Probabilistic Stacked Denoising Autoencoder for Power System Transient Stability Prediction With Wind Farms

To address the uncertainties of renewable energy and loads in transient stability assessment with credible contingencies, this letter proposes a stacked denoising autoencoder (SDAE)-based probabilistic prediction method. The correlations among wind farms have been effectively considered through the...

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Vydáno v:	IEEE transactions on power systems Ročník 36; číslo 4; s. 3786 - 3789
Hlavní autoři:	Su, Tong, Liu, Youbo, Zhao, Junbo, Liu, Junyong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Confidence intervals Monte Carlo simulation Noise reduction Power system stability Probabilistic logic probabilistic prediction Stability analysis Stability criteria stacked denoising autoencoder (SDAE) Statistical analysis Transient analysis Transient stability Wind farms Wind power Wind power generation wind uncertainty
ISSN:	0885-8950, 1558-0679
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Abstract	To address the uncertainties of renewable energy and loads in transient stability assessment with credible contingencies, this letter proposes a stacked denoising autoencoder (SDAE)-based probabilistic prediction method. The correlations among wind farms have been effectively considered through the variable transformation via the Cholesky decomposition. SDAE allows learning the mapping relationship between operational features and the transient stability margin. The possible operation scenarios are sampled under different confidence levels to generate appropriate inputs for SDAE to assess the probabilistic transient stability distribution. Results on the modified IEEE 39-bus system show that our proposed method can achieve a similar level of accuracy as the benchmark and improved Monte Carlo simulations-based methods while having much higher computational efficiency.
AbstractList	To address the uncertainties of renewable energy and loads in transient stability assessment with credible contingencies, this letter proposes a stacked denoising autoencoder (SDAE)-based probabilistic prediction method. The correlations among wind farms have been effectively considered through the variable transformation via the Cholesky decomposition. SDAE allows learning the mapping relationship between operational features and the transient stability margin. The possible operation scenarios are sampled under different confidence levels to generate appropriate inputs for SDAE to assess the probabilistic transient stability distribution. Results on the modified IEEE 39-bus system show that our proposed method can achieve a similar level of accuracy as the benchmark and improved Monte Carlo simulations-based methods while having much higher computational efficiency.
Author	Su, Tong Liu, Youbo Liu, Junyong Zhao, Junbo
Author_xml	– sequence: 1 givenname: Tong orcidid: 0000-0002-5424-6757 surname: Su fullname: Su, Tong email: sutongscu@gmail.com organization: School of Electrical Engineering and Information, Sichuan University, Chengdu, China – sequence: 2 givenname: Youbo orcidid: 0000-0002-5465-5243 surname: Liu fullname: Liu, Youbo email: liuyoubo@scu.edu.cn organization: School of Electrical Engineering and Information, Sichuan University, Chengdu, China – sequence: 3 givenname: Junbo orcidid: 0000-0002-8498-9666 surname: Zhao fullname: Zhao, Junbo email: junbo@ece.msstate.edu organization: Electrical and Computer Engineering, Mississippi State University, Mississippi State, USA – sequence: 4 givenname: Junyong surname: Liu fullname: Liu, Junyong email: liujy@scu.edu.cn organization: School of Electrical Engineering and Information, Sichuan University, Chengdu, China
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SubjectTerms	Confidence intervals Monte Carlo simulation Noise reduction Power system stability Probabilistic logic probabilistic prediction Stability analysis Stability criteria stacked denoising autoencoder (SDAE) Statistical analysis Transient analysis Transient stability Wind farms Wind power Wind power generation wind uncertainty
Title	Probabilistic Stacked Denoising Autoencoder for Power System Transient Stability Prediction With Wind Farms
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