Satu aspek penting dalam penilaian kualiti kuasa adalah pengesanan dan pengkelasan gangguan kualiti kuasa secara automatik yang memerlukan penggunaan teknik kepintaran buatan. Kertas kerja ini membentangkan penggunaan sistem pakar-kabur untuk pengkelasan gangguan voltan jangka masa pendek yang termasuk lendut voltan, ampul dan sampukan. Untuk memperolehi sifat unik bagi gangguan voltan, analisis jelmaan Fourier pantas dan teknik purataan punca min kuasa dua digunakan untuk menentukan parameter gangguan seperti tempoh masa, magnitud voltan pmk maksimum dan minimum. Berasaskan pada parameter ini, sebuah sistem pakar–kabur telah dibangunkan dengan mengset aturan kabur yang menimbangkan lima masukan dan tiga keluaran. Sistem ini direka bentuk untuk mengesan dan mengkelaskan tiga jenis gangguan voltan tempoh masa pendek dengan menentukan sama ada gangguan adalah gangguan ketika, gangguan seketika dan bukan gangguan lendut, ampul dan sampukan. Untuk mengesahkan kejituan sistem yang dicadangkan, ia telah diuji dengan gangguan voltan yang diperolehi dari pengawasan. Keputusan ujian menunjukkan bahawa sistem pakar–kabur yang dibangunkan telah memberikan kadar pengkelasan yang betul sebanyak 98.4 %.
Kata kunci: Kualiti kuasa, sistem pakar–kabur, lendut, ampul dan sampukan
One of the important aspects in power quality assessment is automated detection and classification of power quality disturbances which requires the use of artificial intelligent techniques. This paper presents the application of fuzzy–expert system for classification of short duration voltage disturbances which include voltage sag, swell and interruption. To obtain unique features of the voltage disturbances, fast Fourier transform analysis and root mean square averaging technique are utilized so as to determine the disturbance parameters such as duration, maximum and minimum rms voltage magnitudes. Based on these parameters, a fuzzy-expert system has been developed to set the fuzzy rules incorporating five inputs and three outputs. The system is designed for detecting and classifying the three types of short duration voltage disturbances, so as to determine whether the disturbance is instantaneous, momentary and non sag, swell and interruption. To verify the accuracy of the proposed system, it has been tested with recorded voltage disturbances obtained from monitoring. Tests results showed that the developed fuzzy–expert system gives a correct classification rate of 98.4 %.
Key words: Power quality, fuzzy–expert system, sag, swell and interruption.
Localization and Classification of Power Quality Disturbances using Maximal Overlap Discrete Wavelet Transform and Data Mining based Classifiers∗∗Sponsor and financial support acknowledgment goes here. Paper titles should be written in uppercase and lowercase letters, not all uppercase.
