Knowledge-driven Identification Method for Low Voltage Ride through Strategy of Photovoltaic Inverters

The rapid development of renewable energy power generation technology represented by photovoltaic and wind power has changed the fault characteristics of the grid. However, there is a major problem in the calculation of short-circuit current in existing renewable energy: the current analytical research on fault currents of various types of renewable energy power supplies is too dependent on the control strategy and parameters of renewable energy, while the control strategy and parameters are too hard to acquire. Aiming at the above problems, a knowledge-driven identification method for low voltage ride through strategy of photovoltaic inverters is proposed. Firstly, multi-dimensional features reflecting the differences of different control strategies are extracted from the time-varying trajectories and a knowledge-driven control strategy classification feature set is constructed; secondly, a typical control strategy identification model based on the random forest algorithm is established. Finally, the effectiveness of the proposed method is verified by simulation results.