A Methodology for Detection of Power Quality Disturbances in the Context of Demand Side Management

Rodrigues, Wilson L.; Borges, Fábbio A. S.; Rabelo, Ricardo A. L.; Rodrigues, Joel J. P. C.

doi:10.23919/splitech.2019.8782985

Cited by 3 publications

(2 citation statements)

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“…Since the features for classification present a long variation, the feature vector of each disturbance was extracted using the features of an ideal sine signal as a reference, as shown in the Equation (7), where SE Sin and LOE Sin correspond to the Shannon entropy and log-energy entropy proper to this signal.…”

Section: Pqd Mathematical Model Parametersmentioning

confidence: 99%

“…The improvement of power quality is an aspect that concerns both the power supplier and the power consumer [4], and the high penetration of renewable sources has implied studies that contribute to the mitigation of PQ issues [5], mainly because renewable energy is highly unpredictable [6]. Therefore, new equipment and devices have been proposed to allow prompt monitoring [7].…”

Section: Introductionmentioning

confidence: 99%

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Effect of Phase Shifting on Real-Time Detection and Classification of Power Quality Disturbances

Reyes-Archundia,

Yang,

Gutiérrez Gnecchi

et al. 2024

Energies

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Power quality improvement and Power quality disturbance (PQD) detection are two significant concerns that must be addressed to ensure an efficient power distribution within the utility grid. When the process to analyze PQD is migrated to real-time platforms, the possible occurrence of a phase mismatch can affect the algorithm’s accuracy; this paper evaluates phase shifting as an additional stage in signal acquisition for detecting and classifying eight types of single power quality disturbances. According to their mathematical models, a set of disturbances was generated using an arbitrary waveform generator BK Precision 4064. The acquisition, detection, and classification stages were embedded into a BeagleBone Black. The detection stage was performed using multiresolution analysis. The feature vectors of the acquired signals were obtained from the combination of Shannon entropy and log-energy entropy. For classification purposes, four types of classifiers were trained: multilayer perceptron, K-nearest neighbors, probabilistic neural network, and decision tree. The results show that incorporating a phase-shifting stage as a preprocessing stage significantly improves the classification accuracy in all cases.

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Section: Pqd Mathematical Model Parametersmentioning

confidence: 99%