Smart Grid Management System Based on Machine Learning Algorithms for Efficient Energy Distribution

Reddy, V. Sandeep Kumar; T., Saravanan; Velusudha, N.T.; Selwyn, T. Sunder

doi:10.1051/e3sconf/202338702005

Cited by 12 publications

(1 citation statement)

References 8 publications

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“…The AHBFS-ODLSP approach performs best with a 99% F score. Reddy et al [22], neural networks, decision trees (DTs), and SVM were utilized in smart grid management systems and performed well. Maizana et al [23] carried out a stability investigation of the smart grid management system on the campus building.…”

Section: Introductionmentioning

confidence: 99%

An efficient smart grid stability prediction system based on machine learning and deep learning fusion model

Lakshmanarao,

Srisaila,

Srinivasa Ravi Kiran

et al. 2024

IJEECS

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<span>A smart grid is a modern power system that allows for bidirectional communication, driven mostly by the idea of demand responsiveness. Predicting the stability of the smart grid is necessary for improving its dependability and maximizing the efficacy and regularity of electricity delivery. Predicting smart grid stability is difficult owing to the various elements that impact it, including consumer and producer engagement, which may contribute to smart grid stability. This research work proposes machine learning (ML) and deep learning (DL) approaches for predicting smart grid sustainability. Five ML algorithms, namely support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and logistic regression (LR), were applied for the prediction of smart grid stability. Later, the stacking ensemble and voting ensemble of ML algorithms were also applied for prediction. To further increase accuracy, a novel fusion model with DL artifical neural networks (ANN) and ML SVM was applied and achieved an accuracy of 98.92%. The experiment results show that the proposed model outperformed existing models for smart grid stability prediction.</span>

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Section: Introductionmentioning

confidence: 99%

An efficient smart grid stability prediction system based on machine learning and deep learning fusion model

Lakshmanarao,

Srisaila,

Srinivasa Ravi Kiran

et al. 2024

IJEECS

View full text Add to dashboard Cite

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Grid-Forming Converters: Stability and Control in Islanded Power Systems

Revathi,

Sasikala,

Geetha

et al. 2024

E3S Web of Conf.

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This review paper delves into the burgeoning field of Grid-Forming (GFM) Converters, particularly focusing on their role in ensuring stability and control in islanded power systems. The paper reviews work in three broad areas. The first area explores the various control schemes for GFM converters, offering a generalized control structure and comparing different solutions for each of the main subsystems. It also discusses open issues such as angle stability, fault ride-through capabilities, and the transition between islanded and grid-connected modes. The second area of study scrutinizes the challenges posed by low-inertia grids, which have become increasingly prevalent due to the integration of renewable energy sources. It evaluates the performance of GFM converters in the context of both small-signal and large-signal stability, filling a gap in the literature by offering a comparative study of various GFM control schemes for largesignal stability. The paper concludes by outlining the challenges and future trends in the application of GFM converters.

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A Review of Smart Grid Management Systems Using Machine Learning Algorithms for Efficient Energy Distribution

Aggarwal,

Kalpana

et al. 2024

E3S Web of Conf.

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The smart grid is an intelligent electricity network that uses digital technology to improve the efficiency, reliability, and sustainability of power delivery. Machine learning is a type of artificial intelligence that can be used to analyze data and learn from it. This makes it a valuable tool for the smart grid, as it can be used to solve a variety of problems, such as⸻forecasting energy demand, detecting, and preventing outages, optimizing power flows, managing distributed energy resources, ensuring grid security.In this article, we will review the use of machine learning in the smart grid. We will discuss the different machine learning algorithms that are being used, the challenges that need to be addressed, and the future of machine learning in the smart grid..

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Smart Grid Management System Based on Machine Learning Algorithms for Efficient Energy Distribution

Cited by 12 publications

References 8 publications

An efficient smart grid stability prediction system based on machine learning and deep learning fusion model

An efficient smart grid stability prediction system based on machine learning and deep learning fusion model

Grid-Forming Converters: Stability and Control in Islanded Power Systems

A Review of Smart Grid Management Systems Using Machine Learning Algorithms for Efficient Energy Distribution

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