Work

Interturn Short-Circuit Fault Diagnosis in a Permanent Magnet Synchronous Generator Using Wavelets and Binary Classifiers

Year: 2026

Type: article

Abstract: Condition monitoring and diagnosis in a permanent magnet synchronous generator (PMSG) are crucial for ensuring its service continuity and reliability. Recent advancements have introduced innovative, non-invasive techniques for detecting mechanical and electrical faults in this machine. This paper proposes a novel application of the discrete wavelet transform and binary classifiers for diagnosing interturn short-circuit faults in a PMSG with high accuracy and low computational burden. The objective of fault diagnosis is to detect the presence of an interturn short-circuit fault (fault vs. no-fault) under different fault severities and operating speeds. Multiple binary models were trained separately for each fault scenario. The three-phase currents from the PMSG are processed using the discrete wavelet transform to extract features, which are then fed into a binary classifier based on a Random Forest algorithm. Optimization techniques are used to improve the performance of the binary classifiers. Experimental results obtained under various stator fault conditions in the PMSG are presented. Metrics such as accuracy and confusion matrices are used to evaluate the performance of binary classifiers.

Source: Processes

DOI: 10.3390/pr14020377

Authors: Jose Antonio Alvarez-Salas, Francisco Javier Villalobos-Pina, Mario Arturo Gonzalez-Garcia, Ricardo Alvarez-Salas

Institutions: Instituto Tecnológico de Aguascalientes, Autonomous University of San Luis Potosí

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

OpenAlex ID: W7125426185

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