학술논문

Substation Operation Sequence Inference Model Based on Deep Reinforcement Learning
Document Type
article
Source
Applied Sciences, Vol 13, Iss 13, p 7360 (2023)
Subject
substation
sequence of operations
deep reinforcement learning
Neo4j diagram knowledge base
inference automation
Technology
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
Language
English
ISSN
13137360
2076-3417
Abstract
At present, substation operation ticket system is developed based on an expert system, which has some problems such as knowledge base redundancy, intelligence deficiency and automatic learning ability. To solve this problem, this paper proposes an operation sequence reasoning model based on the knowledge base of the Neo4j knowledge graph and DuelingDQN (Dueling Deep Q Network) algorithm. Firstly, the diagram structure model of substation main wiring was established using the Neo4j knowledge graph. Based on the diagram structure model, the operable equipment set of the operation task was searched to form the task space, action space and action selection model of DuelingDQN. The reward and punishment function was designed based on the “five defense” rules and the state change of equipment. Make DuelingDQN model and Neo4j model interact in real time, and automatically learn the operation sequence. The test results show that the method proposed in this paper can automatically deduce the correct operation steps under different wiring modes and realize the transfer within the station, which is of great significance to the intellectualization of the operation ticket system.