Resilient power network structure for stable operation of energy systems: A transfer learning approach. (15th August 2021)

• Record Type: Journal Article
• Authors: Huang, Wanjun; Zhang, Xinran; Zheng, Weiye
• Is Part Of: Applied energy. Volume 296(2021)
• Publication Date: 2021-08-15
• Subjects: Transfer learning -- Sub-transmission system -- Power network structure -- Short-term voltage stability; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.117065
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: An improved index is derived to quantify the STVS of different network structures. A BiLSTM-based model is developed to identify resilient network structures. A transfer learning approach is proposed to adapt to new systems for expansion. Network structure is described by sequences to extract the structural features. Numerical tests show the high reliability and efficiency of the proposed approach. Abstract: With increasing dynamic loads, short-term voltage stability (STVS) problems are emerging in sub-transmission expansion planning (SEP), which threats the stable operation of energy systems. However, it is computationally intensive to evaluate all possible network structures in SEP, since STVS is traditionally analyzed for a fixed network structure at a certain operating condition using time-domain simulations. Taking advantage of big data analytics, a deep transfer learning approach based on bi-directional long short-term memory (BiLSTM) is proposed to identify resilient network structures with better STVS performance efficiently. First, an improved voltage recovery index (IVRI) is introduced to quantify the STVS of different network structures with a higher degree of distinguishment. Then, a BiLSTM-based STVS evaluation machine is devised to identify resilient network structures with better STVS performances with high efficiency, which predicts the STVS of various network structures without resorting to time-consuming time-domain simulations. Finally, the STVS evaluation machine is transferred to adapt to new systems with different numbers of buses in the context of SEP. Numerical tests on the IEEE benchmarks and the real Guangdong Power Grid have verified the effectiveness of the proposed approach. An illustrative application example indicates the potential of the proposed approach in tackling STVS-based SEP for the stable operation of energy systems.