全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于证据理论改进合成法则的电力系统安全检验综合判定算法

, PP. 247-255

Keywords: 证据理论,合成法则,电力系统,安全检验,综合判定

Full-Text   Cite this paper   Add to My Lib

Abstract:

为了评价电力二次系统等级保护建设的达标与否,本文采用证据理论对安全检验结果进行合成。在研究证据理论原始合成法则的过程中发现其对于高度冲突的证据的合成难以取得较好的效果,而目前已有的多种改进方法效果也并不理想。因此,本文提出了证据间冲突变化程度平均性的概念,根据此概念设计了一种新型的证据理论合成法则,并建立了一套综合判定改进算法。此算法能够较好地解决证据间的高度冲突并较为合理地减轻不确定性因素的影响,从而得到较好的符合性判定结果,能够高效准确地判断出目标系统实际建设情况与应满足的安全要求之间的符合性程度,从而保障电力二次系统乃至一次系统的正常运行。

References

[1]  Zhang Xiaoqiang, Guan Lin, Wang Tongwen. Kernel feature identification based on improved ant colony optimization algorithm for transient stability assessment[J]. Transactions of China Electrotechnical Society, 2010, 25(12): 154-160.
[2]  国家电力监管委员会. 电力二次系统安全防护规定. 2008.
[3]  Yager R R. On the dempster-shafer framework and new combination rules[J]. Information Science, 1987, 41: 93-137.
[4]  Inagaki T. Interdependence between safety-control police and multiple-sensor schemes via dempster- shafer theory[J]. IEEE Transactions on Reliability, 1991, 40(2): 182-188.
[5]  孙全, 叶秀清, 顾伟康. 一种新的基于证据理论的合成公式[J]. 电子学报, 2000, 28(8): 117-119.
[6]  Xiang Yang, Shi Xizhi. Modification on combination rules of evidence theory[J]. Journal of Shanghai Jiaotong University, 1999, 33(3): 357-360.
[7]  杜峰, 施文康, 邓勇. 证据特征提取及其在证据理论改进中的应用[J]. 上海交通大学学报. 2004, 38(增刊): 164-168.
[8]  Du Feng, Shi Wenkang, Deng Yong. Feature extraction of evidence and its application in modification of evidence theory[J]. Journal of Shanghai Jiaotong University, 2004, 38(z1): P164-168.
[9]  Murphy C K. Combining belief fuctions when evidence Conflicts[J]. Decision Support Systems, 2000, 29(1): 1-9.
[10]  孙怀江, 杨静宇. 一种相关证据合成方法[J]. 计算机学报, 1999, 22(9): 1004-1007.
[11]  Li Bicheng, Wang Bo, Wei Jun, et al. An efficient combination rule of evidence theory[J]. Journal of Data Acquisition & Processing, 2002, 17(1): 33-36.
[12]  国家电力监管委员会. 电力行业信息系统安全等级保护定级工作指导意见. 2007.
[13]  章小强, 管霖, 王同文. 针对特征选择问题的改进蚁群算法及其在电力系统安全评估中的应用[J]. 电工技术学报, 2010, 25(12): 154-160.
[14]  Sun Quan, Ye Xiuqing, Gu Weigang. A new combination rule of evidence[J]. Acta Electronica Sinica, 2000, 28(8): 117-119.
[15]  向阳, 史习智. 证据理论合成发展的一点修正[J]. 上海交通大学学报, 1999, 33(3): 357-360.
[16]  邓勇, 施文康. 一种改进的证据推理组合规则[J]. 上海交通大学学报. 2003, 37(8): 1275-1278.
[17]  Deng Yong, Shi Wenkang. A modified combiantion rule of evidence theory[J]. Journal of Shanghai Jiaotong University, 2003, 37(8): 1275-1278.
[18]  Smets P. The combination of evidence in the transferable belief model[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1990, 12(5): 447-458.
[19]  Sentz K. Combination of evidence in dempster- shafer theory[M]. New York: Binghamton University Press, 2002.
[20]  Liu Vanqiong, Chen Yingwu, Gao Feng, et al. Risk evaluation using evidence reasoning theory[C]. 2005 International Conference on Machine Learning and Cybernetics(ICMLC 2005), 2005: 2855-2860.
[21]  高会生, 朱静. 基于D-S证据理论的网络安全风险评估模型[J]. 计算机工程与应用, 2008, 44(6): 754-759.
[22]  Gao Huisheng, Zhu Jing. Security risk assessment model of network based on D-S evidence theory[J]. Computer Engineering and Application, 2008, 44: 754-759.
[23]  Shafer G. A mathematical theory of evidence[M]. New Jersey: Princeton University Press, Princeton, 1976.
[24]  Dempster A P. A generalization of bayesian inference (with discussion)[J]. Journal of the Royal Statistical Society Series B, 1968, 30(2): 205-247.
[25]  Bae H R, Grandhi R V, Canfield R A. Sensitivity analysis of structural response uncertainty propagation using evidence theory[J]. Structural and Multidisciplinary Optimization, 2006, 31(4): 270-279.
[26]  Sun Huaijiang, Yang Jingyu. A combination method for dependent evidences[J]. Chinese Journal of Computers, 1999, 22(9): 1004-1007.
[27]  李弼程, 王波, 魏俊, 等. 一种有效的证据理论合成公式[J]. 数据采集与处理, 2002, 17(1): 33-36.
[28]  佘二永, 王润生, 徐学文. 基于预处理模式的D-S证据理论改进方法[J]. 模式识别与人工智能, 2007, 20(5): 711-715.
[29]  She Eryong, Wang Runsheng, Xu Xuewen. An improved method of D-S evidence theory based on pretreatment mode[J]. Pattern Recognition And Artificial Intelligence, 2007, 20(5): 711-715.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133