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

Remote diagnosis of overhead line insulation defects

High voltage insulation defects cause partial discharges (PD) which can be detected through the reception of radiated radio frequency (rf) impulses. The paper describes a method of detecting insulation defects on overhead lines using vehicle mounted rf measuring equipment. The equipment is based on a 4 antenna array that is directly sampled using digital equipment with a bandwidth of 1 GHz. The results are analysed by firstly estimating the time delays apparent between the 4 antennas. Secondly, using the time delays, bearing and RMS time delay error information is calculated that allows identification of the PD source. The equipment has been tested in the field on a 132 kV overhead line defect that was initially reported to a radio spectrum management agency. The results show that equipment has the sensitivity to identify the defective insulator string

Characteristics of impulsive noise in electricity substations

Measurements of noise in an electricity substation are reported. The measurements are made in four contiguous frequency bands covering the range 100 MHz to 6 GHz. The range includes those bands relevant to modern wireless LAN and wireless PAN technologies such as IEEE 802.11a/b/g and IEEE802.15.1/4). Impulsive events are extracted from the measured data and a statistical analysis these events is presented

TEM horn antenna for detection of impulsive noise

A TEM half-horn antenna has been designed and constructed for the detection impulsive noise. The particular application of interest is the recording of partial discharge and/or sferic radiation in a 400 kV electricity transmission substation. The characteristics and performance of the half-horn are presented. Laboratory verification of the antenna's utility for the detection partial discharge has been carried out using a partial discharge simulator

Impulsive noise modelling and prediction of its impact on the performance of WLANs

As part of a larger project to assess the risk associated with the deployment of wireless equipment in electricity substations the BER performance of IEEE 802.11b and IEEE 802.11a in the presence of impulsive noise has been investigated. Middleton class A noise model is used to simulate impulsive noise environment and Simulink is used to simulate the WLAN physical layer. The observed degradation in performance is compared with that due to additive white Gaussian noise

ZigBee Performance in 400 KV Air Insulated Power Substation

An experimental assessment of the impact of the electromagnetic environment of a 400 kV substation on the performance of ZigBee equipment is described. The experimental assessment includes a pragmatic field trial and a laboratory test. The laboratory test, in which all external noise and interference are excluded, is used as a control. A simple set of metrics are used to compare the performance of ZigBee equipment deployed in the substation with that deployed in a controlled laboratory environment. The results based on more than 1.6 Gbit of transmitted data show no significant adverse impact of the substation electromagnetic environment on the performance of ZigBee equipmen