Model Structure Choice for a Static VAR Compensator Under Modeling Uncertainty and Incomplete Information

Abstract

To simulate the complex behavior of power systems, operators frequently rely on models. The task of model identification and validation becomes important in this context. The validity of the models has a direct influence on operator’s decisions and actions. In other words, erroneous or imprecise models lead to erroneous predictions of the systems’ behavior which may result in unwanted operator’s actions. This paper addresses the challenge of model structure choice for modeling and parameter identification in power systems. Three types of model structures are analyzed: 1) physical principle-based modeling; 2) black-box modeling (NARX, transfer function, Hammerstein–Wiener model); and 3) combination of physical and black-box modeling. This analysis has been performed using real grid measurements and available knowledge about a static VAR compensator (SVC) connected to the U.K.’s transmission network and operated by National Grid. The SVC’s modeling is presented in the context of a generalized modeling and identification algorithm, that is offered as a guideline for engineers. The model validity issues of the identified SVC models that include modeling uncertainty are discussed.