Exponential Smoothing of Quadrature Amplitude Modulation for Power Quality Disturbance Detecting and Classification

Abstract

In this study, exponential smoothing (ES)-based quadrature amplitude modulation (QAM) is applied to detect and classify the power quality (PQ) events with high accuracy and low complexity. Considering the IEEE-1159 standards, in the simulation environment, 14 different power quality distortion signals are produced. Each of these signals is modulated at the fundamental frequency by QAM. The high-frequency constituents in the in-phase and quadratic components resulting from the QAM are filtered by the ES. PQ events are detected and classified by extracting features from the filtered in phase and quadrature component. The performance of the proposed method is examined by taking 1000 randomly selected data samples for each PQ event, and its performance is 100% and 98.28%, respectively, at noiseless and 30 dB SNR environments. The obtained results are compared with the others in the literature for the detection and classification of PQ events, and it is shown that the proposed method has better performance in noisy and noiseless conditions. In addition, to verify that the proposed method can be applied to real systems, oscillating transitions produced by capacitor banks in Simulink environment and voltage sag and swell events are generated in experimental environment. The proposed method successfully detected and classified PQ events.