Adaptive neuro-fuzzy inference system based under-frequency load shedding for Tamil Nadu

Abstract

Under-frequency load shedding (UFLS) is a popular technique around the world, especially in India for maximizing the power system stability and to avoid blackout in case of contingency. Electricity demand–supply gap due to generation failure is the main cause for power shortage, which leads to frequency degradation and major collapse of the whole system. Load shedding helps utility to restore the system stability. But the traditional load shedding methods shed incorrect as well as excess load which increases economic loss to utility and discomfort to customers. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) based UFLS controller using back-propagation and least-square estimation algorithm is simulated and tested for Tamil Nadu power system, a leading industrialized state of India with an objective to shed minimum possible load. The simulations are carried out using MATLAB toolbox. The performance of the proposed ANFIS based UFLS controller is compared with an artificial neural network (ANN) based controller. The comparison of ANN and ANFIS based UFLS shows that by using the proposed ANFIS controller, the amount of load shed could be reduced between 83 and 1264 MW for various generation–load scenarios, which significantly benefits both utility and customers to enhance energy security and revenue up to 7.5 million INR per hour.