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Data-driven power system reliability evaluation based on stacked denoising auto-encoders
- Source :
- Energy Reports, Vol 8, Iss, Pp 920-927 (2022)
- Publication Year :
- 2022
- Publisher :
- Elsevier, 2022.
-
Abstract
- The increasing penetration of renewable energy resources in power systems inadvertently leads to a surge in the number of random states. The calculation of optimal load shedding for all the proliferated states is rather overwhelming. It has been considered a bottleneck in the reliability evaluation process. To address this issue, a deep-learning-based approach is proposed to evaluate system reliability more efficiently considering the fluctuation of generation and loads. In the proposed power flow model, a stacked denoising auto-encoder (SDAE) serves as a multilayer neural network for the deep-learning process. Its stacked structure and encoding-decoding recursion enable it to extract high-order features even from non-linear equations. The features of contingency states are crucial to directly acquire the minimum load curtailment without the time-consuming optimal power flow (OPF). The after-trained model is applied to states simulated with the Monte-Carlo method in the RTS-79 system. The numerical result reveals the great advantage of the SDAE-based method in computation time while ensuring high accuracy. As an alternative way to classic OPF algorithms, it can be integrated with the impact-increment method and other state selection approaches.
- Subjects :
- Artificial neural network
Computer science
Computation
Process (computing)
Recursion (computer science)
Power system reliability
Data-driven
Bottleneck
TK1-9971
Electric power system
General Energy
Control theory
Stacked denoising auto-encoder
Electrical engineering. Electronics. Nuclear engineering
Reliability (statistics)
Optimal power flow
Subjects
Details
- Language :
- English
- ISSN :
- 23524847
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- Energy Reports
- Accession number :
- edsair.doi.dedup.....b433284548cf90036f616ae5ee56777c