Parallelizing power flow analysis program /
In my M.S. thesis, the parallelization and implementations of
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| Format: | Thesis Book |
| Language: | English |
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[Place of publication not identified] :
[publisher not identified] ;
1994.
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| Online Access: | http://proxy.library.tamu.edu/login?url=http://proquest.umi.com/pqdweb?did=741965751&sid=1&Fmt=2&clientId=2945&RQT=309&VName=PQD |
| Summary: | In my M.S. thesis, the parallelization and implementations of Gauss-Seidel (G-S) and Successive overrelaxation (SOR) for power flow analysis have been investigated on a Sequent Balance shared memory (SM) machine. In this dissertation, we generalize the idea to more general computer architectures and demonstrate how to effectively speedup SOR algorithms by properly managing the bottlenecks on both Sequent Balance SM and nCUBE2 distributed memory (DM) machines. For SOR algorithms, when our coloring process is used to schedule the processors, there is almost no sequential portion. Thus, the only decisive factor left, which has a direct impact on the speedup upper bound, is the synchronization overhead. Accordingly, we propose a new synchronization scheme which can reduce the synchronization overhead on the Sequent Balance machine. Also, on the nCUBE2 machine, the desired properties to maximize the speedup, such as the minimum communication overhead and the balancing computational load, are described. To achieve the desired properties, we investigate a two stage parallelization scheme for DM type machines. In the first stage, we introduce a new efficient heuristic clustering algorithm that reduces the communication time and balances the computational load. In the second stage, we devise a heuristic coloring algorithm that minimizes the synchronization overhead and coordinates the information exchange among processors. It is shown that the parallelization scheme effectively increases the speedups and the associated upper bound of SOR algorithms on the nCUBE2 machine. For the SOR power flow analysis, constant acceleration factors obtained from experiments are commonly used to speedup convergence. The disadvantage of the approach is that the carefully tuned acceleration factors loading conditions. To overcome this disadvantage, we propose a new adaptive nonlinear SOR (ANSOR) algorithm, applying the concept of automatic adaptation of acceleration factors during the iteration process. The algorithm is shown to be faster due to the significant reduction in the number of iterations, and to converge robustly on heavily-loaded large power systems. We also implement parallel SOR and ANSOR algorithms on the nCUBE2 machine by using the results of the two stage parallelization scheme. It is shown that our parallel ANSOR algorithm is competitive with the fast decoupled load flow (FDLF) algorithm on the nCUBE2 machine due to the high speedup attained and reasonable number of iterations. Moreover, the portability of the parallel ANSOR code is demonstrated by porting the code to the Intel iPSC/860 hypercube and the Paragon mesh MIMD machines. Finally, we shall use the developed ANSOR scheme as a tool in the continuation power flow program to study the voltage stability analysis. |
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| Item Description: | Vita. "Major Subject: Electrical Engineering". |
| Physical Description: | xii, 168 leaves : illustrations ; 28 cm. Issued also on microfiche from University Microfilms Inc. |
| Bibliography: | Includes bibliographical references. |