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文章摘要
基于改进灰色预测与熵权融合体系的变压器寿命预测研究
Transformer life prediction model based on the improved grey forecasting and entropy method
Received:July 23, 2016  Revised:October 22, 2016
DOI:
中文关键词: 变压器  寿命预测  灰色预测  熵权法  健康指数  指标体系
英文关键词: transformer, life  prediction, grey  forecasting, entropy  method, health  index
基金项目:
Author NameAffiliationE-mail
Li Yice College of Power Mechanical Engineering,Wuhan University liyice@whu.edu.cn 
Guo Jiang* College of Power Mechanical Engineering,Wuhan University guo.river@whu.edu.cn 
Zhang Kefei College of Power Mechanical Engineering,Wuhan University zhang.kefei@qq.com 
Cai Wei College of Power Mechanical Engineering,Wuhan University 410163936@qq.com 
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中文摘要:
      变压器是电能转化的关键设备,其安全可靠运行对电网发挥重要作用。变压器材料老化和部件损耗都严重影响变压器的剩余寿命,继而影响电力系统的安全稳定运行,因此,对变压器健康状况和剩余寿命的研究越来越受到关注。本文提出了一种基于熵权融合体系与改进灰色预测理论的变压器寿命预测模型。该模型采用的健康指标体系基于熵权法对变压器层次分析和可靠性指标两个方面进行了融合,并充分考虑运行环境、负荷情况与故障缺陷等相关因素,提出了适应于运行实际的健康指数修正因子,提高了变压器健康状况评估的全面性及客观性。通过实例验证表明,模型预测结果合理有效,能够对变压器寿命预测和检修决策工作的开展提供指导与建议。
英文摘要:
      Transformer is one of the critical equipment for electric power transmission and distribution, and its safety situation plays a great effect on stability and security level of power system. The material aging and equipment deterioration can both affect the remaining life immensely, and then influence the stability of power system. Therefore, researches on health situation and remaining life of transformers are receiving more attention in academia. The article proposes a transformer life prediction model based on the improved grey forecasting and entropy method. The health index system of the model takes advantage of both hierarchical analysis system and reliability index system, which could overcome the limits of each system. Considering the transformer-life-related factors, such as operating environment, load rate and fault or defects, the model puts forward several modifying factor of health index, which can greatly improve the assessment ability in transformer health situation. The efficiency of the model is successfully verified by examples, and the model can provide useful guidance and suggestion in the decision-making work and has a great potential in the application of transformer life prediction.
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