Method Combining ANNs and Monte Carlo Simulation for the Selection of the Load Shedding Protection Strategies in Autonomous Power Systems

Abstract

This paper describes an efficient computational methodology that can be used for calculating the appropriate strategy for load shedding protection in autonomous power systems. It extends an existing method that is based on the sequential Monte Carlo simulation approach for comparing alternative strategies by taking into account the amount of load to be shed and the respective risk for the system stability. The extended methodology uses artificial neural networks (ANNs) for determining directly the parameters of the most appropriate load shedding protection strategy. For this purpose, the system inputs are the desirable probabilistic criteria concerning the system security or the amount of customer load interruptions. Using this methodology, the utility engineers can adopt a specific strategy that meets the respective utility criteria. The methodology was tested on a practical power system using a computer simulation for its operation, and the obtained results demonstrate its accuracy and the improved system performance