Hybrid Neuro-Fuzzy controller based Adaptive Neuro-Fuzzy Inference System Approach for Multi-Area Load Frequency Control of Interconnected Power System

This paper concentrated on the design and analysis of Neuro-Fuzzy controller based 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. So there is necessity robust controller required to maintain the nominal system frequency. The proposed ANFIS controller combines the advantages of fuzzy controller as well as quick response and adaptability nature of artificial neural network however the control technology implemented with sugeno rule to obtain the optimum performance. 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 nonlinarites 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 reduces the overshoot of the different frequency deviations.