FPGA-Based Online PQD Detection and Classification through DWT, Mathematical Morphology and SVD

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. Author Contributions: M.L.-R. conceived the methodology, performed the experimentation and compile the mathematical definition for the treated PQD; E.C.-Y. supervised the work, defined all the tasks and wrote the paper; L.M.L.-C. provided support during experimentation and simulations; H.M.-V. provided the equipment and guided the obtaining of the real signals used for validating the proposed approach; C.R.-D. designed and implemented the data acquisition system; R.A.L.-M. verified the PC implementation for signal models and processing algorithms. Conflicts of Interest:The authors declare no conflict of interest.

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

“…These algorithms are reviewed under the miscellaneous category for their effectiveness in power quality assessment. These includes, hardware and software architecture of expert system [198], rule-based model [199], improved generalized adaptive resonance theory (IGART) [200], recurrence quantification analysis [201], stochastic ordering theory with coded quickest classification [202], variety of supervised NN with online learning capabilities [203], attribute weighted artificial immune evolutionary classifier (AWAIEC) [204], spectral kurtosis to separate hybrid PQ disturbances [205], DT initialized fuzzy C-means clustering system based on ST [206], variational mode decomposition (VMD) [207], real-time calculation of the spectral kurtosis [208], online PQDs detection and classification using DWT, MM and SVD [209], curvelet transform and deep learning [210], rule-based ST and adaboost with decision stump as weak classifier [211], random forests based PQ assessment framework [212], deep learning-based method and stacked auto-encoder, as a deep learning framework [213], ICA with a sparse autoencoder (SAE) for gaining automatically training features [214] and a new class-specific weighted random vector functional link network (CSWRVFLN) [137]. The performance analysis of different AI techniques is listed in Table 6.…”

A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances

“…Yet, all related works sum up that no upper domain exists while dealing with signal characterization, but the fusion of information represents the most promising approach [15]. The reduction of such sets of numerical indicators, regarding their relevance for characterizing patterns, represents a critical data-driven step [16]. Accordingly, non-significant and redundant information must be discarded or attenuated in order to optimize further pattern recognition tasks.…”

Accurate identification and characterisation of transient phenomena using wavelet transform and mathematical morphology

“…Farhan and Shanti Swarup [23] developed a methodology for the detection of islanding condition in microgrids by using a filter based on dilated and erode difference to determine a ratio index and use it to track the islanding condition. Lopez-Ramirez et al [24] developed a hardware PQD sorter based on mathematical morphology and singular value decomposition; the sorter is implemented in a field-programmable gate array and the results show efficacy in the PQD classification.…”