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The Identification of Human Errors in the Power Dispatching Based on the TRACEr Method

  • Xiaobi Teng
  • Yanyu LuEmail author
  • Zhen Wang
  • Bingbing Song
  • Hai Ye
  • Yi Zhou
  • Shan Fu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10906)

Abstract

Most of the power dispatching accidents were caused by human errors. Human error should be symptoms of systemic problems and opportunities to learn about the features of complex systems. Therefore, the identification and analysis of the human errors in the power dispatching is the significant to guide against the human risk and ensure the stable and safe operation of power nets. Human error identification methods have been used to identify the nature of the human errors and causal factors, and recovery strategies in many industrial domains such as the aviation, nuclear power and chemical processing industries. The Technique for Retrospective and Predictive Analysis of Cognitive Errors (TRACEr) is a human error identification technique that was developed for use in the air traffic control domain. In this study, the TRACEr was improved in the combination of the task features of the power dispatching and human information processing, and was used to identify the human errors in the power dispatching. A total of seventy-two incidents or accidents performed by operators were analyzed. The analyzing processing was carried out with the objective of classifying task error, identifying external error modes, internal error modes and psychological error mechanisms, and identifying the performance shaping factors. The performance factors analysis considered the time, interface, training and experience, procedures, organization, stress and complexity which may have an impact to the task and help to propose some recovery strategies. The results revealed that the identification was a necessary and effective step toward the safety improvement of power dispatching.

Keywords

Human factors Power dispatching Human error identification 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xiaobi Teng
    • 2
  • Yanyu Lu
    • 1
    Email author
  • Zhen Wang
    • 1
  • Bingbing Song
    • 2
  • Hai Ye
    • 2
  • Yi Zhou
    • 2
  • Shan Fu
    • 1
  1. 1.School of Electronics, Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.East China Branch of State Grid Corporation of ChinaShanghaiChina

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