Stacked-denoising-auto-encoder-based reliability assessment method for power system operation. (March 2023)

• Record Type: Journal Article
• Authors: Zhang, Bingchen; Hou, Kai; Dong, Ziheng; Liu, Zeyu; Wang, Xinglin
• Is Part Of: Energy reports. Volume 9(2023)Supplement 1
• Page Start: 505
• Page End: 511
• Publication Date: 2023-03
• Subjects: Data-driven -- Power system reliability -- Stacked denoising autoencoders; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.10.437
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: With the continuous expansion of renewable energy in the power grid, a massive number of operating states have been added to the power system. The heavy computational task brings new challenges to the efficiency of reliability assessment. In this regard, a stacked-denoising-auto-encoders-based (SDAE) model is proposed to replace the time-consuming optimal power flow (OPF) algorithm in the reliability assessment of power system operation. The system state and the optimal load curtailment are used as training samples, and the corruption process of SDAE is improved to make it more suitable for power systems. Moreover, unsupervised pretraining and supervised fine-tuning are used to get the optimal coding parameters which can establish the nonlinear mapping relationship between the system states and the minimum load curtailment. Case studies are performed on the RTS-79 system considering renewable energy. Results indicate that the proposed method can greatly improve the efficiency of reliability assessment for power system operation.