Load–frequency control of interconnected power system using emotional learning-based intelligent controller

In this paper a novel approach based on the emotional learning is proposed for improving the load–frequency control (LFC) system of a two-area interconnected power system with the consideration of generation rate constraint (GRC). The controller includes a neuro-fuzzy system with power error and its derivative as inputs. A fuzzy critic evaluates the present situation, and provides the emotional signal (stress). The controller modifies its characteristics so that the critic’s stress is reduced.The convergence and performance of the proposed controller, both in presence and absence of GRC, are compared with those of proportional integral (PI), fuzzy logic (FL), and hybrid neuro-fuzzy (HNF) controllers.

► An emotional learning-based approach for load-frequency control is presented.
► A two-area interconnected power system with the consideration of GRC is analyzed.
► The obtained results are compared with those of PI, FL, and HNF controllers.
► The proposed controller has the ability to faster damp the frequency oscillations.