Optimal Electric-Power Distribution and Load-Sharing on Smart-Grids: Analysis by Artificial Neural Network
This study refers to developing an electric-power distribution system with optimal/suboptimal load-sharing in the complex and expanding metro power-grid infrastructure. That is, the relevant exercise is to indicate a smart forecasting strategy on optimal/suboptimal power-distribution to consumers served by a smart-grid utility. An artificial neural network (ANN) is employed to model the said optimal power-distribution between generating sources and distribution centers. A compatible architecture of the test ANN with ad hoc suites of training/prediction schedules is indicated thereof. Pertinent exercise is to determine smartly the power supported on each transmission-line between generating to distribution-nodes. Further, a “smart” decision protocol prescribing the constraint that no transmission-line carries in excess of a desired load. An algorithm is developed to implement the prescribed constraint via the test ANN; and, each value of the load shared by each distribution-line (meeting the power-demand of the consumers) is elucidated from the ANN output. The test ANN includes the use of a traditional multilayer architecture with feed-forward and backpropagation techniques; and, a fast convergence algorithm (deduced in terms of eigenvalues of a Hessian matrix associated with the input data) is adopted. Further, a novel method based on information-theoretic heuristics (in Shannon’s sense) is invoked towards model specifications. Lastly, the study results are discussed with exemplified computations using appropriate field data.
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