节点文献
基于模式识别的电力系统暂态稳定评估技术的研究
Research on Pattern Recognition Based Power System Transient Stability Assessment
【作者】 曹旌;
【导师】 王成山;
【作者基本信息】 天津大学 , 电力系统及其自动化, 2005, 硕士
【摘要】 在电力系统的暂态稳定评估中,故障筛选是一个必需的环节。本文基于模式识别方法,围绕暂态稳定故障筛选问题,进行了研究。本文的工作主要集中在分类特征量的选取和分类算法两个方面。首先,提出了一组用于暂态稳定故障筛选的特征量。目前常用的特征量都是利用系统故障后仿真结束时刻的状态信息来表征或反映系统的稳定性情况,这些特征量仅反映了系统状态轨迹在故障后系统稳定平衡点的稳定域中的状态特征,但无法给出关于状态点距离稳定域边界远近的测度。基于这一点考虑,本文将能量裕度值与故障后仿真结束时刻的状态信息结合,作为故障筛选使用的特征量。随后,使用BP神经网络方法实现了暂态故障筛选。通过对比引入能量裕度特征量前后神经网络的工作效果,证明本文中使用的特征量有效的提高了故障筛选的正确性。对比神经网络方法,本文还提出了一种基于聚类分析的电力系统暂态故障筛选方法。该方法建立在模糊聚类分析(FCM)和矢量量化方法(VQ)基础之上,结合了两种聚类分析方法的优点。通过新英格兰10机39节点系统和IEEE50机145节点系统的测试,证明了该方法的有效性。
【Abstract】 Contingency screening is a vital segment of power system transient stabilityassessment (TSA). In this dissertation, methods based on pattern recognition areused for contingency screening. The Selecting of contingency features andclassifying methods are the main contents of this paper.First, this paper summarizes a contingency feature set for transient stabilityclassification. At present, most contingency features, which are used for reflectingthe stability of system, are obtained by time-domain simulation. These features onlyfigure out the system state information at the time of simulation finished, but theycan’t tell the distance between the state-point and the stability region boundary. Forthis reason, this paper chooses features that combining the transient margin with thepost-fault system state information.Back-propagation neural network (BPNN) method is used in transientcontingency screening. Results have shown that the features selected in this paperhave efficiently improved the contingency screening accuracy. Another methodbased on clustering analysis is also put forward. This method is based on Fuzzyc-means (FCM) and Vector Quantization (VQ) Method. Case studies on the10-machine New England system and IEEE 50-machine system are given to showthe validity of this method.
【Key words】 Contingency Screening; BP Neural Network; Clustering Analysis; Power System; Transient Stability;
- 【网络出版投稿人】 天津大学 【网络出版年期】2006年 06期
- 【分类号】TM712
- 【被引频次】9
- 【下载频次】383