Load Shedding in Microgrid System with Combination of AHP Algorithm and Hybrid ANN-ACO Algorithm

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Load Shedding in Microgrid System with Combination of AHP Algorithm and Hybrid ANN-ACO Algorithm

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Quyen, Huy Anh / Trieu, Tan Phung / Le, Trong Nghia / Nguyen, Thai An / Huynh, Thi Ngoc Thuong

This paper proposes a new load shedding method based on the application of intelligent algorithms, the process of calculating and load shedding is carried out in two stages. Stage-1 uses a backpropagation neural network to classify faults in the system, thereby determining whether or not to shed the load in that particular case. Stage-2 uses an artificial neural network combined with an ant colony algorithm (ANN-ACO) to determine a load shedding strategy. The AHP algorithm is applied to propose load shedding strategies based on ranking the importance of loads in the system. The proposed method in the article helps to solve the integrated problem of load shedding, classifying the fault to determine whether or not to shedding the load and proposing a correct strategy for shedding the load. The IEEE 25-bus 8-generator power system is used to simulate and test the effectiveness of the proposed method, the results show that the frequency of recovery is good in the allowable range.