A large number of iterations and oscillation are those of the major concern in solving the economic load dispatch problem using the Hopfield neural network. This paper develops two different methods, which are the slope adjustment and bias adjustment methods, in order to speed up the convergence of the Hopfield neural network system. Algorithms of economic load dispatch for piecewise quadratic cost functions using the Hopfield neural network have been developed for the two approaches. The results are compared with those of a numerical approach and the traditional Hopfield neural network approach. To guarantee and for faster convergence, adaptive learning rates are also developed by using energy functions and applied to the slope and bias adjustment methods. The results of the traditional, fured learning rate, and adaptive learning rate methods are compared in economic load dispatch problem.
This paper compares the shunt capacitor, SVC and STATCOM in static voltage stability improvement. Various performance measures are compared under different operating system conditions for the IEEE 14 bus test system. Important issues related to shunt compensation, namely sizing and installation location, for exclusive load margin improvement are addressed. A methodology is also proposed to alleviate voltage control problems due to shunt capacitor compensation during lightly and heavily loaded conditions.
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