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Power grid stability analysis using pipeline machine
by
Li, Jian Ping
, Husain, Mohammed Aslam
, Khan, Asif
in
Artificial intelligence
/ Computer engineering
/ Electrical engineering
/ Electricity
/ Electricity distribution
/ Machine learning
/ Measuring instruments
/ Multimedia
/ Phasors
/ Real time
/ Stability analysis
/ Supply & demand
/ Systems stability
/ Transient stability
2023
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Power grid stability analysis using pipeline machine
by
Li, Jian Ping
, Husain, Mohammed Aslam
, Khan, Asif
in
Artificial intelligence
/ Computer engineering
/ Electrical engineering
/ Electricity
/ Electricity distribution
/ Machine learning
/ Measuring instruments
/ Multimedia
/ Phasors
/ Real time
/ Stability analysis
/ Supply & demand
/ Systems stability
/ Transient stability
2023
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Do you wish to request the book?
Power grid stability analysis using pipeline machine
by
Li, Jian Ping
, Husain, Mohammed Aslam
, Khan, Asif
in
Artificial intelligence
/ Computer engineering
/ Electrical engineering
/ Electricity
/ Electricity distribution
/ Machine learning
/ Measuring instruments
/ Multimedia
/ Phasors
/ Real time
/ Stability analysis
/ Supply & demand
/ Systems stability
/ Transient stability
2023
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Journal Article
Power grid stability analysis using pipeline machine
2023
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Overview
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.
Publisher
Springer Nature B.V
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