Low‐complexity wireless sensor system for partial discharge localisation

Abstract

This study describes a key element of any modern wireless sensor system: data processing. The authors describe a system consisting of a wireless sensor network and an algorithmic software for condition-based monitoring of electrical plant in a live substation. Specifically, the aim is to monitor for the presence of partial discharge (PD) using a matrix of inexpensive radio sensors with limited processing capability. A low-complexity fingerprinting technique is proposed, given that the sensor nodes to be deployed will be highly constrained in terms of processing power, memory and battery life. Two variants of artificial neural network (ANN) learning models (multilayer perceptron and generalised regression neural network) that use regression as a form of function approximation are developed and their performance compared to K-nearest neighbour and weighted K-nearest neighbour models. The results indicate that the ANN models yield superior performance in terms of robustness against noise and may be particularly suited for PD localisation.

General Machine Learning-Based Approach to Pulse Classification for Separation of Partial Discharges and Interference

“…With recent advancements in Machine Learning (ML) techniques, there has been a noted growth in approaches to handle PD pulses with new or existing algorithms [23]. The uses range from attempts to denoise the recorded PD signal [24] with Neural Networks (NN), to the localization of PD defects [25], and the classification of PD pulses with regard to the emitting source (a different defect or an interfering factor) [23].…”

LoRaWAN‐implemented node localisation based on received signal strength indicator

“…Signal propagation is dynamic and modelling the environment in which electromagnetic signals propagate is challenging. Measurable quantities such as the Received Signal Strength Indicator (RSSI) [15] [16] vary with the position of the transmitters and are time dependent. A Gaussian process…”

“…Signal propagation is dynamic and models the environment in which electromagnetic signals propagate is challenging. Measurable quantities such as the Received Signal Strength Indicator (RSSI) [15,16] vary with the position of the transmitters and are time dependent. A number of reported network-based location estimations based on RSSI indicate that deterministic solutions lack accuracy because of the temporal dependence of the measurements.…”