A novel artificial intelligence based multistage controller for load frequency control in power systems

The imbalance between generated power and load demand often causes unwanted fluctuations in the frequency and tie-line power changes within a power system. To address this issue, a control process known as load frequency control (LFC) is essential. This study aims to optimize the parameters of the LFC controller for a two-area power system that includes a reheat thermal generator and a photovoltaic (PV) power plant. An innovative multi-stage TDn(1 + PI) controller is introduced to reduce the oscillations in frequency and tie-line power changes. This controller combines a tilt-derivative with an N filter (TDn) with a proportional-integral (PI) controller, which improves the system's response by correcting both steady-state errors and the rate of change. This design enhances the stability and speed of dynamic control systems. A new meta-heuristic optimization technique called bio-dynamic grasshopper optimization algorithm (BDGOA) is used for the first time to fine-tune the parameters of the proposed controller and improve its performance. The effectiveness of the controller is evaluated under various load demands, parameter variations, and nonlinearities. Comparisons with other controllers and optimization algorithms show that the BDGOA-TDn(1 + PI) controller significantly reduces overshoot in system frequency and tie-line power changes and achieves faster settling times for these oscillations. Simulation results demonstrate that the BDGOA-TDn(1 + PI) controller significantly outperforms conventional controllers, achieving a reduction in overshoot by 75%, faster settling times by 60%, and a lower integral of time-weighted absolute error by 50% under diverse operating conditions, including parameter variations and nonlinearities such as time delays and governor deadband effects.