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Article

Fault Detection in MV Switchgears Through Unsupervised Learning of Temperature Conditions

1
Department of Information Engineering, Polytechnic University of Marche, 60131 Ancona, Italy
2
Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(15), 4818; https://doi.org/10.3390/s25154818
Submission received: 30 June 2025 / Revised: 29 July 2025 / Accepted: 2 August 2025 / Published: 5 August 2025
(This article belongs to the Special Issue Sensors Technology Applied in Power Systems and Energy Management)

Abstract

This paper presents a distributed measurement system intended to effectively monitor the health status of switchgears under varying temperature conditions. In particular, thermocouples are deployed as temperature sensors for the continuous monitoring of a medium-voltage (MV) switchgear. Then, by integrating a low-cost microcontroller unit, the proposed system can implement previously trained unsupervised learning techniques for health status evaluation. This approach enables the early detection of potential faults by identifying anomalous temperature patterns, thus supporting predictive maintenance and extending the lifespan of switchgears. The results show strong clustering performance with low execution times, highlighting the suitability of the method for resource-constrained hardware. Furthermore, onboard temperature processing eliminates the need for data transmission to remote servers, reducing latency and communication overhead while improving system responsiveness. The paper includes a numerical analysis on synthetic data as well as a validation on real measurements. Overall, the presented distributed measurement system offers a scalable and cost-effective solution to enhance the reliability and safety of MV switchgears.
Keywords: fault detection; medium voltage switchgears; distributed measurement system; temperature monitoring; unsupervised learning; clustering algorithms fault detection; medium voltage switchgears; distributed measurement system; temperature monitoring; unsupervised learning; clustering algorithms

Share and Cite

MDPI and ACS Style

Iadarola, G.; Mingotti, A.; Negri, V.; Spinsante, S. Fault Detection in MV Switchgears Through Unsupervised Learning of Temperature Conditions. Sensors 2025, 25, 4818. https://doi.org/10.3390/s25154818

AMA Style

Iadarola G, Mingotti A, Negri V, Spinsante S. Fault Detection in MV Switchgears Through Unsupervised Learning of Temperature Conditions. Sensors. 2025; 25(15):4818. https://doi.org/10.3390/s25154818

Chicago/Turabian Style

Iadarola, Grazia, Alessandro Mingotti, Virginia Negri, and Susanna Spinsante. 2025. "Fault Detection in MV Switchgears Through Unsupervised Learning of Temperature Conditions" Sensors 25, no. 15: 4818. https://doi.org/10.3390/s25154818

APA Style

Iadarola, G., Mingotti, A., Negri, V., & Spinsante, S. (2025). Fault Detection in MV Switchgears Through Unsupervised Learning of Temperature Conditions. Sensors, 25(15), 4818. https://doi.org/10.3390/s25154818

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