.202611_34.021  

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Inversion, conversion, and motor drives are power electronic devices central to current industrial systems and are often subject to operational challenges created by thermal stress, voltage imbalance, and dynamic loading. These problems particularly affect China’s market sectors of high performance, where reliability and continuous operation matter. While fault detection systems originally used in such applications are likely to be somewhat reactive and thus fall short of watching systems fail in real-time, this study introduces a predictive framework and real-time estimation for monitoring two upmost operating states, namely: junction temperature and DC bus voltage via a discrete Kalman filter. The structure of the framework is based on a recursive state space model that receives real-time sensor input to dynamically predict and correct internal state variables. The proposed discrete Kalman filter framework continuously estimates and predicts junction temperature and DC bus voltage from real-time sensor measurements, enabling proactive fault detection and thermal management before critical operating limits are reached under dynamic loading conditions. The algorithm is lightweight, and therefore suitable for embedded systems, industrial applications, and applications with high computational loads involved. An additional condition for the model is based on biologically inspired thermal stress indicators that enhance validation of thermal behavior. Validated with simulated datasets at various thermal and load conditions, the model instigates empirical results regarding its predictive performance. Experimental results confirm above 97 percent accuracy level for junction temperature prediction by the proposed system and a significant reduction in failure risk. In addition, multiple sensory data fusion further strengthens prediction’s robustness, enhancing early fault detection for proactive thermal management. Compared with conventional threshold-based models and those based on an artificial intelligence model, discrete Kalman filter provides
more efficient, reliable, and scalable real-time state estimation.

Keywords: Kalman filter, power electronics, state estimation, predictive control, thermal management

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