A Power Capacitor Fault Diagnosis System based on Empirical Mode Decomposition Method and Extension Neural Network

• Main Authors: LIN,KUN-DE, 林坤德
• Other Authors: WANG,MENG-HUI
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/ujyty5

碩士 === 國立勤益科技大學 === 電機工程系 === 107 === This study proposes combining an extension neural network with the Chaos Theory and Empirical Mode Decomposition for power capacitor fault recognition, where the current data are measured and diagnosed for a power capacitor bank running at low voltage, and the capacitor current measurement is tested by a power testing machine. Afterwards, the Empirical Mode Decomposition is combined with the chaos synchronization detection method to analyze the voltage and current signals extracted by the high frequency oscillograph, and the dynamic chaos error scatter map using chaos eyes as the fault diagnosis feature is established. Finally, the extension neural network algorithm is used for capacitor fault detection, and the real -time status of the power capacitor is monitored by the developed human - machine interface. The advantage of the proposed method is that big data are compressed and meaningful eigenvalues are extracted, in order to effectively detect subtle changes in the power capacitor current signals, and diagnose the faults in the operating state of the power capacitor. According to the actual measurement result, the accuracy of the proposed method is as high as 95%, which is better than the extension theory (84%) and the multilayer artificial neural network (91%), proving this method is applicable to power capacitor discharge detection.