• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

电力建设 ›› 2017, Vol. 38 ›› Issue (7): 131-.doi: 10.3969/j.issn.1000-7229.2017.07.016

• 智能电网 • 上一篇    下一篇

一种基于体液免疫原理的电网故障诊断模型设计方法 
 

 王守鹏,赵冬梅    

  1.  (华北电力大学电气与电子工程学院,北京市 102206)  
     
  • 出版日期:2017-07-01
  • 作者简介:王守鹏(1987), 男, 博士研究生,本文通信作者, 研究方向为电力系统分析、控制与保护,电网故障诊断; 赵冬梅(1965), 女, 博士, 教授, 博士生导师, 研究方向为智能技术在电力系统中的应用、电网故障诊断。
  • 基金资助:
     国家自然科学基金项目(51377054);中央高校基本科研业务费专项资金资助项目(2017XS019)  Project supported by National Natural Science Foundation of China (51377054); The Fundamental Research Funds for the Central Universities (2017XS019)   
     

  A Design Method for Power Grid Fault Diagnosis Model Based on Humoral Immunity Principle 
 

 WANG Shoupeng, ZHAO Dongmei    

  1.  (School of Electrical and Electronic Engineering, North China Electric  Power University, Beijing 102206, China) 
     
  • Online:2017-07-01
  • Supported by:
      Project supported by National Natural Science Foundation of China (51377054); The Fundamental Research Funds for the Central Universities (2017XS019)   
     

摘要:  摘要:该文借鉴体液免疫应答原理解决当前电网故障诊断所面临的故障信息不确定性问题。首先,构建了体液免疫应答过程与电网故障诊断过程所涉及的基本量的对应关系。其次,模拟体液免疫抵御抗原入侵的免疫机制和结构,构建了基于体液免疫应答机制的电网故障诊断模型,该诊断模型具有较强的容错能力,不仅可以根据先验知识诊断出已知的故障,而且能够通过系统的连续学习功能诊断未知的故障。最后,通过诊断算例验证了所构建诊断模型的有效性与可行性。 

 

关键词:  , 故障诊断, 警报信息, 体液免疫, 学习进化 

Abstract:  ABSTRACT:   According to the uncertainty of fault information in the power grid fault diagnosis, this paper draws lessons from the humoral immune response mechanism addressing the problem. Firstly, we establish the relationship between the process of the humoral immune response and the basic amount of the power grid fault diagnosis. Secondly, we construct a fault diagnosis model based on humoral immune response mechanism via simulating the mechanism and structure of humoral immunity resisting the invasion of antigens. The proposed model has a higher fault-tolerant ability, which can not only detect known faults according to the prior knowledge, but also judge unknown faults through the continuous learning function of the fault diagnosis system. Finally, the results of the test cases of the fault diagnosis show that the proposed model is feasible and efficient. 

 

Key words:  fault diagnosis, alarm information, humoral immunity, learning evolution  

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