Automation of adaptive controller design for power generation from renewable energy sources using multiple chaotic maps-based Harris hawk optimizer

Renewable energy generation systems are expanding fast worldwide. These systems utilize inverters along with a filter to inject power into the grid. Among commonly used filters, the inductor-capacitor-inductor (LCL) filter stands out due to its characteristics. However, it has an inherent peak of resonance close to the frequency of interest that may make the system unstable. Active damping strategies come in to solve it. In this sense, direct adaptive controllers present advantageous benefits in comparison to controllers with fixed gains because they can update their gains online in response to changes in system parameters and exogenous disturbances. However, adaptive controllers have many parameters to design, requiring a very experienced designer to configure them to obtain satisfactory performance. In this context, this work proposes a procedure to parametrize adaptive controllers using a new multiple-chaotic maps-based Harris hawk optimizer (HHO). The parametrization procedure considers the controller stability constraints, tracking errors, and physical control action synthetization. The systematic methodology is evaluated on a robust adaptive model reference proportional integral controller applied to grid-injected current control of a voltage source inverter with an LCL filter. The proposed method surpasses the performance of 9 chaotic HHO. High-fidelity simulation results, considering a Kalman filter-based phase-locked loop and space vector modulation, indicate the high performance of the optimized controller, where the transient regimes end between one quarter and half of a grid cycle, and the steady-state total harmonic distortion (THD) is around 2.5%.