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潮流不同排序方案的比较—学士学位论文文献翻译中英文对照.doc

发布:2017-08-05约2.41万字共22页下载文档
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A Comparison of Power Flow by Different Ordering Schemes Abstract—Node ordering algorithms, aiming at keeping sparsity as far as possible, are widely used today. In such algorithms, their influence on the accuracy of the solution is neglected because it won’t 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 groundwork 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 detailed. Keywords—power 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 control [1].Mathematically speaking, power flow problem is to find a numerical solution of nonlinear equations. Newton method is the most commonly used to solve the problem and it involves repeated direct solutions of a system of linear equations. The solving efficiency and precision of the 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 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,
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