Adaptive Data-Driven Secondary Control for Renewables Dominated Islanded Microgrids and Field Tests

This paper proposes a novel adaptive data-driven secondary control method for islanded microgrids (MGs) dominated by grid-forming (GFM) inverters, without depending on accurate network parameters. Using the measured input/output data of the MG, a data-driven dynamic linearization model related to disturbances is built at each operating point of the closed-loop control system. Next, we develop a robust recursive regression method to learn the dynamic linearization parameter that is updated online. To estimate the lumped disturbance, an adaptive disturbance observer with an observer gain updated online is introduced. Subsequently, the adaptive data-driven cooperative controller with an iterative structure is developed. This method can effectively regulate the voltage and frequency of the islanded MG with adaptability to system changes. The effectiveness of the proposed method is verified through both simulations and field tests in a real 100% PV powered islanded microgrid.