Resilient power network structure for stable operation of energy systems: A transfer learning approach

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.