Locating multiple partial discharge sources using MAP estimation and ray tracing

Ishimaru, Hirokazu; Kawada, Masatake

doi:10.1002/tee.22033

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Reference proposed a new tracing algorithm for virtually randomly located sources. Exploiting a MIMO‐like operation, an energy‐efficient localization procedure was perfectly fulfilled in Ref.…”

Section: Mimo: General Standpointmentioning

confidence: 99%

A comprehensive survey on MIMO arrays: IEEJ TEE perspective

Zamanipour¹

2018

IEEJ Transactions Elec Engng

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The letter fundamentally provides a comprehensive overview on the articles published in the Journal IEEJ Transactions on Electrical and Electronic Engineering on multi‐input multi‐output (MIMO) systems. All the methods, criteria, metrics and requirements are essentially taken into consideration. Finally, an outline‐based figure is given, listing all the references investigated in the letter. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Mimo: General Standpointmentioning

confidence: 99%

A comprehensive survey on MIMO arrays: IEEJ TEE perspective

Zamanipour¹

2018

IEEJ Transactions Elec Engng

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“…TDoA requires accurate synchronisation, making it computationally complex and energy hungry, hence not possible to deploy for continuous monitoring and localisation of PD. DoA measurement has also been successfully used to locate PD sources [14, 15]. In this method, directional antenna arrays are used to estimate the DoA of the RF signals that propagate from the discharge generating defect.…”

Section: Introductionmentioning

confidence: 99%

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

Iorkyase

Tachtatzis

Glover³

et al. 2019

High Voltage

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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.

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“…TDoA and AoA methods are computationally expensive and have energy‐hungry signal processing costs. RSS‐based approaches are more appropriate to low‐complexity implementations but at the cost of reduced localisation accuracy [14, 15]. The reduced accuracy of purely RSS‐based methods is attributable to their susceptibility to interference due to multipath propagation, path loss attenuation and/or signal shadowing.…”

Section: Introductionmentioning

confidence: 99%

Low‐complexity wireless sensor system for partial discharge localisation

Iorkyase

Tachtatzis

Lazaridis

et al. 2019

IET wirel. sens. syst.

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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.

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Locating multiple partial discharge sources using MAP estimation and ray tracing

Cited by 4 publications

References 15 publications

A comprehensive survey on MIMO arrays: IEEJ TEE perspective

A comprehensive survey on MIMO arrays: IEEJ TEE perspective

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

Low‐complexity wireless sensor system for partial discharge localisation

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