| 引用本文: | 王 亮,韩 冬,王长江,魏俊红,李 斌.基于MVEE和LSPTSVM的电力系统暂态稳定评估[J].电力系统保护与控制,2020,48(17):46-54.[点击复制] |
| WANG Liang,HAN Dong,WANG Changjiang,WEI Junhong,LI Bin.Power system transient stability assessment based on MVEE and LSPTSVM[J].Power System Protection and Control,2020,48(17):46-54[点击复制] |
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| 摘要: |
| 针对采用模式识别法进行电力系统暂态稳定评估时输入特征集构建困难和评估模型训练速度慢的问题，提出一种基于最小体积闭包椭球理论(Minimum Volume Enclosing Ellipsoid, MVEE)和最小二乘投影孪生支持向量机(Least Square Projection Twin Support Vector machine, LSPTSVM)的电力系统暂态稳定评估方法。首先，根据MVEE理论对系统轨迹信息进行优化处理，确定高维空间内包含所有轨迹信息的最小体积闭包椭球，并利用最小体积闭包椭球的物理属性构建输入特征集，可有效实现特征集降维。其次，在传统投影孪生支持向量机的目标函数中引入正则化项，并改进评估模型的内部约束条件，提高模型的求解速度，达到大规模电力系统的计算效率需求。最后，通过对IEEE-39和IEEE-145节点系统的算例分析，验证所提方法的有效性与可行性。 |
| 关键词: 模式识别 暂态稳定评估 最小体积闭包椭球 最小二乘投影孪生支持向量机 |
| DOI：DOI: 10.19783/j.cnki.pspc.191244 |
| 投稿时间：2019-10-12修订日期：2019-11-19 |
| 基金项目:国家电网有限公司科技项目资助(SGTYHT17-JS- 199)“千万千瓦级分层接入直流送受端系统动态行为机理和协调控制措施研究”；国网辽宁省电力限公司科技项目资助(SGTYHT17JS201)“辽宁电网利用广域量测系统提升电网安全稳定运行水平的技术研究” |
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| Power system transient stability assessment based on MVEE and LSPTSVM |
| WANG Liang,HAN Dong,WANG Changjiang,WEI Junhong,LI Bin |
| (1. State Grid Liaoning Electric Power Supply Co., Ltd., Shenyang 110006, China; 2. School of Electrical
Engineering, Northeast Electric Power University, Jilin 132012, China; 3. Northeast China Branch, Huadian
Electric Power Research Institute Co., Ltd., Shenyang 110167, China; 4. Electric Power Research
Institute of State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, China) |
| Abstract: |
| Given the difficulties of constructing the feature set and the slow training speed of the evaluation model when using the pattern recognition method for power system transient stability assessment, a power system transient stability assessment method based on Minimum Volume Enclosing Ellipsoid (MVEE) and Least Square Projection Twin Support Vector Machine (LSPTSVM) is proposed. First, according to the MVEE theory, the system trajectory information is optimized to determine the minimum volume closure ellipsoid with all trajectory information in high dimensional space. We construct the input feature set using the physical properties of the minimum volume closure ellipsoid. This can effectively achieve feature set dimension reduction. Secondly, a regularization term is introduced into the objective function of the traditional projection twin support vector machine, and the internal constraints of the evaluation model are improved. This can improve the solution speed of the model and meet the computational efficiency requirements of large-scale power systems. Finally, the validity and feasibility of the proposed method are verified by the case analysis of IEEE-39 and IEEE-145 node systems.
This work is supported by Science and Technology Project of State Grid Corporation of China (No. SGTYHT17- JS-199) and Science and Technology Project of State Grid Liaoning Electric Power Co., Ltd. (No. SGTYHT17JS201). |
| Key words: pattern recognition transient stability assessment minimum volume enclosing ellipsoid least square projection twin support vector machine |
