On-line emission and economic load dispatch using adaptive Hopfield neural network |
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Authors: | S. Balakrishnan P. S. Kannan C. Aravindan P. Subathra |
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Affiliation: | a Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, Tamil Nadu, India;b Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai 625014, Tamil Nadu, India;c Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, Tamil Nadu, India |
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Abstract: | This paper presents an adaptive Hopfield neural network (AHNN) based methodology, where the slope of the activation function is adjusted, for finding approximate Pareto solutions for the multi-objective optimization problem of emission and economic load dispatch (EELD). We have placed emphasis on finding solutions quickly, rather than the global Pareto solutions, so that our algorithm can be employed in large on-line power systems where variations in load are quite frequent. To enable faster convergence, adaptive learning rates have been developed by using energy function and applied to the slope adjustment method. The proposed algorithm has been tested on selected IEEE bus benchmark systems. The convergence of AHNN is found to be nearly 50% faster than the non-adaptive version. |
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Keywords: | Multi-objective optimization Adaptive Hopfield neural network Emission and economic load dispatch Pareto solutions |
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