Dynamic State Estimation for Photovoltaic Under Variations of Solar Irradiance

Dynamic state estimation (DSE) plays a fundamental role in the monitoring and operation of power systems. Although previous work focuses mainly on traditional synchronous generations, with the increasing penetration of renewables, the estimation of photovoltaic (PV) systems is gaining increasing popularity. However, they primarily address static estimation or adopt an oversimplified dynamic model with a deterministic assumption for solar irradiance. Obviously, this cannot hold in practice, which will inevitably lead to biased estimation results. Facing these problems, this article explores DSE for the first time for a detailed two-stage PV system with the PV array, boost converter, inverter, and filter included. Also, to avoid biased estimation results under solar irradiance variations, we further propose to treat the randomness of solar irradiance as the unknown input of a DSE, which is further analytically merged into the unscented Kalman filter (UKF) framework with an unbiased minimum-variance (UMV) manner. Simulations performed on IEEE standard test systems reveal that even under severe variations of solar irradiation that serve as unknown inputs to the system, the proposed method can produce an unbiased estimate of the dynamic states of the PV, which is also verified in a real-world system. This accurate DSE can serve as a reliable prerequisite for the protection and control of PV-penetrated power systems.