A nonlinear observer-based control strategy for hybrid energy storage systems to improve voltage disturbance...

This paper proposes an efficient nonlinear observer-based a simple and effective control approach for DC microgrids integrating hybrid energy storage systems (HESSs) composed of supercapacitors and batteries with photovoltaic (PV) generation. The study focuses on improving HESS performance in DC microgrids, particularly in achieving fast and accurate DC microgrid bus voltage control, improved dynamic response, enhanced disturbance rejection capabilities, and better HESS power allocation. The proposed scheme combines a proportional-integral (PI) voltage controller with a variable-gain Nonlinear High-Gain Observer (NHGO) that estimates and compensates external disturbances, including load changes and PV power fluctuations. Unlike conventional high-gain or extended-state observers, which negatively impacted by measurement noise that can only be mitigated by reducing observer gain, consequently weakening dynamic response, the NHGO overcomes this limitation by dynamically adjusts its gain to achieve fast disturbance estimation during transients while maintaining noise immunity in steady state. This is achieved by utilizing high gain values during transient states for rapid disturbance estimation and rejection, followed by gain reduction during steady state to minimize noise and estimation error. Accordingly, the control strategy strategically allocates power demands between battery and supercapacitor storage components based on their inherent characteristics, effectively managing both transient and steady-state power imbalances while maintaining stable DC microgrid bus voltage. The complete system was implemented and validated through MATLAB/Simulink and real-time experiments using the OP5700 simulator. Results prove up to 45% reduction in voltage overshoot, 35% improvement in settling time, and superior robustness against parameter variations compared with ESO-LPF, HGO-LPF, and STO-LPF based control methods.