Digital-Twin-Driven PI Parameter Evaluation Method for Grid-Connected Converters

Digital twin technology describes and models the characteristics, behavior, formation process and real-world performance of physical objects by using digital technologies such as sensors, Internet of Things (IoT), and artificial intelligence (AI). It is one of the key technologies to realize real-time perception and operation optimization of electric power equipment. Proportional-integral (PI) modules are the most commonly used blocks in the control system of power converters. But choosing the parameters with the best control effect requires repeated experiments. Therefore, this paper proposes a data-driven model that can evaluate the control performance of different PI parameters accurately and efficiently. First, the concept of digital twins and a general architecture applicable to the key power electronic equipment are demonstrated. Then, taking the grid-connected converter as an example, a data-driven model based on artificial neural network (ANN) and Levenberg-Marquardt (LM) algorithm is built to realize the evaluation of PI controller parameters. Comparisons with simulation models are also provided to show the validity of the digital-twin driven control parameter evaluation method.