文章摘要
黄天罡,薛禹胜,林振智,等.动态EEAC的自适应分段映射[J].电力系统自动化,2018,42(21):21-27. DOI: 10.7500/AEPS20180903005.
HUANG Tiangang,XUE Yusheng,LIN Zhenzhi, et al.Dynamic EEAC with Adaptive Subsection Mapping[J].Automation of Electric Power Systems,2018,42(21):21-27. DOI: 10.7500/AEPS20180903005.
动态EEAC的自适应分段映射
Dynamic EEAC with Adaptive Subsection Mapping
DOI:10.7500/AEPS20180903005
关键词: 扩展等面积准则  映象系统  时变程度  分段哈密顿化  算法族内的协调  电力系统暂态稳定
KeyWords: extended equal area criterion(EEAC)  imaging system  time-varying degree  piecewise Hamilton-approximation  coordination within an algorithm family  power system transient stability
上网日期:2018-09-19
基金项目:国家电网公司科技项目“电力转型对能源转型的主动支撑研究——以青海省为例”;国家自然科学基金资助项目(61533010)
作者单位E-mail
黄天罡 东南大学电气工程学院, 江苏省南京市 210096
南瑞集团(国网电力科学研究院)有限公司, 江苏省南京市 211106 
 
薛禹胜 南瑞集团(国网电力科学研究院)有限公司, 江苏省南京市 211106
智能电网保护和运行控制国家重点实验室, 江苏省南京市 211106 
xueyusheng@sgepri.sgcc.com.cn 
林振智 浙江大学电气工程学院, 浙江省杭州市 310027  
文福拴 浙江大学电气工程学院, 浙江省杭州市 310027  
徐岩 南洋理工大学电力与电子工程学院, 新加坡 639798  
岳东 南京邮电大学先进技术研究院, 江苏省南京市 210023  
摘要:
      在完整的扩展等面积准则(EEAC)算法流程中,动态EEAC(DEEAC)是在解析的静态EEAC(SEEAC)与精确的集成EEAC(IEEAC)之间的桥梁环节。为了更好地综合IEEAC的精确性与SEEAC的快速性,文中研究了DEEAC的分段数对完整EEAC算法性能的影响规律。并在此基础上,融合因果分析及机器学习方法,在DEEAC中采用自适应分段的泰勒级数展开及降维映射,以替代在故障中及故障后的2分段展开方式。根据DEEAC与SEEAC所得稳定裕度之差,定义算例的时变度指标,并据此自动调整DEEAC环节的分段数。对36套中国区域和省级电力系统数据的3 272个算例,包括对称及不对称故障在内的仿真结果表明:自适应分段的DEEAC比固定分段的DEEAC的计算量平均增加4%,但显著提升了强时变算例的分析精度,减小了稳定分类器的降效性误判率。
Abstract:
      In the full process of extended equal area criterion(EEAC)algorithm, dynamic EEAC(DEEAC)is the bridge between analytical static EEAC(SEEAC)and accurate integrating EEAC(IEEAC). For better coordinating the accuracy of IEEAC and the speedability of SEEAC, the influence of the subsection number of DEEAC on the performance of full EEAC is studied in detail. Based on theoretical analysis as well as the machine learning method, an adaptive segmental Taylor series expansion and dimensional reduction mapping are adopted to replace the 2-section expansions for post-fault in DEEAC. The difference between the stability margin given by the original DEEAC and that given by SEEAC is defined as the time-varying index. According to the index, the number of subsections in DEEAC can be adjusted automatically. Its robust performance is verified by 36 sets of data from 9 actual Chinese regional and provincial power systems, where both symmetric and asymmetric faults are considered, totally 3 272 cases. Compared with the average computational burden of the original DEEAC, the computational burden of the proposed adaptive DEEAC increases about 4%, however, the analysis precision for strong time-varying cases, as well as the effectiveness of the classifier for stable cases are significantly improved.
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