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Analysis and Suppression Strategy of Sub-synchronous Oscillation for Wind-Thermal-Bundled Power Transmitted by Series-Compensated System Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Key Constraint Variable Identification, Transient Stability Assessment and Feasible Region Generation of Power Grid Operation Based on Machine Learning Method

Authors : Binjiang Hu, Qi Guo, Yihua Zhu

Published in: Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control

Publisher: Springer Singapore

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Abstract

The existing machine learning methods for the decision-making of power grid operation lack sufficient interpretability and focus on transient stability assessment. In response to this deficiency, this paper analyzed the process of humankind learning and decision-making in the field of power grid operation. To further speed up the decision making process, a model which combines correlation analysis, deep learning and decision tree used for key constraint variable identification, transient stability assessment and feasible region generation is proposed. This model is able to take advantage of feature extraction and fitting effect of deep learning, good interpretability of correlation analysis and decision tree. Experiment results on a subsystem in China Southern Power Grid demonstrate the feasibility and effectiveness of the model.

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previous chapter Comprehensive Evaluation of Power Quality via Sequential Floating Search Method
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Metadata
Title
Key Constraint Variable Identification, Transient Stability Assessment and Feasible Region Generation of Power Grid Operation Based on Machine Learning Method
Authors
Binjiang Hu
Qi Guo
Yihua Zhu
Copyright Year
2020
Publisher
Springer Singapore
Book
Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control

Print ISBN: 978-981-13-9782-0

Electronic ISBN: 978-981-13-9783-7

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-13-9783-7_49