PSO-ANN approach for transient stability constrained economic power generation

This paper presents an approach to solve the online transient stability constrained power generation (TSCPG) by a mixture of a modified particle swarm optimization (PSO) and artificial neural network (ANN). This mixture (PSO-ANN) has been used as optimization tool to guarantee searching the optimal solution within the hyperspace reducing the time consumed in the computations and improving the quality of the selected solution. TSCPG is formulated as a nonlinear constrained optimization problem subject to load flow equations, power system capacity requirements and power system transient stability behavior. The critical clearing time (CCT) at the critical contingency is considered as an index for transient stability. The rescheduling process based on the generation companies (GENCOs)/consumer's bids is used as a remedial action to direct system operation in the direction of transient stability enhancement. The goal of the approach is to minimize the opportunity cost payments for GENCOs/consumers backed down in generation/consumption and the additional cost for GENCOs/consumers increased their generation/consumption in order to enhance system transient stability. The proposed approach provides a fast and accurate tool to evaluate continuous online adaptation for the power system operation to enhance system transient stability.