Security-constrained Optimal Rescheduling of Real Power using Hopfield Neural Network
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Abstract
A new method for security-constrained corrective rescheduling of real power using the Hopfield neural network is presented. The proposed method is based on solution of a set of differential equations obtained from transformation of an energy function. Results from this work are compared with the results from a method based on dual linear programming formulation of the optimal corrective rescheduling. The minimum deviations in real power generations and loads at buses are combined to form the objective function for optimization. Inclusion of inequality constraints on active line flow limits and equality constraint on real power generation load balance assures a solution representing a secure system. Transmission losses are also taken into account in the constraint function.
