Differential Fault Detection Scheme for Islanded AC Microgrids Using Digital Signal Processing and Machine Learning Techniques

Arunan, Anusuya; Ravishankar, Jayashri; Ambikairajah, Eliathamby

doi:10.1109/icpes47639.2019.9105433

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…An ML-based secondary-layer CVC framework [247] predicts optimal active and reactive power from each DER to restore voltage. In another approach, SVM-based fault detection [248] measures voltage and current at each selected point to accurately locate the fault section. Another tree-based ML model [249] has been proposed to measure voltage and current signals at each feeder to identify faulty events and alert the control system.…”

Section: Exploring Ai-based Research Methodologies For Microgrid Controlmentioning

confidence: 99%

Exploring the Potential of Microgrids in the Effective Utilisation of Renewable Energy: A Comprehensive Analysis of Evolving Themes and Future Priorities Using Main Path Analysis

Pillai

Valsala

Raj

et al. 2023

Designs

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Microgrids are energy systems that can operate independently or in conjunction with the main electricity grid. Their purpose is to link different energy sources, enhance customer participation in energy markets, and improve energy system efficiency and flexibility. However, regulatory, technical, and financial obstacles hinder their deployment. To comprehend the current state of the field, this study utilized citation network analysis (CNA) methodology to examine over 1500 scholarly publications on microgrid research and development (R&D). The study employed modularity-based clustering analysis, which identified seven distinct research clusters, each related to a specific area of study. Cluster 1, focused on control strategies for microgrids, had the highest proportion of publications (23%) and the maximum citation link count (151), while Cluster 4, which examined microgrid stability, had the lowest proportion of papers (10%). On average, each publication within each cluster had four citation links. The citation network of microgrid research was partitioned using cluster analysis, which aided in identifying the main evolutionary paths of each subfield. This allowed for the precise tracing of their evolution, ultimately pinpointing emerging fronts and challenges. The identification of key pathways led to the discovery of significant studies and emerging patterns, highlighting research priorities in the field of microgrids. The study also revealed several research gaps and concerns, such as the need for further investigation into technical and economic feasibility, legislation, and standardization of microgrid technology. Overall, this study provides a comprehensive understanding of the evolution of microgrid research and identifies potential directions for future research.

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Section: Exploring Ai-based Research Methodologies For Microgrid Controlmentioning

confidence: 99%

Exploring the Potential of Microgrids in the Effective Utilisation of Renewable Energy: A Comprehensive Analysis of Evolving Themes and Future Priorities Using Main Path Analysis

Pillai

Valsala

Raj

et al. 2023

Designs

View full text Add to dashboard Cite

show abstract

“…Afterwards, these features are applied to extract differential features to be used for creating a tree-based ML model. Fast Fourier transform and SVM have been applied in [42] for fault detection in islanded MGs based on differential protection. The work reported in [43] uses an ensemble classifier to perform fault detection and classification as well as to locate the fault in a PV-integrated MG.…”

Section: ) Lack Of Sensitivity and Selectivitymentioning

confidence: 99%

Machine learning for protection of distribution networks and power electronics-interfaced systems

Aminifar

Teimourzadeh²,

Shahsavari

et al. 2021

The Electricity Journal

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Machine Learning k-Means Cluster Support S-FSCV Algorithm to Estimate Integrated Network Operating State

Reddy

2022

Arab J Sci Eng

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Differential Fault Detection Scheme for Islanded AC Microgrids Using Digital Signal Processing and Machine Learning Techniques

Cited by 5 publications

References 13 publications

Exploring the Potential of Microgrids in the Effective Utilisation of Renewable Energy: A Comprehensive Analysis of Evolving Themes and Future Priorities Using Main Path Analysis

Exploring the Potential of Microgrids in the Effective Utilisation of Renewable Energy: A Comprehensive Analysis of Evolving Themes and Future Priorities Using Main Path Analysis

Machine learning for protection of distribution networks and power electronics-interfaced systems

Machine Learning k-Means Cluster Support S-FSCV Algorithm to Estimate Integrated Network Operating State

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