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.
S. Ghosh and B. H. Chowdhury, "Security-constrained Optimal Rescheduling of Real Power using Hopfield Neural Network," IEEE Transactions on Power Systems, Institute of Electrical and Electronics Engineers (IEEE), Jan 1996.
The definitive version is available at http://dx.doi.org/10.1109/59.544637
Electrical and Computer Engineering
Keywords and Phrases
Hopfield Neural Nets; Hopfield Neural Network; Active Line Flow Limits; Corrective Rescheduling; Differential Equations; Dual Linear Programming Formulation; Energy Function Transformation; Equality Constraint; Inequality Constraints; Linear Programming; Load Dispatching; Optimal Corrective Rescheduling; Power System Analysis Computing; Power System Security; Real Power; Real Power Generation; Real Power Generation Load Balance; Scheduling; Security-Constrained Optimal Rescheduling; Transmission Losses
International Standard Serial Number (ISSN)
Article - Journal
© 1996 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.