Analysis of Intelligent Machine Learning Techniques for the Protection of AC Microgrid

被引:0
作者
Singh, Mukul [1 ]
Singh, Omveer [1 ]
Ansari, M. A. [1 ]
机构
[1] Gautam Buddha Univ, Sch Engn, Dept Elect Engn, Gautam Budh Nagar 201312, UP, India
关键词
Machine Learning (ML); Random Forests (RF); Support Vector Machines (SVM); Gradient Boosting (GB); Logistic Regression (LR); K-Nearest Neighbors (K-NN); Artificial Neural Network (ANN); Renewable Energy Sources (RES); Distributed Energy Generation (DEG); Energy Storage Systems (ESS); Recursive Feature Elimination (RFE); Solid Oxide Fuel Cell (SOFC); SELECTION;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The rapid integration of renewable energy sources into the power grid has necessitated the development of intricate protection techniques to ensure the stability and reliability of AC microgrids. This research paper examines the advancement and comparative assessment of intelligent machine learning (ML) based protection strategies for AC microgrids. Five well-known machine Neighbours (K-NN) are assessed for their effectiveness in predicting important parameters such as voltage, current, and power in different energy components. These components include batteries, grids, photovoltaic (PV) systems, solid oxide fuel cells (SOFCs), gearbox systems, and wind energy systems. The research utilizes performance criteria, including accuracy, precision, recall, F1-score, macro average, and weighted average, to determine the most efficient models for improving microgrid safety. The results emphasize that the K-NN model is the most resilient, with Gradient Boosting and Random Forest models following closely behind. On the other hand, SVM and Logistic Regression models demonstrate poorer performance, indicating their limited usefulness in intricate energy systems.
引用
收藏
页码:1482 / 1498
页数:17
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