A Grey Compensation-Based Back-Stepping Control Method for Energy Storage Converters

To address grid stability issues caused by power fluctuations in photovoltaic systems, this paper proposes a Back-stepping control strategy integrated with a grey compensation mechanism. By leveraging the rapid response capability of energy storage converters, this approach incorporates grey prediction algorithms into the traditional Back-stepping control framework to dynamically compensate for stochastic disturbances in photovoltaic power generation. The algorithm implementation involves real-time prediction models that adjust control parameters based on historical power data trends.

The control architecture employs a hierarchical design: the upper layer generates reference commands for active power (P) and reactive power (Q) according to grid dispatch requirements, while the lower layer achieves dual-objective tracking through an enhanced Back-stepping controller. The grey compensation module establishes prediction models using historical data to preemptively correct tracking lag caused by system inertia. Compared to conventional PI control, this method improves dynamic response speed by approximately 23%, with implementation involving recursive prediction algorithms and error correction mechanisms.

For voltage stability, a mathematical model incorporating converter dynamic characteristics is established. Based on Lyapunov stability theory, adaptive laws are designed to ensure grid connection point voltage fluctuations remain within the allowable range of ±5%. The code implementation typically includes voltage error calculation modules and adaptive gain adjustment functions. Experimental data shows that under sudden illumination changes, this method confines power tracking errors within 1.5% while effectively suppressing voltage flicker phenomena.

The innovation of this scheme lies in combining the uncertainty handling capability of grey system theory with the nonlinear system advantages of Back-stepping control, providing new control insights for high-penetration renewable energy integration scenarios. Key functions in the implementation include disturbance observers and parameter adaptation algorithms. Future research could focus on online optimization strategies for compensation parameters to adapt to more complex grid operating conditions, potentially involving machine learning-based tuning algorithms.