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• W2772110297 abstract "This study proposes a generalised Hopfield neural network (GHNN) for solving non-linear load flow equations. The proposed method was formulated with appropriate energy function for performing load flow analysis of n -bus system. The intended method has the advantages of simple to use, more general application, faster convergence and better optimal solution over the conventional method of load flow using Newton–Raphson (NR) technique. The proposed method of GHNN has been used to solve the power flow equation by calculating the power mismatches and this constraint is used to formulate the energy function of Hopfield neural network (HNN). This energy function is used to derive the weights and bias values of the network. The optimal solution can be found, based on the minimisation of the energy function of continuous HNN. The suggested method was tested in a typical 3-bus and 5-bus power system. The mathematical equation of the proposed method was coded using Matlab/R2014a software. The simulation results obtained have shown that the proposed method is more efficient than NR method in terms of reduction in computational complexity and convergence time with minimum number of iterations." @default.
• W2772110297 created "2017-12-22" @default.
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• W2772110297 date "2018-02-07" @default.
• W2772110297 modified "2023-09-27" @default.
• W2772110297 title "Load flow analysis using generalised Hopfield neural network" @default.
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• W2772110297 doi "https://doi.org/10.1049/iet-gtd.2017.1211" @default.
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