Using an improved Neural Network with Bacterial Foraging Optimization algorithm for Load Shedding

Making accurate load-shedding decisions helps to reduce losses for customers and the power system. This article proposes an improved Artificial Neural Network (ANN) application using the Bacteria Foraging optimization (BFO) algorithm. The proposed load-shedding model uses ANN to identify load-sheddi...

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Bibliographic Details
Published in2023 International Conference on System Science and Engineering (ICSSE) pp. 281 - 286
Main Authors Nguyen, Hoang Minh Vu, Phung, Trieu Tan, Le, Trong Nghia, Nguyen, Ngoc Au, Nguyen, Quang Tien, Nguyen, Phuong Nam
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.07.2023
Subjects
Artificial neural networks
Bacteria Foraging optimization
Load forecasting
Load shedding
Machine Learning
Microorganisms
Power system stability
Stability analysis
Training
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Summary:Making accurate load-shedding decisions helps to reduce losses for customers and the power system. This article proposes an improved Artificial Neural Network (ANN) application using the Bacteria Foraging optimization (BFO) algorithm. The proposed load-shedding model uses ANN to identify load-shedding/non-shedding events combined with optimized load-shedding calculations to ensure system stability within allowable limits. The proposed neural network has been experimented on the IEEE 37-bus system, and its performance is compared with conventional neural networks and those improved by other algorithms. The results show that BFO provides high data identification efficiency with minimal training time, making it a potential solution for load-shedding forecasting.
ISSN:2325-0925
DOI:10.1109/ICSSE58758.2023.10227214