## 1 Introduction
A strategy of PCPs needs to minimize the length of CCF sequences so as to block the propagation of CCF in an ADN by exerting the control actions with low cost. The selected PCP needs meet some prevention goals optimally, such as preventing voltage collapses, transient instabilities, etc. Theoretical analysis methods [3; 4; 5; 6; 7; 8] and physical experimental methods [9; 10; 11] are two typical solutions for a PCP construction. The theoretical analysis method needs to obtain the approximated models of controlled plants, and the experimental method needs to build physical prototypes and owns a heavy cost. So, traditional methods for constructing PCPs always meet some challenges, such as high costs, long durations, and inaccuracies. A data-driven approach can make full use of a lot of available historical data to overcome the shortcomings of the existing methods, generating and optimizing a PCP through AI technologies with lower cost. According to the goals of preventive controls against failures, there are many AI technologies that could be adopted for constructing PCPs. [PERSON] et al. propose a dynamic security assessment method and obtain a PCP based on a decision tree idea to ensure the dynamic security [12]. In order to improve the voltage stability margin of power systems, [PERSON] et al. propose a preventive control method for rescheduling power generation based on a neural sensitivity model. The time series data samples from time domain simulations and the dynamic behavior information of the system are used, and the sensitivity is used to select the most effective set of generators to improve the security of the power system [13]. [PERSON] et al. use population optimization techniques to construct a PCP. They reduce the search space of each algorithm according to the size of an objective function and make multiple optimization algorithms run continuously, so as to improve the transient security of a power CPS [14]. [PERSON] et al. use a wide area measurement system (WAMS) and phasor measurement devices to conduct real-time transient stability assessment. Specifically, the support vector machine (SVM) based on least squares is used to identify the steady state of the power systems in real time, and then, the appropriate dispatching generators are selected for preventive control to ensure transient stability [15]. [PERSON] et al. use the algorithm of deep deterministic policy gradient with a safety exploration layer for preventive control to ensure that the node voltages of active distribution network are within the limit [16]. However, these studies focus on the construction of PCPs for only a single failure in CPS, so these constructed PCPs cannot prevent the occurrence of cascading failures. In order to prevent the occurrence of cascading failures, researchers construct many PCPs. [PERSON] et al. use a distributed adaptive multi-agent algorithm to get a PCP. The PCP could block the propagation paths of cascading failures by dispatching the power of generators based on N-1 criterion [17]. [PERSON] et al. use neural network techniques to construct a PCP to prevent the propagation of cascading failures [18], and they also use the reinforcement learning method based on \(Q\) learning to obtain a PCP to prevent cascading failures [19]. [PERSON] and [PERSON] use a strategy of specific thresholds to evaluate the lines with high vulnerabilities and then obtain a PCP based on a genetic algorithm. The PCP eliminates the overload of high vulnerability lines through load shedding, so as to prevent the spread of cascading failures in a CPS [20]. [PERSON] et al. develop a distributed management system using adaptive critical design based on adaptive dynamic programming. The system can flexibly take preventive actions and corrective actions to deal with the thermal overload of lines in active distribution network [21]. The existing research work about data-driven methods only focuses on a single PN and is not studied from the perspective of the interdependence between PN and CN.