A model-free sparse wide-area damping controller for inter-area oscillations. (March 2022)

• Record Type: Journal Article
• Authors: Zenelis, Ilias; Wang, Xiaozhe
• Is Part Of: International journal of electrical power & energy systems. Volume 136(2022)
• Publication Date: 2022-03
• Subjects: Wide-area damping control -- Inter-area oscillations -- Model-free methods -- Phasor measurement units -- Sparse optimal control -- Modal linear quadratic regulator; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107609
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Model-free sparse WADC requires no model information and reduces communication cost. Inter-area modes are identified via PMU data and damped using minimal control effort. MLQR-based optimal control directly shapes the closed-loop inter-area mode features. The WADC adapts to any operating condition and is robust to missing/noisy PMU data. Abstract: As the successor of local control, wide-area damping control (WADC) is considered to be an effective tool for damping inter-area oscillations. The state-of-the-art WADC approaches are either model-based or centralized, so they are subject to model inaccuracy, bad data, high communication costs, and other nontrivial design challenges. On the other hand, current data-driven WADC practices are affected by the selection of input/output measurements, need to be pre-tuned and trained offline, or lack clear physical interpretation of the power grid small-signal dynamics. In this paper, we propose a novel online purely data-driven WADC method using phasor measurement unit (PMU) data, which requires no power system model information and can damp multiple inter-area modes simultaneously using minimal control effort. To directly shape the closed-loop characteristics of poorly-damped inter-area modes and account for physical constraints on communication network, we develop a modal linear quadratic regulator (MLQR)-based sparsity-promoting optimal state feedback controller. The developed WADC strategy does not require offline training, is adaptive to the applied PMU dataset and can be mapped to the actual grid physics. Dynamic simulations on the widely studied IEEE 68-bus benchmark system verify the effectiveness of the proposed approach in spite of measurement noise, PMU losses, and communication network restrictions. Moreover, it is shown that the proposed controller can adapt to different operating conditions due to its data-driven nature. Finally, our data-driven sparse WADC has comparable performance to conventional model-based and centralized data-driven WADC, while overcoming their limitations.