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Power converters (PCs) are vital elements of critical applications, making their reliable operation crucial. Enhancing PCs’ reliability can be achieved by adding intelligence to the system, enabling it to predict failures and generate early warnings before a failure occurs. In this context, intelligence is integrated into the system through preventive diagnostic algorithms (PDAs) that assess the converter condition. This article introduces a PDA designed to determine the optimal replacement timing for aluminum electrolytic capacitors (AECs) within power converters. AECs, in addition to being a fundamental component of PCs, also represent the most vulnerable element of the PCs’ power section. The aging of AECs is characterized by a decrease in capacitance (C) and an increase in the equivalent series resistance (ESR). Therefore, ESR and C serve as key indicators for assessing the AECs’ health status. One of the most critical functional requirements for designing a PDA is its accuracy, which can be significantly affected by transients. The solution proposed in this paper is resilient to transients, overcoming a common problem in implementing AECs’ PDAs. The proposed algorithm employs discrete wavelet transform (DWT) to extract the converter signal modes. Subsequently, key characteristics of these modes are extracted, enabling the calculation of both ESR and C. Finally, by using the estimated ESR and C values, two fault indicators can be obtained that are resilient to transients. Employing a Kalman filter reduces noise and ensures the indicators’ resilience to transients, making them highly effective for evaluating the AECs’ health status. The proposed PDA was validated through multiple computer simulations conducted in MATLAB/Simulink for a three-phase interleaved boost converter (3ϕIBC), which includes a proportional-integral (PI) controller with anti-windup capability.
Amaral et al. (Sat,) studied this question.