【作者】 吴为；

【导师】 柳进；

【作者基本信息】 哈尔滨工业大学 ， 电气工程， 2008， 硕士

【摘要】 无功优化问题是一个典型的非线性数学规划问题,目前已有很多算法用于无功优化计算。随着电力系统规模的日益扩大,约束条件的增多,无功优化问题变得越来越复杂。为了提高内点法无功优化计算的收敛性和计算速度,本文对内点法无功优化过程进行了研究。发现在内点法寻优过程中,随着无功优化大循环的进程,一方面部分控制变量的改变量逐渐减小,趋近于零;另一方面,寻优点的轨迹主要贴近少数约束边界行进,而距离部分约束边界较远。在以上两条规律性认识的基础上,提出了对内点法无功优化算法的改进。具体内容是:在后期无功优化大循环过程中,减少那些改变量逐渐趋于零的控制变量和离约束边界较远的约束条件。本文运用摄动法对网损灵敏度和相对灵敏度进行计算,建立了无功优化线性规划的数学模型。采用原对偶路径跟踪法对该模型进行计算,并结合潮流计算程序完成了系统整个无功优化计算。本文在内点法小循环计算收敛之后,对所有的控制变量和约束条件进行判断并统计,在下一次无功优化大循环到来之前,减去明显符合判别标准的控制变量和约束条件,从而减小了系统的规模。与改进前的内点法无功优化算法相比较,改进后的算法总的计算量明显降低。通过对IEEE118节点系统的仿真计算表明,改进后的内点法无功优化算法的计算速度和收敛性得到了明显提高,而且在每次无功优化大循环计算后,所降低的网损与改进前的内点法无功优化算法计算结果相比较,基本一致,可见改进的内点法无功优化算法并未降低优化的效益。更多还原

【Abstract】 Reactive power optimization is a nonlinear programming problems, many algorithms are used to calculate this problem. With the scale of power system becoming more and more large, the restrictions are more than before, the problem of reactive power optimization becomes complex and it’s difficult to calculate this problem. In order to improve the convergence and calculating speed, reactive power optimization was deeply researched in this paper. During the process of looking for the optimized point with interior-point theory, it finds that parts of the variables are no longer changed, the alteration of variables tends to become zero. On the other hand, the track of looking for the optimized point goes along with a few restrictions, but far away from the bounds of some restrictions. This paper improves the interior-point theory to calculate reactive power optimization,in which the main method is to reduce the variables that tends to become zero and the restrictions that are far away from the bounds.This paper calculates the sensitivity and sets up the model of reactive power optimization by using sensitivity analysis method. Then it uses the prime-dual path following method to calculate this model. Combining with the flow program, this paper finishes the whole process of reactive power optimization.After the convergence of interior-point circulation, all the variables and constrictions are judged. Before the next big circulation of reactive power optimization, this paper reduces the variables and constrictions which accord with the standard, and the scale of system is decreased. Comparing with the prime-dual path following method, the calculation capacity of the improving algorithm is reduced. The computation results of IEEE118 system show that the convergence and speed of algorithm is increased, and the precision of calculation is assured. It is obvious that improving algorithm don’t reduce the efficiency of optimization.更多还原