Methods for voltage sag source location by Cluster Algorithm and Decision Rule Labeling with a Comparative Approach of K-means and DBSCAN Clustering Algorithms

The voltage sag disturbance stands out as the most evident waveform change that is detected in electric networks, since the presence of these events in the network causes damages to the consumers. The first step in diagnosing the problem is to identify the location in the distribution system that is connected to the source causing the sinking disorder. This work presents a methodology based on clustering algorithm combined with decision rule to point out the region (cluster) that aggregates the place of origin. Clustering algorithm is responsible for analyzing the voltage signal data from different measurement nodes and separating these data into clusters. Then the Partial Decision Trees (PART) algorithm is responsible for defining the decision rule set that will confront the characteristics of each cluster and define which group aggregates the disturbance source location. For the clustering task, the k-means and DBSCAN clustering algorithms are evaluated and compared. The methodology was evaluated using the IEEE 34-bus test feeder system and the results show a hit rate higher than 98%.