A deep spatial-temporal data-driven approach considering microclimates for power system security assessment. (1st March 2019)

• Record Type: Journal Article
• Authors: Huang, Tian-en; Guo, Qinglai; Sun, Hongbin; Tan, Chin-Woo; Hu, Tianyu
• Is Part Of: Applied energy. Volume 237(2019)
• Page Start: 36
• Page End: 48
• Publication Date: 2019-03-01
• Subjects: Deep learning -- Microclimates -- Parallelism -- Power system static security -- Renewable energy -- Weak spot detection; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.01.013
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A model for detecting power system security weak spots is proposed. Microclimates are considered in the proposed model. A deep neural network is designed to extract deep features. Parallelism is applied to enhance the training efficiency. The proposed model can greatly improve the task accuracy. Abstract: With the integration of renewable energy and microclimate-sensitive loads, secure and economic power system operation is becoming an increasingly important and complex problem. Therefore, based on big data from power systems and meteorological systems, a deep spatial-temporal data-driven model is proposed to predict and detect power system security weak spots during a future period. First, microclimates are considered in the proposed model. Then, a deep neural network structure is designed to extract deep features layer by layer for security weak spot detection. Furthermore, model simplification and parallelism as well as data parallelism are applied. Finally, the proposed model is evaluated based on the Guangdong Power Grid in China. The simulation results demonstrate that (1) power system security weak spots have spatial-temporal and microclimate-sensitive characteristics; (2) the deep model considering microclimates can greatly improve the task accuracy of online applications; and (3) simplification and parallelism can significantly enhance the training efficiency.