电气工程及其自动化专业外文翻译--潮流不同排序方案的比较

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1、外文翻译（原 文） 1 中文 4900 字 ,3150 单词， 17000 英文字符 出处： Li W, Han X, Zhang B. A comparison of power flow by different ordering schemesC/Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 2011 4th International Conference on. IEEE, 2011: 742-745. A Comparison of Power Flow by Diffe

2、rent Ordering Schemes Wenbo Li, Xueshan Han, Bo Zhang The School of Electric Engineering Shandong University Jinan, China Email:liwenbo_ AbstractNode ordering algorithms, aiming at keeping sparsity as far as possible, are widely used today. In such algorithms, their influence on the accuracy of the

3、solution is neglected because it wont make significant difference in normal systems. While, along with the development of modern power systems, the problem will become more ill-conditioned and it is necessary to take the accuracy into count during node ordering. In this paper we intend to lay ground

4、work for the more rationality ordering algorithm which could make reasonable compromising between memory and accuracy. Three schemes of node ordering for different purpose are proposed to compare the performance of the power flow calculation and an example of simple six-node network is discussed det

5、ailed. Keywordspower flow calculation； node ordering； sparsity； accuracy； Newton-Raphson method ； linear equations I. INTRODUCTION Power flow is the most basic and important concept in power system analysis and power flow calculation is the basis of power system planning, operation, scheduling and c

6、ontrol 1.Mathematically speaking, power flow problem is to find a numerical solution of nonlinear equations. Newton method is the most commonly used to solve 外文翻译（原 文） 2 the problem and it involves repeated direct solutions of a system of linear equations. The solving efficiency and precision of the

7、 linear equations directly influences the performance of Newton-Raphson power flow algorithm. Based on numerical mathematics and physical characteristics of power system in power flow calculation, scholars dedicated to the research to improve the computational efficiency of linear equations by reord

8、ering nodes number and received a lot of success which laid a solid foundation for power system analysis. Jacobian matrix in power flow calculation, similar with the admittance matrix, has symmetrical structure and a high degree of sparsity. During the factorization procedure, nonzero entries can be

9、 generated in memory positions that correspond to zero entries in the starting Jacobian matrix. This action is referred to as fill-in. If the programming terms is used which processed and stores only nonzero terms, the reduction of fill-in reflects a great reduction of memory requirement and the num

10、ber of operations needed to perform the factorization. So many extensive studies have been concerned with the minimization of the fill-ins. While it is hard to find efficient algorithm for determining the absolute optimal order, several effective strategies for determining near-optimal orders have b

11、een devised for actual applications 2, 3. Each of the strategies is a trade-off between results and speed of execution and they have been adopted by much of industry. The sparsity-programmed ordered elimination mentioned above, which is a breakthrough in power system network computation, dramaticall

12、y improving the computing speed and storage requirements of Newtons method 4. After sparse matrix methods, sparse vector methods 5, which extend sparsity exploitation to vectors, are useful for solving linear equations when the right-hand-side vector is sparse or a small number of elements in the un

13、known vector are wanted. To make full use of sparse vector methods advantage, it is necessary to enhance the sparsity of L-1by ordering nodes. This is equivalent to decreasing the length of the paths, but it might cause more fill-ins, greater complexity and expense. Countering this problem, several

14、node ordering algorithms 6, 7 were proposed to 外文翻译（原 文） 3 enhance sparse vector methods by minimizing the length of the paths while preserving the sparsity of the matrix. Up to now, on the basis of the assumption that an arbitrary order of nodes does not adversely affect numerical accuracy, most no

15、de ordering algorithms take solving linear equations in a single iteration as research subject, aiming at the reduction of memory requirements and computing operations. Many matrices with a strong diagonal in network problems fulfill the above assumption, and ordering to conserve sparsity increased

16、the accuracy of the solution. Nevertheless, if there are junctions of very high and low series impedances, long EHV lines, series and shunt compensation in the model of power flow problem, diagonal dominance will be weaken 8 and the assumption may not be tenable invariably. Furthermore, along with t

17、he development of modern power systems, various new models with parameters under various orders of magnitude appear in the model of power flow. The promotion of distributed generation also encourage us to regard the distribution networks and transmission systems as a whole in power flow calculation,

18、 and it will cause more serious numerical problem. All those things mentioned above will turn the problem into ill-condition. So it is necessary to discuss the effect of the node numbering to the accuracy of the solution. Based on the existing node ordering algorithm mentioned above, this paper focu

19、s attention on the contradiction between memory and accuracy during node ordering, research how could node ordering algorithm affect the performance of power flow calculation, expecting to lay groundwork for the more rationality ordering algorithm. This paper is arranged as follows. The contradictio

20、n between memory and accuracy in node ordering algorithm is introduced in section II. Next a simple DC power flow is showed to illustrate that node ordering could affect the accuracy of the solution in section III. Then, taking a 6-node network as an example, the effect of node ordering on the performance of power flow is analyzed detailed in section IV. Conclusion is given in section VI.