【正文】 t monly used to solve the problem and it involves repeated direct solutions of a system of linear equations. 外文翻譯 （原 文） 2 The solving efficiency and precision of the linear equations directly influences the performance of NewtonRaphson 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 putational efficiency of linear equations by reordering 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 generated in memory positions that correspond to zero entries in the starting Jacobian matrix. This action is referred to as fillin. If the programming terms is used which processed and stores only nonzero terms, the reduction of fillin reflects a great reduction of memory requirement and the number of operations needed to perform the factorization. So many extensive studies have been concerned with the minimization of the fillins. While it is hard to find efficient algorithm for determining the absolute optimal order, several effective strategies for determining nearoptimal orders have been devised for actual applications [2, 3]. Each of the strategies is a tradeoff between results and speed of execution and they have been adopted by much of industry. The sparsityprogrammed ordered elimination mentioned above, which is a breakthrough in power system work putation, dramatically improving the puting speed and storage requirements of Newton’s method [4]. After sparse matrix methods, sparse vector methods [5], which extend sparsity exploitation to vectors, are useful for solving linear equations when the righthandside vector is sparse or a small number of elements in the unknown vector are wanted. To make full use of sparse vector methods advantage, it is necessary to enhance the sparsity of L1by ordering nodes. This is equivalent to decreasing the length of the paths, but it might cause more fillins, greater plexity and expense. Countering this problem, several node ordering algorithms [6, 7] were proposed to enhance sparse vector methods by minimizing the length of the paths while 外文翻譯 （原 文） 3 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 node ordering algorithms take solving linear equations in a single iteration as research subject, aiming at the reduction of memory requirements and puting operations. Many matrices with a strong diagonal in work problems fulfill the above assumption, and ordering to conserve sparsity increased the accuracy of the solution. Nevertheless, if there are junctions of very high and low series impedances, long EHV lines, series and shunt pensation in the model of power flow problem, diagonal dominance will be weaken [8] and the assumption may not be tenable invariably. Furthermore, along with the 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 works and transmission systems as a whole in power flow calculation, and it will cause more serious numerical problem. All those things mentioned above will turn the problem into illcondition. 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 focus 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 contradiction between memory and accuracy in node ordering algorithm is introduced in section II. Next a simple DC power f