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This paper presents a novel analysis methodology to detect degradation in electrical contacts, with the main goal of implanting a predictive maintenance procedure for sectionalizing switches, circuit breakers, and current transformers in bays of electric transmission and distribution substations. The main feature of the proposed methodology is that it will produce a predictive failure indication for the system under operation, based on the spectral analysis of the load current that is flowing through the bay’s components, using a defined relationship similar to the signal-to-noise ratio (SNR) used in data communication. A highlight of using the proposed methodology is that it is not necessary to make new investments in measurement devices, as the already-existing oscillography measurement infrastructure is enough. By implementing the diagnostic system proposed here, electrical utilities will have a modern tool for monitoring their electrical installations, supporting the implementation of new predictive maintenance functions typical of the current electrical smart grid scenario. Here, we present the preliminary results obtained by the application of the proposed technique using real data acquired from a 230 kV electrical substation, which indicate the effectiveness of the proposed diagnostic procedure.

Methodology for Predictive Assessment of Failures in Power Station Electric Bays Using the Load Current Frequency Spectrum