Work

Data-Driven Fault Diagnosis Approach for Synchronous Generators

Year: 2025

Type: article

Abstract: This article presents a data-driven approach for diagnosing interturn short-circuit (ITSC) faults in the field winding of synchronous generators (SGs). A notable advantage of this method is its independence from the load’s linearity or nonlinearity. The method’s foundation is derived from analyzing the impact of ITSC faults on the state-space model of an SG, utilizing the SG equations in thedqrotor reference frame. Based on the state-space model, subspace identification and input–output data, including voltages and currents, are used to estimate the eigenvalues of the state matrix. The detection, isolation, and estimation of faults are achieved through the estimated eigenvalues, without relying on the model. Simulation and experimental results validate the effectiveness of this data-driven fault diagnosis methodology.

Source: IEEE Open Journal of Industry Applications

DOI: 10.1109/ojia.2025.3591740

Authors: Zahra Masoumi, Bijan Moaveni

Institutions: Iran University of Science and Technology, K. N. Toosi University of Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,075