Shaobu Wang

Jianming Lian, Shaobu Wang, Ruisheng Diao et al.

As power systems become more and more interconnected, the inter-area oscillations has become a serious factor limiting large power transfer among different areas. Underdamped (Undamped) inter-area oscillations may cause system breakup and even lead to large-scale blackout. Traditional damping controllers include Power System Stabilizer (PSS) and Flexible AC Transmission System (FACTS) controller, which adds additional damping to the inter-area oscillation modes by affecting the real power in an indirect manner. However, the effectiveness of these controllers is restricted to the neighborhood of a prescribed set of operating conditions. In this paper, decentralized robust controllers are developed to improve the damping ratios of the inter-area oscillation modes by directly affecting the real power through the turbine governing system. The proposed control strategy requires only local signals and is robust to the variations in operation condition and system topology. The effectiveness of the proposed robust controllers is illustrated by detailed case studies on two different test systems.

Emilie Purvine, Eduardo Cotilla-Sanchez, Mahantesh Halappanavar et al.

Dynamic model reduction in power systems is necessary for improving computational efficiency. Traditional model reduction using linearized models or online analysis is not adequate to capture dynamic behaviors of the power system, especially with the new mix of intermittent generation and intelligent consumption making the power system more dynamic and non-linear. Real-time dynamic model reduction has emerged to fill this important need. This paper explores using clustering techniques to analyze real-time phasor measurements to identify groups of generators with similar behavior, as well as a representative generator from each group for dynamic model reduction. Two clustering techniques -- graph clustering and k-means -- are considered. These techniques are compared with a previously developed dynamic model reduction approach using Singular Value Decomposition. Two sample power grid data sets are used to test these different model reduction techniques. Based on the algorithms' relative performance, recommendations are provided for practical use.