FPGA-based online PQD detection and classification through DWT, mathematical morphology and SVD

Power quality disturbances (PQD) in electric distribution systems can be produced by the utilization of non-linear loads or environmental circumstances, causing electrical equipment malfunction and reduction of its useful life. Detecting and classifying different PQDs implies great efforts in planning and structuring the monitoring system. The main disadvantage of most works in the literature is that they treat a limited number of electrical disturbances through personal computer (PC)-based computation techniques, which makes it difficult to perform an online PQD classification. In this work, the novel contribution is a methodology for PQD recognition and classification through discrete wavelet transform, mathematical morphology, decomposition of singular values, and statistical analysis. Furthermore, the timely and reliable classification of different disturbances is necessary; hence, a field programmable gate array (FPGA)-based integrated circuit is developed to offer a portable hardware processing unit to perform fast, online PQD classification. The obtained numerical and experimental results demonstrate that the proposed method guarantees high effectiveness during online PQD detection and classification of real voltage/current signals. © 2018 by the authors.

Artificial neural networks; Discrete wavelet transform; Field programmable gate array; Mathematical morphology; Power quality disturbance; Singular value decomposition Discrete wavelet transforms; Logic gates; Mathematical morphology; Neural networks; Numerical methods; Personal computers; Power quality; Signal reconstruction; Singular value decomposition; Wavelet decomposition; Computation techniques; Electric distribution systems; Electrical disturbances; Electrical equipment; Monitoring system; Power quality disturbances; Processing units; Singular values; Field programmable gate arrays (FPGA)

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