POWER QUALITY SIGNALS DETECTION AND CLASSIFICATION USING LINEAR TIME FREQUENCY DISTRIBUTION

• Main Authors: ahmad, Nur Hafizatul Tul Huda, Abdullah, Abdul Rahim, JOPRI, MOHD HATTA
• Format: Conference or Workshop Item
• Language: English
• Published: 2012
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utem.edu.my/id/eprint/9357/1/2012_Paper_POWER_QUALITY_SIGNALS_DETECTION_AND_CLASSIFICATION_USING.pdf; http://eprints.utem.edu.my/id/eprint/9357/
• Institution: Universiti Teknikal Malaysia Melaka

Power quality has become a great concern to all electricity consumers. Poor quality can cause equipment failure, data and economical. An automated monitoring system is needed to ensure signal quality, reduces diagnostic time and rectifies failures. This paper presents the detection and classification of power quality signals using linear timefrequency distributions (TFD). The power quality signals focus on swell, sag, interruption, transient, harmonic, interharmonic and normal voltage based on IEEE Std. 1159-2009. The time-frequency analysis techniques selected are spectrogram and Gabor transform to represent the signals in time-frequency representation (TFR). From the time frequency representation (TFR) obtained, the signal parameters are estimated to identify the signal characteristics. The signal characteristics are the average of root means square voltage (Vave,rms), total waveform distortion (TWD), total harmonic distortion (THD) and total non harmonic distortion (TnHD) and duration of swell, sag, interruption and transient signals will be used as input for signals classification. The results show that spectrogram with the half window shift (HWS) provides better performance in term of accuracy, memory size, and computation complexity