Elman-Recurrent Neural Network For Load Shedding Optimization

widi aribowo

Abstract


Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity lead to spread of a power system into sub-systems and lead to the outage as well as the severe breakdown of the system utility.  in recent years, Neural networks have been very victorious in a number of signal processing and control applications.  Neural networks are capable of handling complex and non-linear problems. This paper provides an algorithm for load shedding using ELMAN Recurrent Neural Networks (RNN). Recurrent neural network goals to loads shed optimally. Elman-recurrent neural network (RNN) has the advantage of dependence not only on current input but also on past operations. Work is implemented in MATLAB and performance is tested with a 6 bus system. The results are compared with the Genetic Algorithm (GA) and RNN. The RNN method is capable of assigning load releases needed and more efficient than other methods.


Keywords


Load shedding,power stability,genetic algorithm, recurrent neural networks, elman network

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