Analysis of the Appropriate Decomposition Level Based on Discrete Wavelet Transform for Detection of Power Quality Disturbances

Rico-Medina, None Aldo Vinicio; Reyes-Archundia, Enrique; Gnecchi, José Antonio Gutiérrez; Olivares-Rojas, Juan C.; Garcia-Ramirez, Maria Del Carmen

doi:10.1109/ropec55836.2022.10018687

Cited by 1 publication

(1 citation statement)

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“…However, increasing the number of levels implies increasing the computational expense, so the key is to choose several levels significant enough to obtain relevant information without compromising the computational performance. In this sense, decomposing the signals into nine resolution levels results in an appropriate number of decomposition levels for the case of power quality disturbances [24], so it was chosen for this work.…”

Section: Pqd Mathematical Model Parametersmentioning

confidence: 99%

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%