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Power distribution system fault cause analysis by using association rule mining

机译：关联规则挖掘的配电系统故障原因分析

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In recent years, with the increasing requirements on power distribution utilities to ensure system reliability and to improve customers and regulators satisfaction, utilities seek to find practical solutions that enable them to restrict specific faults or to better manage their responses to unavoidable power outages. For achieving either, it is crucial to acquire a profound understanding of different faults by exploring their underlying causes and identifying key variables related to those causes. Currently, statistical models as well as advanced data analytics techniques are common tools to gain such understanding. Although basic statistical analysis provides a general knowledge of the primary causes of faults; nevertheless, it falls short of describing nuanced conditions that lead to a fault. On the other hand, applying sophisticated algorithms can produce deeper insight into the main causes; however, it would be computationally burdensome and might require a tremendous amount of running time. In order to overcome these problems, this paper proposes a novel approach for fault cause analysis by using association rule mining. The primary goals are to characterize faults according to their underlying causes and to identify important variables that strongly impact fault frequency. This paper proposes a step-by-step procedure, which deals with data preparation, practical issues associated with fault data sets, and implementation of association rule mining. The procedure is followed by a comprehensive case study to demonstrate how the proposed approach can be used to mine for causal structures and identify frequent patterns for vegetation, animal, equipment failure, public accident, and lightning-related faults. (C) 2017 Elsevier B.V. All rights reserved.
机译：近年来，随着对配电公用事业的要求不断提高，以确保系统可靠性并提高客户和监管机构的满意度，公用事业寻求寻求实用的解决方案，以使他们能够限制特定的故障或更好地管理其对不可避免的断电的响应。为了实现这两者，至关重要的是，通过探索不同的根本原因并确定与这些原因相关的关键变量，来深刻理解不同的故障。当前，统计模型以及高级数据分析技术是获得这种了解的常用工具。尽管基本的统计分析提供了故障主要原因的一般知识；但是，它未能描述导致故障的细微差别。另一方面，应用复杂的算法可以更深入地了解主要原因。然而，这将在计算上繁重并且可能需要大量的运行时间。为了克服这些问题，本文提出了一种使用关联规则挖掘进行故障原因分析的新方法。主要目标是根据故障的根本原因来表征故障，并确定会严重影响故障频率的重要变量。本文提出了一个分步过程，涉及数据准备，与故障数据集相关的实际问题以及关联规则挖掘的实现。在此程序之后，将进行全面的案例研究，以演示如何使用拟议的方法来挖掘因果结构，并确定植被，动物，设备故障，公共事故和与闪电相关的断层的常见模式。 （C）2017 Elsevier B.V.保留所有权利。

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