Four-Area Load Frequency Control of an Interconnected Power System Using Neuro-Fuzzy Hybrid Intelligent Proportional and Integral Control Approach

Abstract

This article presents a novel control approach, hybrid neuro-fuzzy (HNF), for the load frequency control (LFC) of a four-area interconnected power system. The advantage of this controller is that it can handle nonlinearities, and at the same time, it is faster than other existing controllers. The effectiveness of the proposed controller in increasing the damping of local and inter-area modes of oscillation is demonstrated in a four-area interconnected power system. Areas 1 and 2 consist of a thermal reheat power plant, whereas Areas 3 and 4 consist of a hydropower plant. Performance evaluation is carried out by using fuzzy, artificial neural network (ANN), adaptive neuro-fuzzy inference system, and conventional proportional and integral (PI) control approaches. Four different models with different controllers are developed and simulated, and performance evaluations are carried out with said controllers. The result shows that the intelligent HNF controller has improved dynamic response and is at the same time faster than ANN, fuzzy, and conventional PI controllers.

Keywords

• load frequency control (lfc)
• adaptive neuro-fuzzy inference system (anfis)
• artificial neural network (ann)
• fuzzy
• proportional and integral (pi) controllers
• area control error (ace)
• tie-line