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One of the most promising decentralized network controllers is the family of power electronics-based controllers, known as flexible AC transmission system (FACTS) devices. These devices locally modify the topology of the system by rapid switching. By the choice of switching patterns, these devices can achieve a variety of power system objectives, such as voltage support, oscillation damping, and stability improvement. This ability can be used for not only reliability objectives such as increasing the maximum throughput power, but can also be used to adjust power flow for economic reasons. In the power system restructured environment, it is foreseeable that power flows will be adjusted throughout the system to maximize economic objectives. These devices, however, are relatively new and few are currently beyond the prototype stage, therefore their wide-spread impact on the transmission network has not yet been thoroughly analyzed. While these devices offer increased network power flow controllability, the decentralized nature of their actions may cause deleterious interactions between them. In this paper, we propose to utilize flexible topology FACTS devices in developing distributed control strategies to i) detect and mitigate intentional or unintentional cascading failures, ii) develop operating strategies that can automatically adjust to changing economic and physical environments, and iii) develop interaction policies to mitigate counterproductive actions.

The bulk power system grid is arguably the largest man-made interconnected network in existence. The sheer size of the power network makes control of the grid an extremely difficult task. Compounding this, the network is comprised of both continuous and discrete controls which typically operate in a disassociated fashion. Cascading failures are the most severe form of contingency that can occur in a power system. A cascading failure occurs when one contingency on the system induces a second contingency. If not immediately mitigated, the second contingency may lead to a third contingency, fourth contingency, and so on until load must be shed to stabilize the system. A typical cascading failure involves transmission line overloads. In a system where there is a large directional power flow from one region to another, the loss of a single transmission line can instigate a cascading failure. If the first transmission line is lost, the power it carried must be shunted across the remaining parallel transmission lines. This may cause one or more of these transmission lines to overload, thereby directing still more power across fewer and fewer lines. At some point, it is no longer possible to serve the load with the existing network topology. If load is not shed in a timely manner, generators may trip as a result of under-frequency protective relays.


The power flow in a power grid is modeled as a directed network flow problem with a directed graph G (N, A) modeling the power network. The set of nodes, N, correspond to the bus bars (or simply buses) of the power network. Power flow between nodes 1, 2 N is represented by an arc aij A. Each arc is assigned a weight, uij, denoting the current (or maximum allowed) power flow over that arc in the network. For each node, n N, due to Kirchoffs Current Laws (KCL)s, power in must equal power out.


RELATED WORK Much of the previous work in reconfiguring power flow has concentrated on determining a priori how power is distributed in the network from each source. The work of [3] traces the flow of power to load by solving nodal flow equations. This idea is further explored in [15] by selectively removing power sources to identify flow in the network. Both these efforts are targeted towards identifying flows for economic reasons, but the same principles can be extended to configuration management. Our work differs in that we compute, in a distributed realtime manner, the actual flow balance needed to prevent cascading failures. The configuration control is executed by embedded control devices rather than a static allocation or switching. In this paper, we present a basic distributed algorithm and the proposed method of providing the required fault tolerance of the system.

While the Maximum-Flow algorithm can be run from a central site, there are several problems with this centralized approach. The first is that communication with the central site may be compromised due to hardware failures or intrusions into the communications network. The central site may also be subject to failure. Finally, the computational cost of a max-flow algorithm written this way is 0 ([N]2[A]). However, more recent algorithms can reduce this to nearly 0([N][A])[1]. Decentralized, or parallel, algorithms for the solution of max-flow have been proposed by many researchers. In [1], a parallelization using a shared-memory multiprocessor results in nearly linear speedup (performance improves linearly as the number of processors increases). A distributed algorithm is presented by [10] with a time complexity of 0 ([N]2 using a processor at each node.

The paper has concentrated on the coordination requirements of the FACTS devices in configuration control. Even more critical than the basic algorithms are the software/ hardware embedded systems aspects of safety, liveness, fault tolerance, or security addressed by this paper. Experience with the TCAS II Air Traffic Collision Avoidance System [9] has shown that the engineering of software for such systems is an extremely involved and error-prone process and that formal specification and formal methods alleviate many of the ambiguities that lead to software errors.

The traditional approach to hardware fault tolerance is to explicitly replicate a calculation at a number of computing sites and vote on the result. This introduces explicit hardware redundancy overhead and, in general, requires rigid synchronization of processes not present in power grid control. Software fault tolerance typically consists of executing multiple versions of the software (written from the same specification) to catch residual design flaws. Work has shown, though, that little benefit is gained from this approach once the system is installed. By contrast, compliance to formal specifications can be approached either at development time, through program verification, or at run time, through distributed run-time assertion checking, using one or both.

This paper presents a framework for developing fault tolerance approaches for bulk power transmission systems that can be implemented through the hardware and software of distributed FACTS devices. We have laid the foundation for developing fault tolerance assertions based on the well known max-flow algorithm that allow the run-time identification of system faults, whether intentional or unintentional. Several approaches to conflict resolution have been proposed.