Multiple-deme parallel genetic algorithm based on modular neural network for effective load shedding

One of the most effective corrective control strategies to prevent voltage collapse and instability is load shedding. In this paper, a multiple-deme parallel genetic algorithm is used for a suitable design of load shedding. The load shedding algorithm is implemented when the voltage stability margin index of the power system is lower than a predefined value. In order to increase the computational speed, the voltage stability margin index is estimated by a modular neural network method in a fraction of a second. In addition, in order to use the exact values of the voltage stability margin index for neural network training, a simultaneous equilibrium tracing technique has been employed considering the detailed model of the components of the generating units such as the governor and the excitation system. In the proposed algorithm, the entire population is partitioned into several isolated subpopulations (demes) in which demes distributed in different processors and individuals may migrate occasionally from one subpopulation to another. The proposed technique has been tested on New England-39 bus test system, and the obtained results indicate the efficiency of the proposed method.

Automated summary

The paper introduces a multiple-deme parallel genetic algorithm for load shedding control to prevent voltage collapse and instability. A modular neural network method is implemented to estimate the voltage stability margin index, and a simultaneous equilibrium tracing technique is employed to consider the detailed model of generator components. Test results on the New England-39 bus test system show the efficiency of the proposed method.