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Investigation of a comprehensive identification method used in acoustic detection system for GIS
68
Citations
31
References
2010
Year
Acoustic DetectionEngineeringAcoustic Detection SystemAcoustic SensorOcean AcousticsEngineering AcousticNoiseAcoustic SignalsAcoustical EngineeringSound PropagationDetected Acoustic SignalsAcoustic Signal ProcessingAcoustic AnalysisSonar Signal ProcessingHealth SciencesAcoustic MethodsStructural Health MonitoringAcoustic PropagationComprehensive Identification MethodSignal ProcessingCivil EngineeringSpeech ProcessingComputational Acoustics
Nowadays, the acoustic detection is widely used for defect diagnosis of gas insulated substations (GIS) in normal operation and factory tests. In this paper in order to develop a data analyzer for acoustic detection system to make an assistant diagnosis, the characteristic of acoustic signals generated by different artificial defects such as protrusions, floating shield, void in spacer and bouncing particles are investigated. Some meaningful parameters behind the detected acoustic signals are extracted and discussed, which are used to distinguish background noise, partial discharge (PD) phenomena or bouncing particles. Based on those works, a comprehensive identification method realized by processing the acoustic pulse sequences q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , ( Δ t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i,</sub> q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ) and (t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ) is introduced, which gives a recognition result with noise, PD type or bouncing particles. For the sequence (t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i,</sub> q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ), the backpropagation artificial neural network optimized by genetic algorithm (GA-BPANN) is used as a classifier based on the fingerprint consisting of 24 operators, which are derivate from typical 2D histograms of phase-resolved partial discharge (PRPD). And with considering the trigger source may having a phase difference from the working voltage, identification with phase compensation (IPC) is used as a try to deal with the challenge. Experimental results show that the comprehensive identification method is practical and effective.
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