DSP를 이용한 인공지능형 전력품질 진단기법 연구

국문 초록 (Abstract)

본 논문은 이산웨이블렛 변환, 푸리에 변환 및 실효값의 연산 결과를 이용하여 전력품질을 진단하는 인공지능형 진단기법을 제안한다. 제안된 진단기법을 채택한 인공지능형 전력품질 진단기는 과도현상, 순간전압강하, 순간전압 상승, 순간정전 및 전고조파 외형률의 진단 및 분류가 가능하다. 신호처리를 위한 데이터 샘플링주파수는 15.36[㎑] 가 사용되었으며 샘플링된 이산데이터는 이산웨이블렛변환, 고속푸리에변환, 실효값의 연산에 사용되어진다. 효율 적인 인공지능형 전력품질 진단을 위해서 진단하고자 하는 전력품질 요소에 맞추어 간단한 다층구조의 인공신경망을 설계하였다. 제안된 인공신경망은 C＋＋ 언어로 프로그램되어 PSIM 시뮬레이션 연구에 사용되었으며, TI DSP 320C6713 마이크로프로세서를 사용한 MP PQ＋256 하드웨어에서 검증하였다.

다국어 초록 (Multilingual Abstract)

This paper proposes a new Artificial-Intelligent(AI) Power Quality(PQ) diagnosis algorithm using Discrete Wavelet Transform(DWT), Fast Fourier Transform(FFT), Root-Mean-Square(RMS) value. The developed algorithm is able to detect and classify the PQ problems such as the transient, the voltage sag, the voltage swell, the voltage interruption and the total harmonics distortion. The 15.36[㎑] sampling frequency is used to measure the voltages in a power system The measured signals are used for DWT, FFT, RMS calculation. For AI diagnosis of the PQ problems, a simple multi-layered Artificial Neural Network (ANN) with the back-propagation algorithm is adopted, programed in C＋＋ and tested in PSIM simulation studies. Finally, the algorithm, which is instalJed in MP PQ＋256 with TI DSP320C6713, is proved to diagnose the PQ problems efficiently.

목차 (Table of Contents)

• 요약
• Abstract
• 1. 서론
• 2. 전력품질 분석을 위한 신호처리
• 3. 인공신경망(ANN)
• 4. PSIM 프로그램을 이용한 인공신경망의 학습
• 5. 320C6713 DSP를 이용한 인공지능형 단상전력품질 진단기기 개발
• 6. 결론
• References
• 저자소개

참고문헌 (Reference)

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