Optimal design of adaptive under frequency load shedding using artificial neural networks in isolated power system

Abstract

The frequency and voltage stability is a basic principle in the power system operation. Different short circuits, load growth, generation shortage, and other faults which disturb the voltage and frequency stability are serious threats to the system security. The frequency and voltage instability causes dispersal of a power system into sub-systems, and leads to blackout as well as heavy damages of the system equipments. Optimum load shedding during contingency situations is one of the most important issues in power system security analysis. This paper presents a fast and optimal adaptive load shedding method, for isolated power system using Artificial Neural Networks (ANNs). By creating an appropriate data-base of contingencies for training the neural network, the proposed method is able to perform correct load shedding in various loading scenario. In this regard, the total power generation, the total loads in power system, the existing spinning reserved capacity value in the network and frequency reduction rate were selected as the ANN inputs. This method has been tested on the New-England power system. The simulation results show that the proposed algorithm is very fast, robust and optimal values of load shedding in different loading scenarios, related to conventional method.