Title: Power grid fault diagnosis technology based on improved deep Q-network model

Authors: Qiang Wang; Hongyan Song

Addresses: State Grid Shanxi Electric Power Company, Taiyuan, Shanxi, China ' State Grid Shanxi Electric Power Company, Taiyuan, Shanxi, China

Abstract: Owing to the increasing complexity of the power system, the difficulty of fault diagnosis in the power grid is also increasing. In response to the issue of continuously decreasing fault diagnosis accuracy, a power grid fault diagnosis model based on an improved deep Q-network model is raised. This model enhances its information integration capability by constructing a fault parameter recognition model and introducing alarm information for text processing. By identifying power grid fault parameters and processing alarm information, the efficiency and accuracy of fault diagnosis can be improved. The experimental results show that the model shows significant performance improvement in multiple state dimensions, and is significantly better than the traditional algorithm in single fault diagnosis and multi-fault diagnosis scenarios. The results show that the proposed method has significant advantages in the accuracy of fault prediction, processing efficiency and anti-noise ability, which verifies the validity and practicability of applying this model in complex power systems. It also emphasises the importance of combining reinforcement learning with unstructured data to further promote the development of smart grid technology.

Keywords: DQN; alarm information; power grid; reinforcement learning; fault diagnosis.

DOI: 10.1504/IJRS.2026.150488

International Journal of Reliability and Safety, 2026 Vol.20 No.1, pp.1 - 18

Received: 27 Sep 2024
Accepted: 22 May 2025
Published online: 15 Dec 2025 *

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