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Power system congestion event prognosis (CEP) based on multivariate time series (MTS) learning is an effective way to improve the warning abilities against risky situations. However, it is still challenging to decide which measurement sequences of system variables should be selected to train MTS learning-based CEPs models, especially when facing high-dimensional candidate features from power systems. Focusing on this issue, this paper proposes a novel high-dimensional feature selection (FS) method to identify and select variables that contain beneficial data patterns for the prognosis of network congestion events. A wrapper framework embedded with multiple MTS learning models is first built to treat FS as a combinatorial optimization task and receive MTS as inputs directly without sequence compressions. Then, to improve the combinatorial optimization efficiency and FS results in the high-dimensional case, a hybrid structure called enhanced evolutionary computation (EEC) is developed by combining with information theory-based feature priority scores, which play the role of semi-guidance in the iterative search. Test results on both the real-world and synthetic datasets validate the effectiveness of the developed EEC structure and show that the selected features by the proposed FS method are more beneficial in identifying the early patterns of network congestion events, thus contributing to efficient and accurate CEP for power systems.