A new approach to condition monitoring of high voltage overhead lines using neural networks

Jayasinghe, J.A.S.B.; Aggarwal, Ruchi; Johns, A.T.; Downes, J. A.

doi:10.1049/cp:19990501

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2017

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“…The technique of using artificial neural networks for monitoring overhead transmission lines was suggested by Jayasinghe et al [7]. This technique is based on the high frequency phenomena associated with the partial discharge occurrences that originate from different defects arising in the transmission lines.…”

Section: Rizzo and Lanza DI Scaleamentioning

confidence: 99%

Nondestructive Testing of Overhead Transmission Lines—numerical and Experimental Investigation

Kulkarni¹,

Hurlebaus²

2009

AIP Conference Proceedings

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Section: Rizzo and Lanza DI Scaleamentioning

confidence: 99%

Nondestructive Testing of Overhead Transmission Lines—numerical and Experimental Investigation

Kulkarni¹,

Hurlebaus²

2009

AIP Conference Proceedings

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Image processing and machine learning applied for condition monitoring of 11-kV power distribution line insulators using curvelet and LTP features

Potnuru

Bhima

2017

2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI)

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Remote diagnosis of overhead line insulation defects

Moore¹,

Portugues²,

Glover³

IEEE Power Engineering Society General Meeting, 2004.

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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

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A new approach to condition monitoring of high voltage overhead lines using neural networks

Cited by 3 publications

References 0 publications

Nondestructive Testing of Overhead Transmission Lines—numerical and Experimental Investigation

Nondestructive Testing of Overhead Transmission Lines—numerical and Experimental Investigation

Image processing and machine learning applied for condition monitoring of 11-kV power distribution line insulators using curvelet and LTP features

Remote diagnosis of overhead line insulation defects

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