Radio Location of Partial Discharge Sources: A Support Vector Regression Approach

Partial discharge (PD) can provide a useful forewarning of asset failure in electricity substations. A significant proportion of assets are susceptible to PD due to incipient weakness in their dielectrics. This paper examines a low cost approach for uninterrupted monitoring of PD using a network of inexpensive radio sensors to sample the spatial patterns of PD received signal strength. Machine learning techniques are proposed for localisation of PD sources. Specifically, two models based on Support Vector Machines (SVMs) are developed: Support Vector Regression (SVR) and Least-Squares Support Vector Regression (LSSVR). These models construct an explicit regression surface in a high dimensional feature space for function estimation. Their performance is compared to that of artificial neural network (ANN) models. The results show that both SVR and LSSVR methods are superior to ANNs in accuracy. LSSVR approach is particularly recommended as practical alternative for PD source localisation due to it low complexity

RF-based location of partial discharge sources using received signal features

Partial discharges (PDs) are symptomatic of some localised defects in the insulation system of electrical equipment. PD activity emits electrical pulses in the form of radio frequency (RF) signals which can be captured using appropriate sensors. The analysis of the measured RF signals facilitates localisation of PD. This study investigates the plausibility of using purely RF received signal features of PD pulses to locate PD at low cost. A localisation approach based on the analysis of these features has been developed, with the assumption that PDs generate unique RF spatial patterns due to the complexities and nonlinearities of RF propagation. In this approach, two distinct frequency bands which hold different PD information are exploited. PD location features are extracted from the main PD signal and the two sub-band signals. Correlation-based feature selection (CFS) is employed for feature selection and dimensionality reduction. Experimental results show that PD location can be inferred from the features of the PD pulses. The application of CFS to PD data reduces the memory/computational demand and improves localisation accuracy