Structured controller parameter tuning for power systems. (August 2020)

• Record Type: Journal Article
• Authors: Mešanović, Amer; Münz, Ulrich; Szabo, Andrei; Mangold, Martin; Bamberger, Joachim; Metzger, Michael; Heyde, Chris; Krebs, Rainer; Findeisen, Rolf
• Is Part Of: Control engineering practice. Volume 101(2020)
• Publication Date: 2020-08
• Subjects: Power system control -- Structured controller synthesis -- H-infinity design -- Linear matrix inequalities -- Distributed energy system -- Power oscillation damping -- Optimization; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2020.104490
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Reliable and secure operation of power systems becomes increasingly challenging as the share of volatile generation rises, leading to largely changing dynamics. Typically, the architecture and structure of controllers in power systems, such as voltage controllers of power generators, are fixed during the design and buildup of the network. As replacing existing controllers is often undesired and challenging, setpoint adjustments, as well as tuning of the controller parameters, are possibilities to counteract changing dynamics. We present an approach for fast and computationally efficient adaptation of parameters of structured controllers based on H ∞ optimization, also referred to as structured H ∞ controller synthesis, tailored towards power systems. The goal of the tuning is to increase the robustness of the system towards disturbances. Conditions are established that guarantee that the approach leads to stability. The results are verified in a testbed microgrid consisting of six inverters and a load bank, as well as in several simulation studies. Furthermore, the performance of the approach is compared to other tuning approaches, thereby demonstrating significantly reduced computation times. The proposed method improves the system robustness, as well as the time-response to step disturbances and allows structured controller tuning even for large networks.