基于人工免疫系统的模式识别与电力变压器故障诊断
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摘要
人工免疫系统是近年新兴的一种智能计算方法,是当前的一个研究热点,故障诊断可视为模式识别问题,也是人工免疫系统的重要应用领域之一。大型电力变压器的运行状况直接影响着电力系统的安全、可靠运行,其故障诊断技术研究具有十分重要的理论和实际意义。
论文分析综述了人工免疫系统和模式识别有关理论和方法,在国际标准数据测试集上,应用自组织抗体网络和抗体生成算法完成了模式识别仿真实验,测试结果验证了依据该方法进行识别的可行性和有效性,基于变压器油中溶解气体分析数据,研究了的电力变压器故障诊断有关问题。
Artificial immune system (AIS) is a new kind of intelligent computation methods, has been a hot point of computational intelligence. Fault diagnosis can be considered as a pattern recognition problem, and it is an important application area of AIS. An accurate assessment of power transformer state analysis and its fault diagnosis technology has important theoretical significance and practical application value.
This paper analyzed and summarized relevant theory and method of AIS and pattern recognition. The self-organization antibody net (soAbNet) and the antibody generation algorithm have been tested on some benchmark data sets from the UCI repository, test results show that the method is feasibility and effectiveness. Faults diagnosis of power transformers is discussed based on the antibody generation algorithm and the soAbNet.
论文分析综述了人工免疫系统和模式识别有关理论和方法,在国际标准数据测试集上,应用自组织抗体网络和抗体生成算法完成了模式识别仿真实验,测试结果验证了依据该方法进行识别的可行性和有效性,基于变压器油中溶解气体分析数据,研究了的电力变压器故障诊断有关问题。
Artificial immune system (AIS) is a new kind of intelligent computation methods, has been a hot point of computational intelligence. Fault diagnosis can be considered as a pattern recognition problem, and it is an important application area of AIS. An accurate assessment of power transformer state analysis and its fault diagnosis technology has important theoretical significance and practical application value.
This paper analyzed and summarized relevant theory and method of AIS and pattern recognition. The self-organization antibody net (soAbNet) and the antibody generation algorithm have been tested on some benchmark data sets from the UCI repository, test results show that the method is feasibility and effectiveness. Faults diagnosis of power transformers is discussed based on the antibody generation algorithm and the soAbNet.
引文
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[12]钱政,杨莉,张冠军,严璋.基于模糊推理与模糊集理论的电力变压器故障诊断方法.电工电能新技术, 1999, (3):36~39
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[14]符杨,江玉蓉,崔椿洪,曹家麟.基于模糊数学和概率论的变压器故障诊断.高电压技术, 2008, 34(5):1040~1044
[15]熊浩,李卫国,畅广辉,郭惠敏.模糊粗糙集理论在变压器故障诊断中的应用.中国电机工程学报, 2008, 28 (7):141~147
[16]王永强,律方成,李和明.基于贝叶斯网络和DGA的变压器故障诊断.高电压技术, 2004, 30(5):12~14
[17]吴立增,朱永利,苑津莎.基于贝叶斯网络分类器的变压器综合故障诊断方法.电工技术学报, 2005, 20(4):19~26
[18]朱永利,吴立增,李雪玉.贝叶斯分类器与粗糙集相结合的变压器综合故障诊断.中国电机工程学报, 2005, 5(10):159~165
[19]赵文清,朱永利,姜波,等.基于贝叶斯网络的电力变压器状态评估.高电压技术, 2008, 34(5):1032~1039
[20]邱仕义.电力设备可靠性维修.北京:中国电力出版社,2004.
[21]莫宏伟,人工免疫系统原理与应用.哈尔滨工业大学出版社.2002年
[22]赵俊忠,黄厚宽,田盛丰.免疫机制在计算机网络入侵检测中的应用研究.计算机研究与发展,2003,vol.40,No.9:1293~1299
[23] D Dasgupta,Z Ji,F GonZalez. Artificial Immnune System (AIS) Research in the Last Five years. The 2003 Congress on Evolutionary Computation,2003(l):123-130.
[24]李海峰,王海风,陈珩.免疫系统建模及其在电力系统电压调节中的应用.电力系统自动化,2001,25(23):17一23.
[25] K Takahashi. Remark on a Feedback Conortller Based on an Immune Feedback Mechanism.USA Symposium on Flexible Automation,Otsu,Japan,1998:777-783.
[26] M Kawafuku,M Saskai,K Takahashi. Adpative Lemaing Mehtod of Neuarl Network Controller Using an Immune Feedbaek Law. 6th Intemational Coneferece on Neuarl Inofmration Pocressing,1999:641-646.
[27] J T Otsuki,T Sekiguchi. Application of the Immne System Network Concept to Sequential Conortl. Fourh Intenrational Symposium Autonomous Deeenartlized Systems,OTkyo,Jpana,1999(3):869-874.
[28] Y Ishida. An Immune Network Model and Its Applications to Porcess Diagnosis.System and computers.1993,24(6):38-45.
[29] M Kyama,Y Sugita.et al. Disrtibuted Diagnosis System Combing the Immune Network and Learning Vector QuantiZation. Poreeedings of Intematinoal Coneferce on Industrial Eleeortnies , Conortl , and Instrumenattion , orlnado , FL , USA ,1995(2):1531-1536
[30] A Ishiguro,Y Watanabe,Y Uhcikawa. Fualt Diganosis of Plant Systems Using Immune Network. Porc IEEE Intemational Conefernce no Multi-Sensor Fusion and Integration for Intelligent Systems,Las Vegas,NV, 1994:34-42
[31]庄健,王娜,杜海峰,等.一种模糊人工免疫网络故障诊断策略.自然科学进展, 2007, 17(11):1544~1554
[32] L N de Casort,F J Von Zuben. Leaning and Optimization Using the Clonal Selection Principle. IEEE Trnas on Evolutionay Cmoputation. 2002,6(3): 239-251.
[33] D Dasgupt,Y Cao,C Yang. Immnunogenetic Approach to Spectra Recongition. Pore GECCO’99,San Francisco,CA,USA,1999:149-155
[34] S Foerrst,S A Hofmeyr. Immunology as information Processing. In: Segel and Cohen Eds. Design Principle for the Immune Sysetm and Other Disrtibuted AuotnomousSysetms.USA: Oxford Univesrity Press,2000.
[35] D F McCoy, V Devarajna. Artifieial Immune Systems and Aerial lmage Segmenatation.Pore 1997 IEEE Inter Conf on system,1997.
[36] J S Chun,H KJung,S Y Hahn. A Study on Comparison of Optimization performance bewteen Immune Algorithm and Other Heuristie Algorithms. IEEE Trnasactions no Mangetics,1998 34(5):2972-2975.
[37] I Tazawa,S Koakutsu,H Hirata. An Evolutionary Optimization Based on the Immune System and Its APPlieation to the VLSI Floor-Plan Design Problem. Eleertieal Engineering in Jpana,1998,124(4):27-36.
[38] S Endoh,N Toma,K Yamada. Immune Algorithm for n-TSP. IEEE Intemational Conefrenee on System,Mna and Cybemetics. San Diego,Caliofnria,USA,1998(4):3844-3849.
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