Open AccessLoad Shedding in Microgrids with Dual Neural Networks and AHP Algorithm
Author(s) -
Le Thi Hong Nhung,
Trieu Tan Phung,
Nguyen Hoang Minh Vu,
Trong Nghia Le,
Thai An Nguyen,
T. D. Vo
Publication year - 2022
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.4652
Subject(s) - particle swarm optimization , artificial neural network , microgrid , analytic hierarchy process , computer science , fault (geology) , load shedding , dual (grammatical number) , electric power system , algorithm , generator (circuit theory) , power (physics) , mathematical optimization , engineering , artificial intelligence , mathematics , art , physics , control (management) , literature , operations research , quantum mechanics , seismology , geology
This paper proposes a new load shedding method based on the application of a Dual Neural Network (NN). The combination of a Back-Propagation Neural Network (BPNN) and of Particle Swarm Optimization (PSO) aims to quickly predict and propose a load shedding strategy when a fault occurs in the microgrid (MG) system. The PSO algorithm has the ability to search and compare multiple points, so the proposed NN training method helps determine the link weights faster and stronger. As a result, the proposed method saves training time and achieves higher accuracy. The Analytic Hierarchy Process (AHP) algorithm is applied to rank the loads based on their importance factor. The results of the ratings of the loads serve as a basis for constructing the load shedding strategies of a NN combined with the PSO algorithm (ANN-PSO). The proposed load shedding method is tested on an IEEE 25-bus 8-generator MG power system. The simulation results show that the frequency recovery of the power system is positive. The proposed neural network adapts well to the simulated data of the system and achieves high performance in fault prediction.