A Deep Learning Method Based on Bidirectional WaveNet for Voltage Sag State Estimation via Limited Monitors in Power System

Yaping Deng, Lu Wang, Hao Jia, Xiaohui Zhang and Xiangqian Tong
Additional contact information
Yaping Deng: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Lu Wang: School of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Hao Jia: School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Xiaohui Zhang: School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Xiangqian Tong: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China

Energies, 2022, vol. 15, issue 6, 1-17

Abstract: Voltage sag state estimation on the basis of a limited number of installed monitors is essential to dividing the responsibility for the voltage sag and taking corresponding measurements for improvement in voltage quality. Therefore, a deep learning methodology via bidirectional WaveNet for the voltage sag state estimation is proposed in this paper. The presented method can simultaneously estimate voltage sag state at non-monitored buses via limited monitors. Especially, the proposed deep learning method using the bidirectional WaveNet is designed to explore the long-term and long-range temporal dependencies in both the forward and backward directions. In this way, only by using original measured voltages through monitors, high accuracy for voltage sag state estimation can be achieved without restructured or redesign of the raw monitored data. An excellent advantage of the presented algorithm is that it can be implemented without system parameters or operating conditions or any other prior information. The presented methodology was verified by the IEEE 30-bus benchmark system. The experimental results illustrated that the accuracy of the voltage sag state estimation results was over 99.83%. Furthermore, a comparison among different models, including the bidirectional GRU-based model, one-way WaveNet-based model, and bidirectional WaveNet-based model, was also conducted. The results illustrated that the proposed bidirectional WaveNet-based model achieved the highest accuracy and quickest convergence speed.

Keywords: power quality; voltage sag; state estimation; deep learning; bidirectional WaveNet (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/6/2273/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/6/2273/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:6:p:2273-:d:775745

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().