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高压电器  2015 

基于遗传算法改进极限学习机的变压器故障诊断

DOI: DOI:10.13296/j.1001-1609.hva.2015.08.008, PP. 49-53

Keywords: 变压器,三比值法,遗传算法,极限学习机,故障诊断

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Abstract:

针对变压器故障的特征,结合变压器油中气体分析法以及三比值法,提出了基于遗传算法改进极限学习机的故障诊断方法。由于输入层与隐含层的权值和阈值是随机产生,传统的极限学习机可能会使隐含层节点过多,训练过程中容易产生过拟合现象。该方法运用遗传算法对极限学习机的输入层与隐含层的权值与阈值进行优化,从而提高模型的稳定性和预测精度。将诊断结果与传统的基于极限学习机故障诊断进行对比,结果表明,基于遗传算法改进极限学习机变压器故障诊断的精度更高。

References

[1]  刘虎威. 气相色谱方法及应用[M]. 北京:化学工业出版社,2000:1-2. LIU Huwei. Chromatography method and its application[M]. Beijing:Chemical Industry Press,2000:1-2.
[2]  潘 超,马成廉,郑玲峰,等. 一种结合模糊TOPSIS法和BP神经网络的变压器故障诊断方法[J]. 电力系统保护与控制,2009,37(9):20-24. PAN Chao,MA Chenglian,ZHENG Lingfeng,et al. A new method based on fuzzy TOPSIS and BP neural network for power transformer fault diagnosis[J]. Power System Protection and Control,2009,37(9):20-24.
[3]  国家质量监督检验疫总局. GB/T 7252―2001 变压器中溶解气体分析和判断导则[S].2002. State General Administration of Quality Supervision, Inspection and Quarantine. GB/T 7252―2001 Transformer dissolved gas analysis and judgment guidelines[S].2002.
[4]  李 中,苑津莎,张利伟. 基于自组织抗体网络的电力变压器故障诊断[J]. 电工技术学报,2010,25(10):200-206. LI Zhong,YUAN Jinsha,ZHANG Liwei. Fault diagnosis for power transformer based on the self-organization antibody net[J]. Transactions of China Electrotechnical Society,2010,25(10):200-206.
[5]  朱 浪,王 蕾,潘 丰. 基于BP神经网络的变压器故障诊断[J]. 江南大学学报(自然科学版),2012,11(3):262-266. ZHU Lang,WANG Lei,PAN Feng. Fault diagnosis of transformer based on BP neural network[J]. Journal of Jiangnan University(Natural Science Edition),2012,11(3):262-266.
[6]  崔东君,刘 念,刘秀兰. 基于加权小波神经网络的油浸式电力变压器故障检测[J]. 电力系统保护与控制,2010,38(18):19-23. CUI Dongjun,LIU Nian,LIU Xiulan. Fault diagnosis of oil-immerse power transformer based on weighted wavelet neural network[J]. Power System Protection and Control,2010,38(18):19-23.
[7]  HUANG G B,ZHU Q Y,SIEW C K. Extreme learning machine:Theory and applicant[J]. Neurocomputing,2006,70(1/2/3):489-501.
[8]  DENG W,CHEN L. Color imagewatermarking using regularized extreme learning machine[J]. Neural Network World,2010,20(3):317-330.
[9]  ZONG Weiwei,HUANG Guangbin. Face recognition based on extreme learning machine[J]. Neurocomputing,2011,74(16):2541-2551.
[10]  TANG Xiaoliang,HAN Min. Partial lanczos extreme learning machine for single-output regression problems[J]. Neurocomputing,2009(72):3066-3076.
[11]  毛 力,王运涛,刘兴阳,等. 基于改进极限学习机的短期电力负荷预测方法[J]. 电力系统保护与控制,2012,40(20):140-144. MAO Li,WANG Yuntao,LIU Xingyang,et al. Short-term power load forecasting method based on improved extreme learning machine[J]. Power System Protection and Control,2012,40(20):140-144.
[12]  HUANG Guangbin,CHEN Lei. Convex incremental extreme learning machine[J]. Neurocomputing,2007,70(16/18):3056-3062.
[13]  雷英杰,张善文,李续武,等. MATLAB 遗传算法工具箱及应用[M]. 西安:西安电子科技大学出版社,2005.
[14]  周 明,孙树栋. 遗传算法原理及应用[M]. 北京:国防工业出版社,1999.
[15]  李凤婷,晁 勤. 基于Matlab与遗传算法的风电容量[J].电工技术学报,2009,24(3):178-182. LI Fengting,CHAO Qin. Wind power capacity based on matlab and genetic algorithm[J]. Transactions of China Electrotechnical Society,2009,24(3):178-182.
[16]  史 峰,王 辉,郁 磊,等. MATLAB智能算法30个案列分析[M]. 北京:北京航空航天大学出版社,2011.
[17]  谢春娣,梅家斌. 遗传算法在神经网络权值优化中的应用[J]. 中南民族学院学报,2001,20(z1):1-3. XIE Chundi,MEI Jiabin. The appliance of hereditary algorithm in nerve-net weights-optimizing[J]. Journal of South-Central University,2001,20(z1):1-3.
[18]  段侯峰. 基于遗传算法优化BP神经网络的变压器故障诊断[D]. 北京:北京交通大学,2008.

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