Temporal Transfer Learning Framework for Power System Transient Stability Assessment in Internet of Energy

Transfer learning (TL) is a crucial technique to improve the cross-system analysis ability of data-driven transient stability assessment (TSA) approaches. However, existing transfer learning methods for TSA in new scenarios generally depend on a larger number of target samples, and the computational performance is limited by the inconsistency of temporal features in the transfer process. To resolve this issue, this paper proposes a temporal transfer learning framework (TTLF) for TSA, which can effectively improve the performance of cross-system TSA with zero target samples. The TTLF is sequentially composed of two modules. The first module is referred to as source sample characterization (SSC), which aims to better characterize the distribution information of transient time-series curves. The second module is termed temporal distribution matching (TDM), which aims to maximize the interclass dispersion and minimize the intra-class scatter in each hidden layer at each time. What’s more, the framework can also be extended to the situation with a few cross-system samples, which can significantly improve the assessment performance. The simulation results demonstrate the efficiency and scalability of the proposed framework in several utility benchmark systems with different data-driven models.