Optimization of Data Processing Schemes for Locating Low-Frequency Oscillation Sources

Abstract

The organization of data processing for measurement data arrays is one of the key factors for the effective application of synchronized phasor measurement technology (PMU) within a Wide Area Monitoring System (WAMS). This encompasses not only ensuring high computational performance and scalability but also the openness of solutions to the continually evolving data analysis methodologies. In both the scientific community and current operational-dispatching practice, there is a growing interest in the issue of power system oscillatory stability. This interest is driven by the increasing complexity of modern power systems and enhanced observability of processes due to the deployment of WAMS. One of the emerging tasks is the real-time detection of low-frequency oscillation (LFO) sources using PMU data. The dissipating energy flow (DEF) method has shown high effectiveness in addressing this task by enabling the assessment of the magnitude, speed, and direction of oscillation energy propagation. It has been previously demonstrated that, while maintaining the general computational framework of the DEF method, the time required to reach a stable decision about the LFO source depends on the choice of the combination of subtask algorithms and their parameters. This study discusses a prototype computational platform for optimizing generalized data processing schemes based on PMU data. Using the DEF method and a dataset of actual LFOs in the Russian power system as an example, the study explores mechanisms for expanding the solution space and the measurement data base. The paper presents experimental results that provide an estimation of the expected time from the onset of oscillations to the identification of their source.