Frequency Regulation of Nonlinear Power Systems using Neural Network Observer-Based Optimized Resilient Controller

This study introduces a resilient frequency controller for nonlinear interconnected power systems to counteract endogenous/exogenous system disturbances. A neural network-based observer (NNO) is intended estimate lumped disturbances, such as unmodelled dynamics and unknown The estimated NNO’s output incorporated with second-order sliding mode (SOSMC) minimize chattering in the control effort improve nominal performance of undertaken plant. design parameters SOSMC have been optimally identified by applying Harris hawk optimization (HHO), exercising integral error-based objective function. HHO has demonstrated superior tuning capabilities than other well-known methodologies terms convergence rate transient measurements outputs. asymptotic error overall stability established employing Lyapunov argument. System outputs are compared results reported literature validate efficacy proposed controller. Presented showcase mastery applied NNO-based over its counterparts weaker chattering, fast disturbance rejection, high degree robustness against