Abstract
The problems associated with the stability analysis of power system are very important and has a wide scope of improvement. Severity of the transient disturbances arising in power system are usually studied through critical contingencies simulation. There proper study and assessment is extremely important for a reliable, uninterrupted operation, along with ensuring that no generating unit get desynchronized. The main objective of this research is to develop a fast and robust online transient stability assessment tool to classify the system operating states and to identify system critical generators in case of instability. This research proposes a pipeline machine learning multi-feature hybrid network framework that captures the phasor measurement unit (PMU) measurements and monitor the system transient stability in real-time. The test results verified that our proposed framework is fast and accurate, thereby a viable approach for system stability monitoring applications.
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Acknowledgements
This work supported by ”Key Laboratory of Wavelet Active Media Technology” with the National Natural Science Foundation of China (Grant No. 61370073), the National High Technology Research and Development Program of China (Grant No. 2007AA01Z423), Room No: B1301, School of Computer Science, University of Electronic Science and Technology of China (UESTC), No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P. R. China.
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Appendix
In Table 7 mathematical notions are described.
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Khan, A., Li, J.P. & Husain, M.A. Power grid stability analysis using pipeline machine. Multimed Tools Appl 82, 25651–25675 (2023). https://doi.org/10.1007/s11042-023-14384-3
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DOI: https://doi.org/10.1007/s11042-023-14384-3