Method for identifying OPGW cable vibration events based on optimized imbalanced data

Fiber optic composite overhead ground wire (OPGW) bears the important task of transmitting power data and guaranteeing the safety of the power grid, but OPGW cables in the process of operation will often be due to bird pecking, wind dance, ice and other anomalies that affect the normal operation of the cable. Therefore, the identification of abnormal events affecting the operation of fiber optic cables is crucial to the operation of the power system. However, due to geographic and environmental factors, the collected cable anomaly data is often unbalanced, which affects the performance of the classifier and leads to misclassification. Aiming at such problems, this paper proposes a data optimization method that combines improved K-means and Borderline-SMOTE. After calculating the data density and Euclidean distance of the fiber optic cable vibration data collected by phase-sensitive optical time-domain reflectometer (φ-OTDR), the data is divided into clusters by using the improved K-means algorithm, and the samples of a few classes that are easy to be misclassified at the decision boundary of each cluster are identified, and the samples of such classes are classified by using the improved K-means algorithm. A few classes of samples are identified at the decision boundary in each cluster, and such samples are oversampled using the Borderline-SMOTE method to balance the data volume of various types of anomalous events. The experimental results show that the accuracy, recall and F1 score of the three classes of machine learning algorithms combined with this method are improved by more than 5% compared with the traditional methods.