Open Access

ARTICLE

Intelligent Estimation of ESR and C in AECs for Buck Converters Using Signal Processing and ML Regression

Acácio M. R. Amaral^1,2,*

1 Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Coimbra, 3030-199, Portugal
2 CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Covilhã, 6201-001, Portugal

* Corresponding Author: Acácio M. R. Amaral. Email:

(This article belongs to the Special Issue: Signal Processing for Fault Diagnosis)

Computers, Materials & Continua 2025, 85(2), 3825-3859. https://doi.org/10.32604/cmc.2025.067179

Received 26 April 2025; Accepted 05 August 2025; Issue published 23 September 2025

Abstract

Power converters are essential components in modern life, being widely used in industry, automation, transportation, and household appliances. In many critical applications, their failure can lead not only to financial losses due to operational downtime but also to serious risks to human safety. The capacitors forming the output filter, typically aluminum electrolytic capacitors (AECs), are among the most critical and susceptible components in power converters. The electrolyte in AECs often evaporates over time, causing the internal resistance to rise and the capacitance to drop, ultimately leading to component failure. Detecting this fault requires measuring the current in the capacitor, rendering the method invasive and frequently impractical due to spatial constraints or operational limitations imposed by the integration of a current sensor in the capacitor branch. This article proposes the implementation of an online non-invasive fault diagnosis technique for estimating the Equivalent Series Resistance (ESR) and Capacitance (C) values of the capacitor, employing a combination of signal processing techniques (SPT) and machine learning (ML) algorithms. This solution relies solely on the converter’s input and output signals, therefore making it a non-invasive approach. The ML algorithm used was linear regression, applied to 27 attributes, 21 of which were generated through feature engineering to enhance the model’s performance. The proposed solution demonstrates an R² score greater than 0.99 in the estimation of both ESR and C.

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Keywords

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