基于局部学习机和细菌群体趋药性算法的电力系统暂态稳定评估  

Transient Stability Assessment of Power Systems Based on Local Learning Machine and Bacterial Colony Chemotaxis Algorithm

作　　者：顾雪平[1] 李扬[1] 吴献吉 

机构地区：[1]华北电力大学电气与电子工程学院,保定071003 [2]河北工业大学国际教育学院,天津300401

出　　处：《电工技术学报》2013年第10期271-279,共9页Transactions of China Electrotechnical Society

基　　金：中央高校基本科研业务费专项资金(13XS22)资助项目

摘　　要：为了提高电力系统暂态稳定评估的分类正确率,提出一种基于局部学习机(LLM)和改进的细菌群体趋势药性(BCC)算法的暂态稳定评估方法。该方法采用LLM构建暂态稳定评估模型,考虑相量测量单元可以提供的故障后实测信息,构造了一组系统特征作为LLM模型的输入量,稳定结果作为输出量,对稳定结果和系统特征间的映射关系进行训练,并通过综合混沌搜索策略的改进BCC算法优化LLM模型的参数。最后,以新英格兰10机39节点系统为例证明了所提方法的有效性。In order to improve the classification accuracy of transient stability assessment of power systems, a novel method based on local learning machine and an improved bacterial colony chemotaxis （BCC） algorithm is proposed, where local learning machine（LLM） is used to build a TSA model. Considering the possible real-time information provided by PMU, a group of system-level classification features extracted from the power system operation parameters are employed as inputs, and the stability result is used as output of the LLM model. The relationship between input and output is trained and the ideal model is obtained by applying the improved BCC combined with chaotic search strategy to determine the optimal parameters of LLM automatically. The effectiveness of the proposed method is shown by the simulation results on the New England 10-unit-39-bus power system.

关 键 词：暂态稳定评估 局部学习机 细菌群体趋药性 混沌搜索 相量测量单元 

分 类 号：TM712[电气工程—电力系统及自动化]