Predictive Maintenance Based on Artificial Neural Network for MV Switchgears

The distribution network is recognized for its inherent fragility and challenging management compared to the transmission network. This challenge arises from the complex topology of the distribution network, involving thousands of nodes to monitor by predictive maintenance. This article introduces the implementation of an artificial neural network (ANN) for predictive maintenance of medium-voltage (MV) switchgears. In particular, the study here presented consists in proposing a new approach that correlates temperature measurements, at different positions within the switchgear, to potential faults. The specific cause of temperature change is the variable fastening in a single point of the MV switchgear. Thus, starting from experimental measurements, the ANN is thoroughly analyzed, compared, and validated to provide a proper classification of the switchgear health status. The obtained results affirm the efficacy of the proposed approach and highlight the benefits of its application in practical predictive maintenance scenarios.