Screening Voltage Disturbance Sources Using Aggregated Data from Power Quality Measurement Systems

In recent years, power quality (PQ) measurement system have become essential for enhancing the performance and reliability of power distribution networks. The number of PQ analyzers installed in these networks has increased significantly. Moreover, PQ measurements are now often integrated into various devices, including balance and industrial energy meters. However, the sheer volume of data generated by these meters makes manual inspection and analysis impractical. This highlights the need for advanced automated tools to manage and interpret these vast datasets. This paper presents an approach that uses aggregated 10-minute PQ measurement data from these meters as a screening method to locate sources of PQ disturbances. By employing both deterministic indicators and machine learning techniques, specifically neural network classification, this approach opens new possibilities for automating the analysis process, enabling network operators to effectively identify and prioritize significant sources of disturbances in distribution networks.