A Study for Cause Estimation of Faults Using Statistical Analysis

In distribution power systems, these are many lines and equipment. Therefore, a lot of time and work are required to find the fault points when a power failure is occurred in a distribution line. This paper focuses on a cause estimation method of faults to assist finding a fault point. Specifically, this paper proposes an estimation method based on a statistical analysis to improve estimation accuracy, and confirm the effectiveness of the proposed method by verification using measurement data obtained by switches with sensors. INTRODUCTION In general, when a power failure occurs in a distribution line, the location of the fault and the equipment that caused a power failure, that is the point of the fault, needs to be determined for early power recovery. In a current system to determine the fault range, when the system detects a power failure by a protective relay at a substation, the system stops supplying electricity to the distribution line in which the power failure occurs, and narrows down the range of the power failure to some extent by a timed-sequential transmission method. After that, a fault point is specified by patrols and investigation of leakage current by using superimposed voltage. The steps up to narrowing down the range of the power failure by the timed-sequential transmission method are done automatically by the system and these steps are completed in a matter of minutes. On the other hand, the patrols and the investigation are done by workers, and require several tens of minutes to several hours. In mountainous regions in particular, distribution lines are long and the range to narrow down by the system tends to be larger, so the time needed to find a fault point also tends to become longer. In this background, the fault localization methods [1] to specify the point where the fault occurred when a power failure occurs and the methods [2]-[5] to estimate a cause of fault have attracted attention, and various methods have been proposed. On the other hand, many technical issues still remain in response to a wide variety of faults in distribution lines, such as the fault caused by breakdown of underground cables or burnout of a transformer. An extraction of feature quantity by a waveform analysis to estimate a cause of fault, in particular, has been systemized, but the extraction largely depends on rules of thumb in the process of estimating causes based on the feature quantity. How the rules of thumb should be theoretically evaluated has been an issue. However, in recent years, switches with sensors, which have various measurement functions, has been introduced in distribution lines and communication infrastructure such as optical lines has been developed. In addition, circumstances that enables smooth recording and accumulation of voltage and current waveform data and application of statistical analysis have been gradually established. Taking into account such a background, this paper proposes the method to estimate a cause of faults by applying a statistical analysis to a certain number of current waveform data obtained by switches with sensors, without using a collation table of feature quantity and causes of faults, which has been created based on rules of thumb. EXTRACTION OF FEATURE QUANTITY Various methods have been proposed for extracting features of waveforms as values and evaluating them [2]-[5]. Of these methods, this paper introduces the analysis method based on phase-plane trajectory, focusing on evaluating visual shapes of waveforms [2]. This method estimates causes of faults through an extraction of feature quantity from a current waveform (Fig. 1). First, time derivatives for the current waveform data shown in Fig.1 (a) is obtained at regular intervals. Next, the plot graph called the phase-plane trajectory graph as shown in Fig.1 (b) is made. A horizontal axis shows crest values and a vertical axis shows time derivatives. In order to evaluate the phase-plane trajectory graph numerically, the graph is divided into 64 cells as shown in Fig.1 (c) and the number of points plotted in each cell is counted. Then the number of plots is extracted in a specific cell such as the region A and B in Fig.1 (c) and a scatter diagram is created as shown in Fig.1 (d), which allows you to evaluate the shapes of accident waveforms and estimate the cause. This method has an advantage that visual features of waveform data such as steepness of the waveform gradient, square waves and triangle waves can be numerically expressed easily. On the other hand, in the same way as other techniques, a collation table that links the feature quantity of waveforms and causes of faults needs to be created in advance (for example, when needle-like waves are linked continuously, a cause of a fault is likely to be cable ground fault). For this reason, the improvement of methods to create a collation table has been an important issue, as well as the accumulation 24th International Conference on Electricity Distribution Glasgow, 12-15 June 2017