Demand Response Control Strategy of Photovoltaic Grid-connected Inverter Based on Improved Linear Active Disturbance Rejection Dynamic Decoupling

In power systems, because the Auto Disturbances Rejection Controller (ADRC) performs relatively reliable stability and safety, it finds extensive application in the design of power inverters. However, its performance is constrained by inherent response lag and limited harmonic suppression capabilities. To address the evolving demands of modern power systems, Linear Active Disturbance Rejection Control (LADRC) has emerged as an upgraded alternative to conventional control strategies through its enhanced adaptability in practical applications. We propose a frequency-domain-equivalent-based LADRC control strategy, where systematic parameter calibration is implemented through transfer function methodology. Subsequent fine-tuning of critical parameters, which includes proportional gain, derivative gain, observer's natural frequency, control gain and so on, are made fine adjustments to optimize circuit performance. Experimental validation confirms the efficacy of first-order LADRC in improving voltage-current conversion characteristics of LLC-operated transformers. Furthermore, an adaptive control framework for third-order LADRC is established to achieve coupled parameter optimization. This research innovatively incorporates Particle Swarm Optimization (PSO) into the LADRC parameter configuration process, enhancing adjustment efficiency and facilitating optimal solutions. Simulation results verify that the enhanced LADRC effectively mitigates phase delay and improves dynamic response characteristics. The modified PSO algorithm exhibits technical superiority with 11.7% precision enhancement. The response speed has obvious technical advantages, and the coefficient of variation is significantly lower than that of the comparison algorithm, which verifies the advantages of the improved PSO algorithm in robustness. The proposed control strategy successfully compensates for ADRC limitations, achieving an optimal balance between control precision and steady-state performance through gain regulation in repetitive control systems. This advancement will provide critical technical support for addressing control challenges in renewable energy integration.