Adaptive nonlinear control and observation for grid-connected wind-BESS systems with unknown demand

This study addresses the challenges of renewable energy integration in geographically dispersed power systems, where the real-time grid demand is unmeasurable due to the significant distance between generation sites (wind farms) and consumption centers. The proposed architecture connects a wind turbine farm to a Vienna rectifier, followed by a battery energy storage system, and interfaces with the main grid via a High-Voltage Direct Current transmission link. A nonlinear control scheme is designed to maximize wind energy extraction while ensuring grid stability through coordinated operation of the wind turbine farm, Vienna rectifier, and battery energy storage system. The key innovation lies in a linear matrix inequality-based adaptive observer that estimates the unmeasurable grid demand dynamics. The linear matrix inequality formulation guarantees stability and provides the numerical robustness needed to overcome system singularities, a common failure point for traditional observation methods. This robust estimation enables two critical functionalities: (i) Optimal battery energy storage system control: An advanced management strategy that regulates the battery charging and discharging cycles based on the estimated demand. (ii) Grid-Adaptive Generation: The power production is dynamically adjusted according to the estimated demand variations, thus overcoming the limitations imposed by geographical separation. The overall strategy ensures efficient and stable power delivery despite the lack of direct remote measurements. • A novel integrated adaptive control and observation framework for Wind-Battery systems. • Real-time estimation of unknown and fluctuating grid power demand. • Guarantees system stability and power quality without prior load knowledge. • Enhanced resilience against grid-side uncertainties and power fluctuations. • Superior performance validated through extensive simulation studies.