# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 1 Introduction  
The distribution terminal unit (DTU) collects real-time operation data of distribution networks and uploads them to the distribution automation system (DAS) or the supervisory control and data acquisition (SCADA) system. With DTU devices, DAS/SCADA can monitor the operating state of distribution networks and control secondary-side devices remotely, which shortens fault elimination time and reduces the maintenance cost to some extent. Nevertheless, with the development of distribution networks, large-scale integration of DTU also imposes pressure on maintenance work. DTU faults happen frequently while the number of maintenance workers is limited [1, 2, 3, 4, 5]. Moreover, DTU faults may lead to device failure, which would have severe impacts on the distribution network. Therefore, lowering fault incidence and the enhancing fault elimination ability are vital for guaranteeing the safe and stable operation of distribution networks and power systems. During DTU operation, significant fault data is generated and recorded which, however, is not fully utilized. Data mining techniques are effective approaches for analyzing the fault of devices in power systems. The studied methods are mainly based on expert systems [6, 7, 8], state estimation (e.g., the hierarchical clustering method [9], state evaluation [10, 11], and analytical hierarchy process [12, 13, 14, 15]), machine learning (e.g., neural networks [16, 17, 18, 19] and SVM [20, 21, 22, 23]), and association rule mining algorithms [24, 25, 26, 27, 28, 29, 30]. Methods based on expert systems and state estimation involve evaluating devices based on human experience, which is relatively subjective. Machine learning methods and association rule mining algorithmsanalyze faults based on realistic operation data of devices. Therefore, they are more objective compared to the former two methods. Machine learning methods are innovative and effective in detecting and identifying faults. However, they usually require large amounts of high-quality data to achieve good performance. Compared with machine learning, methods based on association rule mining algorithms are not strictly restricted to the amount of data. Many methods based on association rule mining algorithms are designed for power transformers [24; 25; 26; 29; 30]. They are used to find relationships between transformer faults and operation data such as the gas component, voltage, and current. However, these methods are not feasible for DTU because it possesses a specific configuration that is completely different from the transformer. Studies have also utilized association rule mining algorithms to find possible factors that lead to faults of the distribution system [27] and secondary-side devices [28]. These approaches are relatively general rather than specific. Very few studies have analyzed the fault of DTU devices in a comprehensive and objective way. However, large-scale integration of DTU devices makes the investigation of DTU fault analysis necessary and urgent. In order to improve the maintenance efficiency and facilitate the integration of DTU devices, a DTU fault analysis method based on an association rule mining algorithm was proposed. Firstly, the physical configuration of DTU was constructed, and factors leading to DTU fault and fault types were analyzed. Then, three different association rule mining algorithms were evaluated using DTU fault data; the Eclat algorithm, which has the best performance, was selected for this purpose. Afterwards, the DTU fault analysis method was put forward and applied to realistic DTU fault databases. Finally, application examples of this method were illustrated and recommendations for future work were suggested. The unique contributions of this paper are as follows:  
1. This paper studied different aspects of DTU faults (i.e., factors which lead to faults, different fault types, and fault modules of devices); the proposed method can realize fault cause analysis and fault diagnosis, which analyzes DTU faults in a comprehensive way. 2.