| Title |
Development of Artificial-Intelligent Power Quality Diagnosis Algorithm using DSP |
| Authors |
Gyo-Bum Chung ; Sun-Geun Kwack |
| Keywords |
Power quality(PQ) ; Discrete Wavelet Transform(DWT) ; Fast Fourier transform(FFT) ; Artificial Neural Network(ANN) |
| 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. |