應用小波轉換與類神經網路進行電力系統故障種類之判別

Identification of Power System Fault Using Wavelet Transform and Neural Network

徐維聰(Wei-Tsung Hsu)

指導教授 ： 洪穎怡

中原大學/工學院/電機工程研究所/碩士(2003年)

https://doi.org/10.6840/cycu200300547

摘要

近年來由於高科技產業與電腦設備等精密儀器之迅速發展，相對的對於供電的品質要求也愈來愈高，所以穩定的供電品質是很重要的一環，利用監測方式去瞭解故障發生之原因，以作為日後改善之參考依據。

本文針對電力系統故障(單相接地故障，線至線故障，兩線接地故障，三相短路接地故障)之暫態現象進行分析研究，利用小波轉換監測故障並結合類神經網路，實現一電力系統故障種類之判別。

本文以MATLAB及Simulink模擬各種電力系統故障信號波形，以驗證所提方法之可行性。測試結果顯示，本文所提之方法可正確判別電力系統故障波形。

關鍵字

故障 ； 類神經網路 ； 小波轉換 ； 電力品質

並列摘要

Recently, power supply quality has become an increasingly important topic due to rapid development of the high-tech and precision instrument industries. Therefore, the quality of stable power supply is one of the main issues. Monitoring methods can be used to investigate the causes of the power supply faults so as to make further improvements. Therefore, the power system fault monitoring is one of the most important research topics.

This thesis focuses on the analysis of the temporary states of different kinds of the power system faults (single line-to-ground faults, line-to-line faults, double line-to-ground faults and three line-to-ground faults). This thesis utilized the wavelet transform fault monitoring method with the neural network techniques to implement the identification of power system faults.

To assess the performance of the proposed approach, this approach has been tested on many power system fault signals by MATLAB/SIMULINK. The accuracy and efficiency of the proposed approach are verified under many different fault cases. The results show the proposed approach can identify the power system fault correctly.

並列關鍵字

wavelet transform ； power quality ； fault ； neural network

參考文獻

[40]廖炯州，「減少雜訊影響之電力暫態信號監測方法」，博士論文，中原大學，民國90年6月。

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