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Power grid stability analysis using pipeline machine
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DOI：10.1007/s11042-023-14384-3.png)
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摘要
En 中文
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.
Keyword:
Power grid stability assessment
Machine learning techniques
Algorithm
Energy information
Database
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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