An Adaptive Neuro-Fuzzy Logic Controller for a Two Area Load Frequency Control

This paper presents the design and analysis of Neuro-Fuzzy controller based on Adaptive Neuro-Fuzzy inference system (ANFIS) architecture for Load frequency control of interconnected areas, to regulate the frequency deviation and power deviations. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to system collapse. This necessitates a very fast and accurate controller to maintain the nominal system frequency. This newly developed control strategy combines the advantage of neural networks and fuzzy inference system and has simple structure that is easy to implement. So, In order to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters near its optimum. This ANFIS replaces the original conventional proportional Integral (PI) controller and a fuzzy logic (FL) controller were also utilizes the same area criteria error input. The advantage of this controller is that it can handle the nonlinearities at the same time it is faster than other conventional controllers. Simulation results show that the performance of the proposed ANFIS based Neuro-Fuzzy controller damps out the frequency deviation and attains the steady state value with less settling time and reduces the overshoot of the different frequency deviations and also reduces the interchanged tie power.