Analysis of Sensor Trapped Power Quality Indices Using Empirical Wavelet Transform and Rational Dilation Wavelet Transform to Achieve High Accuracy and Frequency Resolution

Panneerselvam, Srividhya Jayaraman; Dhandapani, Sivakumar

doi:10.1166/sl.2019.4059

Cited by 3 publications

(4 citation statements)

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“…Then, these metrics of DC data are labeled into different levels based on the comprehensive label method combined with the AHP and entropy coefficient method [11], [41], and the labels obtained are excellent, good, poor, and very poor. Four typical labels of the group of data are given in Table 2.…”

Section: ) the Proposed Framework Based On Deep Cnnmentioning

confidence: 99%

“…There are many methods for power quality evaluation of AC systems. First, some traditional methods are used in evaluating AC power quality, such as the analytic hierarchy process (AHP) [10], empirical wavelet transform (EWT) and rational dilation wavelet transform (RADWT) [11], S-transform [12], and empirical mode decomposition (EMD) [13]. There are some advantages and disadvantages of each technique.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of DC Power Quality Based on Empirical Mode Decomposition and One-Dimensional Convolutional Neural Network

Li³

et al. 2020

IEEE Access

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With the rise in the use of DC distributed energy resources and the growth of DC electricity load, the difficulty in improving DC power quality has become an important research direction. The research on DC power quality has an important impact on the development of DC power distribution theory and technology. In this paper, an evaluation method that combines empirical mode decomposition (EMD) with a one-dimensional convolutional neural network (1D-CNN) of DC power quality is proposed. As a method of data preprocessing, EMD decomposes the original electrical signal into several intrinsic mode functions (IMFs). Then, the 1-D CNN with a residual module is used to train the data obtained from EMD and conducts a comprehensive evaluation with different levels. In addition, the proposed network was compared with other state-of-the-art deep neural networks, and the experiment proved its effectiveness. Finally, an example analysis is carried out with the data provided by the Gree Photovoltaic Direct-driven Inverter Multi VRF (variable refrigerant flow) System to show the validity of the proposed method for evaluating DC power quality in a real case. INDEX TERMS DC power quality, photovoltaic direct-driven inverter multi VRF, empirical mode decomposition, one-dimensional convolutional neural network.

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Section: ) the Proposed Framework Based On Deep Cnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of DC Power Quality Based on Empirical Mode Decomposition and One-Dimensional Convolutional Neural Network

Li³

et al. 2020

IEEE Access

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“…20 Power quality issues raised by the use of nonlinear loads can be monitored through implementation of transform techniques for suppression of background instability, also for the achievement of high accuracy and frequency resolution in power systems. 21,22 Moradi 23 found a unique genetic algorithm and Particle Swarm Optimization combination for DGs ideal placement and sizing on distribution systems. A hybrid optimization model has been proposed by Singh 24 for the resolution of a multi objective function for solving DG integration issues that cause increase in voltage regulation while minimizing power losses for improvement in voltage profile.…”

Section: Introductionmentioning

confidence: 99%

“…The usage of numerous nonlinear loads in power system causes several power quality issues that reduce the overall efficiency of the power system network 20 . Power quality issues raised by the use of nonlinear loads can be monitored through implementation of transform techniques for suppression of background instability, also for the achievement of high accuracy and frequency resolution in power systems 21,22 . Moradi 23 found a unique genetic algorithm and Particle Swarm Optimization combination for DGs ideal placement and sizing on distribution systems.…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of distributed generation for power flow enhancement in distribution network

Jayaraman Panneerselvam,

Krishnamoorthi,

Ramamoorthy

et al. 2023

Optim Control Appl Methods

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This article articulates the enhancement of voltage profiles in transmission systems by reducing the voltage deviation and system power losses. Electric power flows uniformly throughout Regional Distribution Networks in the power grid station, resulting in low voltage consistency, significant voltage dips and power losses in the distribution network, highlighting high R/X ratios even with a heavy load or power failure issues, despite installing distributed generation in the radial distribution system for enhancing voltage profile in the grid network. The meta‐heuristic optimization algorithm plays a vital role in determining optimal location of DG for attaining the objective of research. In the practical network, a single objective optimization strategy cannot be employed to solve of power system optimization problems of all kinds. Hence multi‐objective function needs to be addressed. The prime task of this research is to investigate and allocate an optimal location to connect the Distributed Generation, with evaluation of the best DG configuration, with minimized power losses and improved voltage profile of the distribution network using the Hummingbird Optimization Algorithm. A standard IEEE 37‐bus Radial Distribution system is employed as the test bus system to test the performance and effectiveness of the optimization technique. To show the stiffness of the proposed algorithm the results are compared with various optimization approaches that employ similar objective functions.

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Power quality enhancement in smart microgrid system using convolutional neural network integrated with interline power flow controller

Srividhya,

Prabha,

Jaisiva

et al. 2024

Electr Eng

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Analysis of Sensor Trapped Power Quality Indices Using Empirical Wavelet Transform and Rational Dilation Wavelet Transform to Achieve High Accuracy and Frequency Resolution

Cited by 3 publications

References 0 publications

Evaluation of DC Power Quality Based on Empirical Mode Decomposition and One-Dimensional Convolutional Neural Network

Evaluation of DC Power Quality Based on Empirical Mode Decomposition and One-Dimensional Convolutional Neural Network

Optimal allocation of distributed generation for power flow enhancement in distribution network

Power quality enhancement in smart microgrid system using convolutional neural network integrated with interline power flow controller

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