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A novel generalised Hopfield neural network (GHNN) based self-adaptive proportional-integral-derivative (PID) controller for load frequency control (LFC) is designed for a two-area interconnected power system with nonlinearities of generator rate constraint and governor dead band. The control problem is conceptualised as an optimisation problem with an objective function as an area control error in terms of the PID controller parameters. The differential equations governing the behaviour of the GHNN were solved to obtain the controller parameters Kp, Ki and Kd. To test the feasibility and robustness of the proposed controller, the system is tested in the presence of randomness in load demands, imprecisely modelled system dynamics, nonlinearities in the system model and uncertainties in the system parameter variations. The proposed method is simulated using Matlab R2014b/Simulink and the results obtained have shown that the propounded controller performance is superior to the integral, PID and fuzzy-based proportional-integral controllers. In addition, the Lyapunov stability analysis of the overall closed-loop system was carried out and the controller is implemented in real-time digital simulator run in hardware-in-theloop to validate the effectiveness of the proposed method. Furthermore, the proposed controller is applied to the three-area power system to test its adaptability. [ABSTRACT FROM AUTHOR]

This paper presents a novel Runge–Kutta (RK4) based modified hopfield neural network (MHNN) for solving a set of non-linear transcendental power flow equations of power system. The proffered method is a Lyapunov based energy function approach to minimize real and reactive power mismatches of the system. A set of non-linear differential equations derived from energy function, describing the dynamical behavior of HNN is framed for solving Power Flow equations. These dynamic equations of the network are solved by RK4 method to deduce the unknown variables of the system. The feasibility of proposed method is tested on 5-bus, IEEE 14-bus, 39-bus and 57-bus test system. The analytical equation describing the behavior of MHNN is coded in MATLAB software. The results obtained reveal that the suggested method gives accurate solution and reduces the computational complexity than conventional Newton Raphson (NR) method. The sensitivity analysis is also tested for change in R/X ratio of the system, initial conditions and loading of the system. The proposed method is robust for above specified changes and involves less computational effort. To prove the applicability and consistency of projected method, IEEE 118-bus system has been tested. The power flow solutions found through proffered method are compared with solutions obtained from numerical approaches in order to validate the proposed approach. Moreover, the stability of the system is studied in Lyapunov sense of notion which assures converged solution of proposed method. • A Numerical RK4 based HNN for solving power flow equations of power system is developed. • Reduced computational complexity and system memory as it bypasses Jacobian formation and its inversion. • The proposed method is validated for IEEE 5, 14, 39, 57 and 118-bus test system. • Global optimal solution of each power network is obtained by solving the dynamics of HNN using RK4. • Stability of solution using MHNN is ensured by Lyapunov sense of notion. [ABSTRACT FROM AUTHOR]