{"title":"Fractal Patterns for Power Quality Detection Using Color Relational Analysis Based Classifier","authors":"Chia-Hung Lin, Mei-Sung Kang, Cong-Hui Huang, Chao-Lin Kuo","volume":41,"journal":"International Journal of Energy and Power Engineering","pagesStart":818,"pagesEnd":825,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/12617","abstract":"This paper proposes fractal patterns for power quality\r\n(PQ) detection using color relational analysis (CRA) based classifier.\r\nIterated function system (IFS) uses the non-linear interpolation in the\r\nmap and uses similarity maps to construct various fractal patterns of\r\npower quality disturbances, including harmonics, voltage sag, voltage\r\nswell, voltage sag involving harmonics, voltage swell involving\r\nharmonics, and voltage interruption. The non-linear interpolation\r\nfunctions (NIFs) with fractal dimension (FD) make fractal patterns\r\nmore distinguishing between normal and abnormal voltage signals.\r\nThe classifier based on CRA discriminates the disturbance events in a\r\npower system. Compared with the wavelet neural networks, the test\r\nresults will show accurate discrimination, good robustness, and faster\r\nprocessing time for detecting disturbing events.","references":"[1] IEEE Recommended Practices for Monitoring Electric Power Quality\r\nANSI\/IEEE Std. 1159-1995.\r\n[2] Kwan, T. and Martin, K., \"Adaptive Detection and Enhancement of\r\nMultiple Sinusoids Using a Cascade of IIR Filters, \"IEEE Trans., 1989,\r\nCAS-36, pp. 936-947.\r\n[3] E. O. Brigham, \"The Fast Fourier Transform, \"Englewood Cliffs, NJ:\r\nPrentice-Hall, 1990.\r\n[4] P. Pillay and A. 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