Research on the Influence Factors System of Human Error in Power System

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Abstract:

Along with the development of science and technology, equipment reliability is improving and human error has become an increasingly important threat to the power system reliability and safety. However, there is seldom research for the human errors in electric power generation. In this paper, the classification and the main causes of human errors in power system are analyzed firstly. Then, the influence factors of human error are divided into several groups, which are organizational factors, mission factors, individual factors, environment and equipment factors. By analyzing the impact of different influence factors, an influence factors system of human error in power system is proposed and lays a foundation for the further explorations.

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687-690

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July 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Y.J. Guo, Reliability of power systems and power equipment, Automation of Electric Power Systems. 25(2001)53-56.

Google Scholar

[2] E. Hollnagel, Cognitive Reliability and Error Analysis Method. Elsevier, Ox-ford, (1998).

Google Scholar

[3] Reason J, Human error. Cambridge university press, UK. Cambridge, (1990).

Google Scholar

[4] Chang Y H J, Mosleh A. Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: Part 1: Overview of the IDAC Model, Reliability Engineering & System Safety. 92(2007)997-1013.

DOI: 10.1016/j.ress.2006.05.014

Google Scholar

[5] Chang Y H J, Mosleh A. Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 2: IDAC performance influencing factors model, Reliability Engineering & System Safety. 92(2007)1014-1040.

DOI: 10.1016/j.ress.2006.05.010

Google Scholar

[6] Li P, Chen G, Dai L, et al. A fuzzy Bayesian network approach to improve the quantification of organizational influences in HRA frameworks, Safety science. 50(2012)1569-1583.

DOI: 10.1016/j.ssci.2012.03.017

Google Scholar

[7] Y. Fujita, E. Hollnagel, Failures without errors: quantification of context in HRA, Reliability Engineering & System Safety. 83(2004)145–51.

DOI: 10.1016/j.ress.2003.09.006

Google Scholar

[8] Groth, Katrina M., Ali Mosleh. A data-informed PIF hierarchy for model-based Human Reliability Analysis, Reliability Engineering & System Safety. 108 (2012)154-174.

DOI: 10.1016/j.ress.2012.08.006

Google Scholar

[9] Lee S W, Kim A R, Ha J S, et al. Development of a qualitative evaluation framework for performance shaping factors (PSFs) in advanced MCR HRA, Annals of Nuclear Energy. 38(2011) 1751-1759.

DOI: 10.1016/j.anucene.2011.04.006

Google Scholar

[10] H.B. Lu, M. Wang, C.X. Guo, et al. A quantitative method for human reliability in power system based on CREAM, Power System Protection and Control. 41(2013)37-42.

Google Scholar

[11] Williams J C. A data-based method for assessing and reducing human error to improve operational performance. Human Factors and Power Plants, Conference Record for 1988 IEEE Fourth Conference on. IEEE, (1988)436-450.

DOI: 10.1109/hfpp.1988.27540

Google Scholar