Wavelet-Based Intelligent System for Recognition of Power Quality Disturbance Signals
Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a new approach for the recognition of power quality disturbances using wavelet transform and neural networks. The proposed method employs the wavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different power quality signal types efficiency.
KeywordsDiscrete Wavelet Transform Power Quality Harmonic Distortion Wavelet Domain Learning Vector Quantization
Unable to display preview. Download preview PDF.
- 1.Santoso, S., Powers, E.J., Grady, W.M.: Power Quality Disturbance Waveform Detection Using Wavelet Transform Analysis. In: Proc. IEEE Conf. Time-Frequency and Time- Scale Analysis, pp. 166–169 (1994)Google Scholar
- 2.Kaewarsa, S., Attakitmongcol, K.: Diagnostic of Power Quality Disturbance Using Wavelet- Based Neural Network. In: Proc. IASTED Int. Conf. Energy and Power Systems, pp. 245–250 (2005)Google Scholar
- 6.Hagan, M.T., Demuth, H.B., Beale, M.: Neural Network Design. PWS Publishing Company, Boston (1996)Google Scholar
- 7.McEachern, A.: Handbook of Power Signatures, Foster city, CA. Basic Measuring Instruments (1988)Google Scholar
- 8.Dugan, R.C., McGranaghan, M.F., Santoso, S., Beaty, H.W.: Electrical Power System Quality. McGraw-Hill, New York (2003)Google Scholar