Detection of power transmission lines faults based on voltages and currents values using K-nearest neighbors

Abstract

The critical factors to consider when implementing a maintenance plan for energy transmission lines are, accuracy, speed, and time, because of the increased global demand for electricity power caused by rapid development, and overuse of electric power transmission lines (both underground cables and overhead transmission lines), which in turn reduces the efficiency of the lines. Consequently, the efficiency of the lines may be reduced as a result of overuse or other activities like excavation that may have tampered with the cables. Thus, it becomes important to investigate the faults to which the lines are exposed. To this end, this article focuses on the detection of fault in transmission lines through the use of k-nearest neighbor algorithm. Using this algorithm, the characteristics were obtained (voltage, current), and these characteristics enable the identification of faults in the transmission lines, and in the specific location (the entire system, phase B, and phase A). The benefits that can be derived from the use of this algorithm include time, accuracy, speed, which are the requirements for the maintenance of transmission lines. Euclidean distance used in the application of the k-nearest neighbor technique for weights, and K = 3 for number of neighbors. The dataset was split into two parts, 70% training set and 30% testing set.