# Substation Operation Sequence Inference Model Based on Deep Reinforcement Learning
## 1 Introduction  
Electric power enterprises are undergoing digital transformation, and artificial intelligence technology is playing an increasingly important role in distribution network scheduling, operation and maintenance. The switching operation of the substation is an important work in the dispatching operation of the distribution network. When the power network fails or is repaired, the load transfer and operation mode adjustment should be realized through the switching operation. Any maloperation may cause serious security accidents. Drawing up the operation ticket in advance and determining the operation steps can improve the correct rate of switching operation, thus avoiding maloperation. However, with the continuous expansion of the power system, the number of power equipment pieces involved has soared, the workload of employees to fill in the operation ticket, the quality requirements of employees grow increasingly larger, and the mistakes of ticketing personnel are inevitable. Therefore, it is necessary to develop an intelligent operation ticket generation system which can adapt to various electrical main wiring and improve the efficiency and accuracy of operation ticket generation. An intelligent operating ticket system can automatically form operation steps. The current operating ticket system is implemented based on an expert system [1; 2; 3; 4; 5]. This type of method needs to build a knowledge base with a large number of operation rules in advance, and then deduce the knowledge base to obtain the operation steps. However, the knowledge base is highly dependent on the experience of the operator. Because the operation rules are highly related to the main wiring mode, equipment composition, initial state of the equipment, and whether it needs to be transferred, there will be differentoperation rules under different conditions even if the wiring structure is the same. Therefore, the knowledge base has insufficient completeness, high redundancy and poor flexibility, and manual intervention must be carried out in the formation of operation steps. Scholars have carried out a significant amount of research on simplifying rules and designing templates, and have made some achievements, but cannot solve the fundamental problem. Rough set, rule tree and other techniques simplify the operation rules, reduce the redundancy of rules and reduce the time required for inference [6,7]. [PERSON], [PERSON] et al. establishes typical interval template and specific main wiring template, solidifies its operation rules, and forms operation steps through rule matching [8,9]. These studies focus on specific main connections and topologies, and the rules need to be refined as new connections emerge. Deep learning is widely used in power system because of its excellent ability in data analysis [10,11]; in order to shorten the reasoning process of operation tickets, some scholars use deep learning to guide the generation of operation tickets [12,13]. By learning the relationship between the environment and the operation sequence before and after the substation operation, a model that can match similar historical tickets according to the operation task and the initial state of the substation is obtained, and the generation of operation tickets is guided. Although the method has a very good accuracy, its essence does not break away from the artificial, thus not being able to achieve the realization of real intelligence. At the same time, the training of the model relies on a large number of historical operation tickets, which is still not friendly to the new plant station and the expansion part. Therefore, it is necessary to construct a universal and intelligent inference model of operation sequence. Reverse brake operation can be regarded as a sequential decision-making process in uncertain scenarios, and its results are uncertain. Deep reinforcement learning has excellent performance in sequential decision-making problems due to its strong decision-making ability [14].