Classification of common discharges in outdoor insulation using acoustic signals and artificial neural network

Abstract

Condition monitoring of outdoor insulation systems is crucial to the integrity of distribution and transmission overhead lines and substations. The objective of this study is to use a commercial acoustic sensor along with artificial neural network (ANN), to classify different typical types of discharges in outdoor insulation systems. First, ANN was used to distinguish between five common electrical discharges that were generated under controlled conditions. Next, this approach was extended to include outdoor ceramic insulators. Three types of defects were tested under laboratory conditions, i.e. a crack in the ceramic disc, surface pollution discharge, and corona near the insulator surface. Both a single disc, and three discs connected in an insulator string were tested with respect to these defects. For both controlled samples and full insulators, a recognition rate of more than 85% was achieved.

Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting

“…The equipment that has been successfully used for the inspection of electrical power systems is the ultrasound detector, as it is directional and can be adjusted according to the condition to be analyzed. Damaged components can generate ultrasonic noise, which can be captured in a directional way, thus facilitating the exact identification of the source of the possible failure [22]. As ultrasound equipment generates an audible noise that is based on a time series, the evaluation of its continuity in contaminated insulators can facilitate the prediction of the development of a failure.…”

A Study of Multilayer Perceptron Networks Applied to Classification of Ceramic Insulators Using Ultrasound

“…The ultrasound equipment was positioned with the captor directed with the focus towards the insulator, considering the ultrasound equipment is directional [21]. Equivalent fault detection applications based on acoustic detection are presented in [28,46].…”

“…Polisetty, El-Hag, Jayram worked with ultrasound techniques [28], but in the detection of five different types of leakage current (secondary effects) and three different types of faults caused by contamination and mechanical defects, also using a non-recurring neural network to interpret the results. The acoustic signals of 60, 120 and 180 Hz provided an accuracy of 90% for detecting leakage currents and 85% for classification between corona, crack and surface discharge.…”

Tools for Measuring Energy Sustainability: A Comparative Review

“…These important social issues related to the use of energy, including poverty, quality of life, education, health, income inequality, and social justice, are themes that should be considered under the social dimension of sustainable development [61]. To improve access to electricity, researchers have assessed how electrical systems can be improved to reliably serve the population [62][63][64][65].…”