Accordingly, the reliable protection of MGs considering uncertainty in RESs is crucial for planners and operators. This paper uses data analysis to extract knowledge from locally available...
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Abstract— Fault diagnosis is essential to attain the microgrid''s stable, reliable, and economical operation. However, a microgrid''s fault current varies drastically based on the connected
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In this context, the application of machine learning techniques has shown promise in enhancing the accuracy of fault detection and classification in MGs. A critical component of this
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Reviews microgrid architecture, key components, and control strategies. Highlights various AI models along with their challenges and advantages. Presents AI applications in sizing, control,
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A novel multi-layer cybersecurity framework that combines blockchain authentication, AI-driven anomaly detection, and system self-healing for smart microgrids. Real-time threat mitigation
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The objective of this paper is to develop an anomaly detection framework for the smart microgrid system at MCAS Miramar to enhance its cyber-resilience. We implement predictive
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The traditional methods for detection of faults in microgrid have faced significant challenges like inability to handle various fault scenarios. Therefore, this research proposes modified dragonfly
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We implement predictive analytics using machine learning to deal with cyber-uncertainties and threats within the microgrid environment. An autoencoder neural network is implemented to classify and
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