Power signal disturbance identification and classification using a modified frequency slice wavelet transform

Biswal, Birendra; Mishra, Sukumar

doi:10.1049/iet-gtd.2013.0171

Cited by 31 publications

(10 citation statements)

References 16 publications

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“…In [48] authors proposed Multi-Wavelet Transform (MWT) using MSD techniques working together with multiple neural networks for the classification of the transient disturbance type. In [49] authors proposed a Modified Frequency Slice WT (MFSWT) based PNN classifier for the detection and classification of non-stationary PQ disturbances. The WT based online methods of PQ disturbances detection were proposed in [50,51].…”

Section: Continuous and Discrete Wavelet Transforms For Feature Extramentioning

confidence: 99%

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Khokhar

Zin

Mokhtar

et al. 2015

Renewable and Sustainable Energy Reviews

222

111

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Section: Continuous and Discrete Wavelet Transforms For Feature Extramentioning

confidence: 99%

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Khokhar

Zin

Mokhtar

et al. 2015

Renewable and Sustainable Energy Reviews

222

111

View full text Add to dashboard Cite

“…Further, a digital-to-analog converter is used to construct an antinoise signal. Gomez-Luna [28]- [30] have presented the application of the modified Morlet wavelet for the transient analysis of switching transformers and other power electronic devices. The wavelet transform-based approach is used for filter response analysis of switching transformer [28], [29].…”

Section: Active Noise Cancellationmentioning

confidence: 99%

Wavelet Transform-Based <sc>EMI</sc> Noise Mitigation in Power Converter Topologies

Alam

Mukul

Thakura

2016

IEEE Trans. Electromagn. Compat.

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This paper proposes a novel wavelet transform-based approach for electromagnetic interference (EMI) noise mitigation in power electronics topologies. The switching action of power semiconductor devices generates EMI in power converter modules which exceeds the noise limit set by the regulation of CISPR11. In order to cancel the EMI noise, the wavelet transform-based timefrequency localization approach has been used for generating a noise cancellation signal. The effectiveness of the proposed method has been evaluated in the terms of signal-to-noise ratio (SNR). Further, the effect of variance of the noise amplitude has been taken into consideration on the SNR. A relationship between the standard deviation, which is proportional to square root of the variance of the signal, and the SNR has been established. The proposed method has been implemented on the different power electronics topologies such as buck converter, boost converter, three-phase voltage source inverter, and Z-source inverter-based ac drive. The proposed method achieves the reduction of noise level below the safe limit set by CISPR11.Index Terms-Boost converter, buck converter, EMI, morlet wavelet, signal-to-noise ratio, three-phase voltage source inverter, wavelet transform, Z-source inverter.

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“…To overcome the drawbacks of both DFT and FFT, wavelet transform (WT) has been broadly utilized, since the WT can dissect the different PQ events at the same time in both time and frequency domains. 7 , 8 Still, it has some drawbacks like excessive computation, sensitivity to noise level and the dependency of its accuracy on the selected wavelet.…”

Section: Introductionmentioning

confidence: 99%

A new protection scheme for PQ events prediction and classification in distribution system: A SSO-ANN strategy

Karthikumar

Kumar

Muneeswaran

2019

Measurement and Control

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Increased utilization of nonlinear loads and fault event on the power system have resulted in a decline in the quality of power provided to the customers. It is fundamental to recognize and distinguish the power quality disturbances in the distribution system. To recognize and distinguish the power quality disturbances, the development of high protection schemes is required. This paper presents an optimal protection scheme for power quality event prediction and classification in the distribution system. The proposed protection scheme combines the performance of both the salp swarm optimization and artificial neural network. Here, artificial neural network is utilized in two phases with the objective function of prediction and classification of the power quality events. The first phase is utilized for recognizing the healthy or unhealthy condition of the system under various situations. Artificial neural network is utilized to perceive the system signal’s healthy or unhealthy condition under different circumstances. In the second phase, artificial neural network performs the classification of the unhealthy signals to recognize the right power quality event for assurance. In this phase, the artificial neural network learning method is enhanced by utilizing salp swarm optimization based on the minimum error objective function. The proposed method performs an assessment procedure to secure the system and classify the optimal power quality event which occurs in the distribution system. At that point, the proposed work is executed in the MATLAB/Simulink platform and the performance of the proposed system is compared with different existing techniques like Multiple Signal Classification-Artificial Neural Network (MUSIC-ANN), and Genetic Algorithm - Artificial Neural Network (GA-ANN). The comparison results demonstrate the superiority of the SSO-ANN technique and confirm its potential to power quality event prediction and classification.

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Power signal disturbance identification and classification using a modified frequency slice wavelet transform

Cited by 31 publications

References 16 publications

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Wavelet Transform-Based <sc>EMI</sc> Noise Mitigation in Power Converter Topologies

A new protection scheme for PQ events prediction and classification in distribution system: A SSO-ANN strategy

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