Hybrid analytical and data-driven model based instance-transfer method for power system online transient stability assessment
Data-driven methods are widely recognized and generate conducive results for online transient stability assessment. However, the tedious and time-consuming process of sample collection is often overlooked. The functioning of power systems involves repetitive sample collection due to the constant var...
Main Authors: | Li, Feng, Wang, Qi, Tang, Yi, Xu, Yan, Dang, Jie |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/181642 |
Similar Items
-
Hybrid method for power system transient stability prediction based on two-stage computing resources
by: Tang, Yi, et al.
Published: (2018) -
Online learning using deep random vector functional link network
by: Shiva, Sreenivasan, et al.
Published: (2024) -
Ensemble-Based Risk Scoring with Extreme Learning Machine for Prediction of Adverse Cardiac Events
by: Liu, Nan, et al.
Published: (2018) -
Bayesian network based extreme learning machine for subjectivity detection
by: Chaturvedi, Iti, et al.
Published: (2018) -
An ensemble approach for short-term load forecasting by extreme learning machine
by: Li, Song, et al.
Published: (2017)