Control Inversion: A Clustering-Based Method for Distributed Wide-Area Control of Power Systems

Abstract

Wide-area control (WAC) has been shown to be an effective tool for damping low-frequency oscillations in power systems. In the current state of art, WAC is challenged by two main factors -namely, scalability of design and complexity of implementation. In this paper we present a control design called control inversion that bypasses both of these challenges using the idea of clustering. The basic philosophy behind this method is to project the original power system model into a lowerdimensional state-space through clustering and aggregation of generator states, and then designing an LQR controller for the lower-dimensional model. This controller is finally projected back to the original coordinates for wide-area implementation. The main problem is, therefore, posed as finding the projection which best matches the closed-loop performance of the WAC controller with that of a reference LQR controller for damping lowfrequency oscillations. We verify the effectiveness of the proposed design using the NPCC 48-machine power system model.

Slow Coherency Identification and Power System Dynamic Model Reduction by Using Orthogonal Structure of Electromechanical Eigenvectors

“…As discussed in Section V-B, it is known that the NPCC 48-machine test system can be well decomposed into 9 areas based on its 9 slowest electromechanical modes. Our previous case study in Section V-B comes to the same conclusion, but it additionally identifies alternative area structures consisting of 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,33,34,35 , 36 2 32, 37, 38, 39, 40, 41, 42 3 43, 44, 45, 46, 47, 48 3 and 6 areas (see Figure 2). The present case study illustrates the validity of decomposing the NPCC system into 6 areas by using our grouping algorithm in Section IV.…”

“…, C k . The results of these moves are evaluated using the update scheme in (8). In Section IV-B, the best move is implemented at every step until all machines in R are assigned.…”

“…Another use case of coherency-based reduced models is motivated by the aversion of system operators to share the full models of their control areas with third parties for confidentiality reasons. Besides applications to power system model reduction, coherency identification techniques are instrumental for the design of certain system integrity protection schemes (SIPS) such as intentional controlled islanding (ICI) [4]- [6], as well as the design of wide-area monitoring and control systems (WAMCS) [7], [8].…”

Functional observability and target state estimation in large-scale networks

“…power grids (22), are important to maintain system stability and in particular, mitigate the impact of perturbations that could lead to large-scale failures (61). However, such control strategies rely on resilient communication networks between spatially distributed components, which are arguably more vulnerable to potential failures and cyberattacks than the physical systems themselves.…”

“…It is often sufficient to focus on a particular subset of nodes of interest. For instance, in decentralized control strategies applied to network systems, each controller only requires feedback signals from a fraction of nodes in the neighborhood determined by the corresponding controlled area ( 21 , 22 ). This is also true for the detection and monitoring of unforeseen failures and cyberattacks, which find several applications in supply networks ( 23 ), power grids ( 24 , 25 ), and autonomous vehicle coordination ( 26 ).…”