基于非线性系统降阶的感应电动机群聚合  

Induction Motors Aggregation Based on Nonlinear Model Reduction

作　　者：张钢[1] 杜正春[1] 倪煜[1] 李崇涛[1] 

机构地区：[1]西安交通大学电气工程学院,陕西省西安市710049

出　　处：《中国电机工程学报》2015年第8期1893-1899,共7页Proceedings of the CSEE

摘　　要：负荷模型对于电力系统稳定性分析结果有着重要的影响,感应电动机群聚合是综合负荷建模的重要组成部分。提出一种利用非线性模型降阶进行感应电动机群聚合的新方法。首先将感应电动机群描述成输入为母线电压,输出为等值阻抗的非线性系统,然后计算系统的经验格拉姆矩阵以及平衡变换矩阵,接着利用自适应遗传算法对平衡变换矩阵进行修正,最后应用Glarkin投影对原系统平衡截断得到低阶负荷模型。以Anderson 3机9节点系统为例,对6台参数差异较大的感应电动机群进行聚合,分析比较所提方法与其他方法得到的聚合模型在不同故障切除时间下的系统响应和临界切除时间。仿真结果表明,所提方法得到的聚合模型在不同的系统运行状态下均能较好地反映原始电动机群的特性,可以保证电力系统暂态仿真的精度。Load model contributes a lot in power system stability analysis. Induction motors aggregation is one of the most important parts in load modeling. This paper presented a new method for aggregating induction motors based on nonlinear model reduction. Firstly, the induction motors were considered as a nonlinear input-output system with bus voltage as input and equivalent impedance as output. Secondly, empirical gramians and transformation matrix of the system were calculated. Then the adaptive genetic algorithm was used to correct the transformation matrix. Lastly, the reduced-order system was obtained by truncating the balanced system based on Galerkin projection. Simulations and analysis were carried out in Anderson 9-bus system. The aggregating procedure was applied on 6 induction motors with different dynamic characteristics. Comparison between the results obtained from the proposed method and corresponding ones obtained from the published methods under different fault clearing time was presented, and the critical clearing time of each method was obtained. The simulation results demonstrate that the proposed method can achieve high accuracy under different operating states and can improve the precision of simulation.

关 键 词：感应电动机群聚合 非线性系统降阶 经验格拉姆矩阵 Galerkin投影 自适应遗传算法 

分 类 号：TM74[电气工程—电力系统及自动化]