Due to the importance of electrical grid reliability, analysis and evaluation of human error in the maintenance of electrical networks should be also considered seriously. The root causes of these errors must be identified and prioritized to plan for human error reduction. One of the objectives of the present study is to identify and predict these roots for power transmission maintenance groups from organizational, job position, communication, individual, and supervision aspects along with the relationships between these factors. In particular, this paper demonstrates that supervisor behavior as an external factor has a significant effect on maintenance personnel error. For this reason, special attention has been paid to identifying and controlling human factors from a supervisory point of view in this study. This paper also provides a method for detecting the extent of the expected influence of these roots on each personnel, since human error has a random nature. This is done based on the law of mathematical expectation. Finally, a method is suggested to rank roots based on greater effectiveness and evaluate personnel with higher error expectations. The proposed method is a combination of intermediate methods, Shannon entropy, and technique for order of preference by similarity to ideal solution (TOPSIS). The origins of the four human errors between 2014 and 2018 related to the two experts of Fars Electricity Maintenance Contractor Company are compared by the proposed method.

中文翻译：

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基于数学期望、香农熵和TOPSIS的人为误差根对电网维护组影响的估计和排序

由于电网可靠性的重要性，还应认真考虑对电网维护中人为错误的分析和评估。必须确定这些错误的根本原因并确定其优先级，以计划减少人为错误。本研究的目标之一是从组织、工作职位、沟通、个人和监督方面以及这些因素之间的关系来识别和预测输电维修组的这些根源。特别是，本文证明了作为外部因素的主管行为对维护人员错误有显着影响。因此，本研究从监督的角度特别注意识别和控制人为因素。本文还提供了一种检测这些根对每个人员的预期影响程度的方法，因为人为错误具有随机性。这是基于数学期望定律完成的。最后，提出了一种基于更高效率对根进行排序并评估具有更高错误预期的人员的方法。所提出的方法是中间方法、香农熵和通过与理想解的相似性（TOPSIS）进行偏好排序的技术的组合。2014年至2018年与法尔斯电力维修承包商公司两位专家相关的四次人为错误的根源通过所提出的方法进行比较。最后，提出了一种基于更高效率对根进行排序并评估具有更高错误预期的人员的方法。所提出的方法是中间方法、香农熵和通过与理想解的相似性（TOPSIS）进行偏好排序的技术的组合。2014年至2018年与法尔斯电力维修承包商公司两位专家相关的四次人为错误的根源通过所提出的方法进行比较。最后，提出了一种基于更高效率对根进行排序并评估具有更高错误预期的人员的方法。所提出的方法是中间方法、香农熵和通过与理想解的相似性（TOPSIS）进行偏好排序的技术的组合。2014年至2018年与法尔斯电力维修承包商公司两位专家相关的四次人为错误的根源通过所提出的方法进行比较。