Abstract
Higher-Order Statistics (HOS) have been frequently applied in Power Quality Disturbance (PQD) analysis as a reliable tool for event detection. This paper outlines a technique based on mean, variance and zero-lag third and fourth cumulants – skewness and kurtosis – along with the Total Harmonic Distortion (THD) index for PQD detection. These statistics are obtained in order to characterize a waveform by a feature vector. A two-layer feed-forward neural network is then used to classify inputs (feature vectors) into a set of PQD categories. The impact of frame duration and number of hidden neurons is analyzed. The network is trained, validated and tested with synthetically-generated PQD waveforms obtained from parameter-controlled equations. As a first approach, five PQD categories are considered: sag, swell, interruption, impulsive transient and oscillatory transient. A promising overall classification rate of 99.7 % is achieved which allows future analysis with more PQD categories and/or a noisy context.
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