This paper presents a data-driven approach for event classification via a regional segmentation of power systems. The data-driven approach is suitable for the complex power systems under transient conditions, as it directly derives the information from the measurement signal database instead of modeling transient phenomena. However, measurement conditions of real-world power system will have unavoidable missing and bad data. Thus, it is necessary for data-driven model to have a robustness and adaptability about varying environment as well as system configurations and measurement conditions. In this work, the clustering-based regional segmentation of power systems is adopted to improve robustness of the data driven model by maintaining the fixed-input-feature format under varieties of measurement conditions. The clustering technique is applied to electrical buses for regional segmentation, and proposed features of phasor measurement unit (PMU) signals are extracted by integrating PMUs in each region based on wavelet analysis. As a result, the regional segmentation achieves improvement of data driven method for event classification with reduced number of calculations and management of bad data. Finally, we verify the event classification algorithm through a case study and analyze the performance of the algorithm for noise and computation time in addition to classification accuracy.
Bibliographical noteFunding Information:
This work was supported in part by the framework of the international cooperation program managed by the National Research Foundation of Korea (NRF) under Grant 2017K1A4A3013579, and in part by the NRF grant funded by the Ministry of Science, ICT and Future .Planning under Grant NRF-2020R1A2B5B03001692.
This work was supported in part by the framework of the international cooperation program managed by the National Research Foundation of Korea (NRF) under Grant 2017K1A4A3013579, and in part by the NRF grant funded by the Ministry of Science, ICT and Future Planning. under Grant NRF-2020R1A2B5B03001692.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)