Enhanced feature combinational optimization for multivariate time series based dynamic early warning in power systems

Multivariate time series (MTS) learning-based dynamic early warning (DEW) shows promising potential in preventing power systems from underlying risks. However, as a fundamental and essential step for constructing DEW models, performing efficient feature selection (FS) within high-dimensional candidate features (HDCF) of power grids is challenging, especially considering the MTS format and security-dominated characteristics of electric utilities. This paper proposes an enhanced feature combinational optimization (FCO) method to select beneficial variables for MTS-based DEW models within the HDCF of power grids. First, to identify features that contain valuable temporal patterns for DEW, a wrapper FCO framework combined MTS learning and evolutionary computation is designed. Then, to improve the FCO efficiency and solution quality when facing HDCF, a maximal information coefficient (MIC) based enhancement scheme is developed, semi-guiding the FCO initialization and evolution. Finally, by integrating MIC and iterative search, hybrid end-to-end FS is achieved. Experiments on the synthetic data from the IEEE 39-bus system and the real-world data acquired from a provincial power grid in China show that, via the MIC semi-guidance, the proposed method possesses higher FCO efficiency and better results within HDCF compared to wrapper benchmarks. Compared to filter benchmarks, it is more compatible with supervised MTS and can consider the practical performance of features by interacting with the embedded learning models. Results also verify that, with the features from the proposed method, more accurate DEW models can be obtained to predict the target event in early stages.