Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration
Gajendra Singh Chawda,Abdul Gafoor Shaik,Mahmood Shaik,Sanjeevikumar Padmanaban,Jens Bo Holm-Nielsen,Om Prakash Mahela,Palanisamy Kaliannan +6 more
TLDR
A critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration is presented, to provide various concepts utilized for extraction of the features to detect and classify the P Q disturbances even in the noisy environment.Abstract:
The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.read more
Citations
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Automated classification of power quality disturbances in a SOFC&PV-based distributed generator using a hybrid machine learning method with high noise immunity
TL;DR: In this article , a new hybrid machine learning (ML) method is developed to classify the power quality disturbances (PQDs) for a hydrogen energy-based distributed generator (DG) system.
Journal ArticleDOI
Measuring Explainability and Trustworthiness of Power Quality Disturbances Classifiers Using XAI—Explainable Artificial Intelligence
TL;DR: In this paper , a method that explains the outputs of power quality disturbance (PQD) classifiers using explainable artificial intelligence (XAI) has been proposed, and the best combination of classifier and XAI techniques for each disturbance is used on the testing set, such that the classifier outputs are more transparent.
Journal ArticleDOI
Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring
TL;DR: In this article, a focus is placed on power quality monitoring using advanced signal processing and machine learning approaches for disturbances characterization, such as wiring issues, grounding, switching transients, load variations, and harmonics generation.
Peer Review
Current Status and Future Trends of Power Quality Analysis
TL;DR: A systematic literature review of 153 articles on power quality analysis in PV systems published in the last 20 years is presented in this paper , which provides readers with an overview on PQ trends in several fields related to instrumental techniques that are being used in the smart grid to visualize the quality of the energy, establishing a solid literature base from which to start future research.
Journal ArticleDOI
Current Status and Future Trends of Power Quality Analysis
TL;DR: A systematic literature review of 153 articles on power quality analysis in PV systems published in the last 20 years is presented in this article , which provides readers with an overview on PQ trends in several fields related to instrumental techniques that are being used in the smart grid to visualize the quality of the energy.
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