Optimizing grid-connected PV systems with novel super-twisting sliding mode controllers for real-time power...

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Control Theory

Background:

• Growing integration of solar photovoltaic (PV) systems into the grid necessitates advanced control strategies.
• Grid-connected PV (GCPV) systems require precise active and reactive power control for stability and efficiency.
• Existing optimization algorithms, like the CAOA, have limitations in convergence and exploration.

Purpose of the Study:

• To propose an innovative optimization algorithm, the Improved Artificial Optimization Algorithm (IAOA), by merging Particle Swarm Optimization (PSO) and CAOA.
• To implement an IAOA-based Sliding Mode Controller (SMC) for effective active and reactive power management in GCPV systems.
• To evaluate the performance and applicability of the IAOA using benchmark functions and a real-time simulation.

Main Methods:

• Development of the IAOA by combining PSO and CAOA principles.
• Implementation of an IAOA-based Sliding Mode Controller (ST-SMC) for power control.
• Performance evaluation using four benchmark functions and simulation in MATLAB.
• Validation on a 40 kW GCPV system using an OPAL-RT 4510 real-time simulator.

Main Results:

• The IAOA demonstrated superior performance in benchmark function testing.
• The IAOA-based ST-SMC achieved significantly reduced settling times for active and reactive power control (minimum 0.01012 s and 0.5075 s, respectively).
• The proposed controller outperformed PSO-based and CAOA-based ST-SMC in simulations.

Conclusions:

• The IAOA is a promising optimization technique for advanced control applications.
• The IAOA-based ST-SMC offers an effective and innovative solution for active and reactive power control in GCPV systems.
• Real-time simulation validates the practical applicability and efficiency of the proposed control strategy for renewable energy integration.