(PMUs) in terms of the accuracy and efficiency enhancements in the estimation results are presented and discussed.
Chapter 12 by Ye Guo, Lang Tong, Wenchuan Wu, Hongbin Sun, and Boming Zhang is under the title “Hierarchical Multi‐Area State Estimation” and is motivated by the need for a coordinated state estimator for multi‐area power systems. Of course, the proposed method should provide the same state estimate as a centralized estimator but solved in a distributed manner. In this chapter, the authors review earlier relevant work in the field, including two‐level single‐iteration estimators, inter‐area Gauss–Newton methods and intra‐area Gauss–Newton methods. In particular, the authors focus on recently published work where local system operators communicate their sensitivity functions to the coordinator. These sensitivity functions fully represent local optimal conditions, and consequently, this method has improved rate of convergence.
The application of parallel processing for static/dynamic state estimation is motivated by the desire for faster computation for online monitoring of the system behavior. In Chapter 13, Hadis Karimipour and Venkata Dinavahi investigate the process of accelerating static/dynamic estimation for large‐scale networks.
In the first part, using an additive Schwarz method, the solution of each subsystem is carried out by using the conventional numerical techniques and exchanging the boundary data among subsystems. To increase the accuracy a slow coherency method was used to decide the domain decomposition. In addition, load balancing by distributing equal workload among processors is utilized to minimize inter‐processor communication. The advantages of the proposed approach over existing approaches include reducing execution time by splitting equal amount of work among several processors, minimizing the effect of boundary buses in accuracy and not requiring major changes in existing power system state estimation paradigm. Next, the proposed method is implemented in massively parallel architecture of GPU. As shown in the results, the advantage of utilizing GPU for parallelization is significant when the size of the system is increased.
The proposed method is general and can be extended to any number of GPUs connected in a cluster. Results show that more GPUs can reduce expected computation time. Result comparisons verified the accuracy and efficiency of the proposed method. In addition, the performance of the slow coherency method as the partitioning tool was analyzed, and it was concluded that for different fault locations in the system, results derived from this method had lower amounts of error.
Chapter 14 is “Dishonest Gauss Newton Method‐Based Power System State Estimation on a GPU”, by Md. Ashfaqur Rahman and Ganesh Kumar Venayagamoorthy. The authors acknowledge that real‐time power system control requires accelerating the computation processes. While many methods to speed up the computational process are available, it is worthwhile to explore current parallel computation technology to develop faster estimators. The authors use the term “dishonest Gauss Newton method,” but the technique is based on the PARTAN (short for Parallel tangent). Their study concerns a graphics processing unit (GPU) implementation. As the method is not explored extensively in the literature, its accuracy is investigated first. Then different aspects of the parallel implementation are explained. It takes a few hundreds of microseconds for IEEE 118‐bus systems, which are found to be the fastest in the existing reported times. For very large systems, the required configuration of a GPU and the corresponding time are also estimated. Finally, the distributed method‐based parallelization is also implemented.
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