Measurements of RIV on Large EHV Apparatus in High Voltage Laboratory

Goliński, J.; Malewski, R.; Train, D.

doi:10.1109/tpas.1979.319294

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Hence, the basic weak points in electrical power networks are sharp-edges corona, polluted-insulators "baby arcs" and loose-contacts arcing [1]. Early locating and identifying such weak-points in electrical power systems significantly decrease the imminent failure, outage time and supply interruption that critically reflect the operability and reliability of the electrical power networks/systems [1][2][3][4][5][6][7]. In general, the basic weak points (sharp-edges, baby-arcs and loose-contacts) generate audio noise, radio interference complaints and/or ultrasonic noise emissions.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the basic weak points (sharp-edges, baby-arcs and loose-contacts) generate audio noise, radio interference complaints and/or ultrasonic noise emissions. Therefore, there are several types of sensors to detect and locate them [2][3][4]. The ability to locate and identify the weak points guides the maintenance staff to take a proper action such as hot washing of lines and insulators, short-circuiting the gaps by better bonding or tightening the connections, and by smoothening the coronating points to suppress corona activity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural networks recognition of weak points in power systems based on wavelet features

Abdel‐Salam¹,

Hasan²,

Sayed³

et al. 2005

18th International Conference and Exhibition on Electricity Distribution (CIRED 2005)

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Early locating and identifying basic weak-points (sharp-edge corona, polluted-insulator "baby arcs" and loose contact arcing) in electrical power systems significantly decrease the imminent failure, outage time and supply interruption. We previously introduced a method for detecting the basic weakpoints based on sound/waveform patterns and frequency analysis of their ultrasonic emissions. However, nonstationary patterns of the basic weak-points' emitted signals and background noise frequently led to confusing discrimination. Therefore, this paper develops an effective pattern recognition scheme, employing wavelet feature extraction and Artificial Neural Network (ANN) classification, to identify the basic weak-points and two weakpoint combinations (polluted insulator stressed by a transmission line with a sharp-edge and multiple sharp-edges on the same line), based on their modulated ultrasonic emissions. Extensive testing proved that the proposed scheme achieved average recognition rate of 98% when tested using weak-points underneath 33-kV and 132-kV transmission lines with 2-second detected signals. Moreover, increasing the acquisition time (>30 seconds) and classifying the weakpoints based on majority voting over the ANN's responses of multiple (15) consecutive sections, consistently led to 100% successful recognition of the considered weak-points.

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