A hybrid MFO‐GHNN tuned self‐adaptive FOPID controller for ALFC of renewable energy integrated hybrid power system

Abstract

This paper proposes a hybrid moth flame optimization–generalized Hopfield neural network (MFO-GHNN) optimized self-adaptive fractional order proportional integral derivative (FOPID) controller for automatic load frequency control of multi-area hybrid power system (HPS). The control problem is formulated with an objective function of area control error associated with unknown parameters such as Kp, Ki, Kd, λ and μ of FOPID controller. The fractional order of differentiator and integrator terms, and the initial values of Kp, Ki and Kd, are drawn from MFO algorithm. Then, the Kp, Ki and Kd are fine-tuned by solving the dynamic equations governing the behaviour of GHNN under system uncertainties. To test the practicability and effectiveness of the proposed controller, the multi-area HPS is studied with uncertain change in load demand, system parameters, solar and wind power generation. The proposed method is modelled using MATLAB/Simulink. The results showed that the steady state and transient performance indices of proposed FOPID controller are significantly enhanced than the PID, MFO-FOPID and GHNN-PID controllers. In addition, the stability of non-linear dynamic HPS is analysed using Matignon's theorem of stability. Further, the performance of controller is validated using real time digital simulator run in hardware-in-the loop environment.