Assessing the Effectiveness of an Intelligent Algorithms-Based PII2 Controller in Enhancing the Quality of Power Output from a DFIG-Based Power System

This paper proposes a novel methodology based on two intelligent algorithms for regulating the power output of a multi-rotor turbine system. A proportional-integral plus second-order integral regulator is utilized to regulate the energy output of an induction generator. The designed controller is characterized by its ease of configuration, cost-effectiveness, high robustness, and ease of implementation. The controller’s parameters are tuned using a genetic algorithm (GA) and a rooted tree optimization (RTO) algorithm, with the objective of maximizing operational performance and power quality. In accordance with the proposed design methodology, the optimal values for the parameters of the designed strategy are attained through the implementation of integral time-weighted absolute error (ITAE). The present controller has been designed to deviate from conventional controllers, and a comparison will be made between the two using MATLAB under various operating conditions. The operational performance was evaluated in comparison to the conventional algorithm in terms of current quality, torque ripples, threshold overshoot, system parameter changes, and so forth. The experimental results, as measured by the tests conducted, demonstrated that the proposed RTO-based regulator exhibited enhancements of up to 89.88% (traditional control) and 51.92% (GA) in active power ripples, 68.19% (compared to traditional control) in ITAE, 51.91% (traditional control) in reactive power overshoot, and 0.5% (compared to GA) in active power response time. Conversely, the proposed GA-based regulator yielded a steady-state error value that was 96.55% superior to the traditional approach and 86.48% more accurate than the RTO algorithm. Moreover, the efficacy of the RTO-based control system was found to be considerably augmented under variable system parameters. Total harmonic distortion improvements of 69% were observed compared to traditional control methods, and 1% compared to the GA technique. The findings of this study offer significant insights into enhancing the robustness of multi-rotor turbine systems and improving power quality.