Abstract
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.
Highlights
Non-linear loads and environmental circumstances might induce power quality disturbances (PQD) in electric distribution networks [1], which produce equipment malfunction and useful life reduction
The detection and categorization results for diverse Power quality disturbances (PQD) in voltage/current signals, utilizing the introduced approach, are shown in Table 5, when noise contamination is considered in a signal-to-noise ratio (SNR) that goes from 20 dB to 50 dB
The obtained results for the PQD detection and classification of real experimental voltage/current signals collected through the test bench described in Section 4 are shown in Table 6, where 50 different trials were considered for each class
Summary
Non-linear loads and environmental circumstances might induce power quality disturbances (PQD) in electric distribution networks [1], which produce equipment malfunction and useful life reduction. For diminishing power quality (PQ) problems, it is important to determine the components provoking the problems in the distribution signal [4,5]. This demands thorough and effective PQ monitoring and classification, making it an open subject for research since the detection and classification of electrical disturbances causing PQDs are difficult tasks that require a high level of engineering [6]. In [7], two empirical-mode, decomposition-based techniques are used for signal denoising in PQD
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