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Low-complexity wireless sensor system for partial discharge localisation
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DOI：10.1049/iet-wss.2018.5075.png)
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摘要
En 中文
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.
Keyword:
learning (artificial intelligence)
condition monitoring
neural nets
multilayer perceptrons
wireless sensor networks
partial discharge measurement
substations
power engineering computing
nearest neighbour methods
approximation theory
regression analysis
generalised regression neural network
PD localisation
low-complexity wireless sensor system
partial discharge localisation
wireless sensor network
algorithmic software
condition-based monitoring
electrical plant
low-complexity fingerprinting technique
artificial neural network learning models
wireless sensor system
substation
radio sensors
multilayer perceptron
weighted K-nearest neighbour models
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期刊
IF:
2.4
论文数:
13
被引数:
420
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