Development of an artificial neural network-based method for determining the flexibility of power systems with high share of wind generation

Abstract

   A method for the online determination of the resilience of an electric power system using artificial neural networks having various structures is presented. A developed algorithm comprised of an artificial neural network with multiple learning paradigms is used for the rapid calculation of the adaptability index of the electric power system. A satisfactory time for obtaining results is ensured by dividing the adaptability calculation into offline and online processes. To train the neural networks, various methods were used. The multilayer perceptron was trained using the method of back-ward propagation of error, while training of the Kohonen neural network was performed based on the winner-take-all rule. Euclidean distance was used as a proximity measure between the studied vectors. An algorithm for analysing the results obtained by two types of artificial neural networks having dissimilar structures was developed in order to select their optimal structure and recommend a neural network for the real-time determination of the resilience of an electric power system. The proposed algorithm was validated on a 6-node scheme following the command script: computing the resilience of a given system, functioning in multiple modes. The criterion analysis showed that the structures of multilayer perceptron having 16 neurons in a hidden layer and Kohonen neural network having 9 output neurons represent the optimal solution for determining the steady-state mode at the minimum resilience in real-time. According to the results, the value of the resilience of the system varies over the course of a day. The possibility of using artificial neural networks for determining the resilience of electric power systems in real-time is demonstrated.