电力系统发电机参数整体辨识的多情景并行内点算法

Multi-scenario Parallel Interior Algorithm for Generator Parameter Identification in Power Systems

同步发电机模型的准确性对电力系统稳定性研究影响甚大,如何快速准确地确定系统中发电机模型参数是电力系统建模领域非常重要的问题之一。利用广域测量装置,提出一种同时对多台发电机在线辨识算法。对多机系统多台发电机同时辨识问题建立了完整的数学模型,同时为了应对相量测量单元(PMU)测量误差,提出多情景参数辨识,提高辨识精度。多台发电机同时辨识问题是一类动态优化问题,采用内点法求解时,对大系统求解,计算时间过长。因参数辨识问题自由度较低,提出简约空间内点算法,极大地降低了计算时间,同时针对多情景辨识,提出情景解耦策略,并行求解该问题。对IEEE 9节点系统和IEEE 39节点系统进行算例测试发现,相对传统单机辨识算法,该方法对发电机辨识有较高的准确性,能较快地计算出辨识结果。

The accuracy of the synchronous generator model has great impact on power system stability study, and how to quickly and accurately determine the parameters of the generators in a power system is one of the most important issues in the field of power system modeling. In this paper, an online system-wide identification algorithm is proposed with the wide area measuring device and a complete mathematical model is developed for online identification multiple generators in a multi- machine system at the same time. And the multi-scenario parameter identification is proposed to improve the recognition accuracy to cope with the phasor measurement unit （PMU） measurement error. Multi-generator identification is a class of dynamic optimization problems, which is very time-consuming when solving large systems. A reduced-space interior point method is proposed to solve the parameter identification problem with small degrees of freedom, greatly reducing the computational time. Further, the scenario-decoupling strategy is proposed to solve the multi-scenario parameter identification in parallel. Two test cases show that the algorithm under discussion has higher accuracy and can quickly find the identified parameters compared with the traditional stand-alone identification algorithm.