## 1 Introduction  
As one of the most complex artificial systems, the power system is operated to maintain the security and stability of the power generation, transmission, and distribution process [1]. However, the power system would inevitably suffer from various natural or human-made disturbances and faults, which may cause a loss of system synchronism and even large-scale blackout. Rapid dynamic security assessment (DSA) and accurate security control (SC) are necessary to protect the power system from dynamic insecurity. Traditional approaches of DSA include time-domain simulation (TDS) methods [2; 3; 4] and direct methods [5]. TDS methods rely on iteratively solving high-dimensional differential-algebraic equations. Such a high computational burden makes it unsuitable for real-time applications. Parallel computation techniques [6; 7] and stopping strategies [8] are applied to TDS to reduce its time consumption. Based on the Lyapunov stability theory, direct methods are fast but challenging to construct the energy function for large-scale power systems. In comparison, data-driven machine learning methods provide better performance with faster assessment and more robust generalization, which are considered promising approaches for the real-time DSA [9]. Some machine learning models have been successfully applied to DSA, such as decision trees [10], support vector machine [11], artificial neural network [12], convolutional neural network [13; 14; 15], stacked auto-encoder [16], generative adversarial network [17], and deep belief network [18]. In general, deep learning (DL) networks can dig out the potential physical information of power systems more profoundly and provide higher prediction accuracy [19]. However,DL is a black box with a certain opacity, which often causes suspicion from system operators and makes such a technique not fully trusted by the industry [20]. In this context, opening the black box by explaining the DL models and their predictions is an indispensable part of DL research, and thus it has become an increasingly hot topic in the industry [21; 22; 23; 24]. Feature importance analysis (FIA), as one of the interpretation approaches for machine learning models, is an available tool for critical feature detection. The traditional FIA is based on the impurity in random forests (RFs) [25]. The impurity is referred to the reduction in a specific criterion brought by a particular feature and is regarded as the feature importance. In addition, [PERSON], [PERSON] et al. proposed a visual feature importance (ViFI) method to visualize the importance of input features of deep neural network by observing the change of weights when training [26]. The above approaches presented a comprehensive influence of features on the model prediction for all samples, called model-level FIA (M-FIA). However, in DSA and SC of power systems, decision makers usually pay more attention to those real-time insecure operating conditions (OCs). Therefore, it is necessary to develop an instance-level FIA (I-FIA) approach to detect the critical units for specific OCs and further to control the power system security. Another exciting issue discussed in this paper is the SC. Traditionally, the SC problem is mathematically modeled as the solution of security constraint optimal power flow (SCOPF) and is tackled with mathematical programming [27; 28; 29] or swarm intelligence optimization [30; 31; 32; 33]. Such methods are treated as global approaches. However, due to the high time complexity of TDS, the excessively long processing times and the large memory space requirement are significant problems in the global approaches. Therefore, it is necessary to develop a faster SC strategy using sequential processing. The main contributions of this paper are as follows. * An efficient DL framework integrated with DSA, critical unit detection, and security control is proposed, achieving rapid security assessment and recovery.