Clamped state solution of artificial neural network for real-time economic dispatch

This paper presents a novel method for the real-time economic dispatch using clamped state variable formulation of the Kennedy, Chua and Lin (1988) artificial neural network (ANN). An efficient economic power dispatch algorithm must use real-time load conditions and the loss penalty-factor for representation of transmission losses in power system. The approach described in this paper assumes that an interface program will calculate the penalty factors for the current power flow state, as calculated by a state estimation program. The proposed method employs an ANN to enhance the speed and capability of algorithms which may use heuristics for online use. The ability of processing feedbacks in a collective parallel analog mode enables a neural network to simulate the dynamics that represent the optimization of an objective function subjected to its constraints for a given optimization model. Different techniques may be used to simulate the neural dynamic system. In this study, the authors propose a new method of simulation called the clamped state variable (CSV) technique. The new approach is very simple and it takes smaller computer time for the algorithm to converge than the complete circuit simulation. The results obtained by the CSV method are very close to that of numerical methods and are reported in this paper