Anomaly Detection and Classification Methods for Power Grid Data Middle Platform Based on Reinforcement Learning

doi:10.3969/j.issn.1008-0198.2026.03.016

Abstract

Abstract: To improve the efficiency of power grid data platform operation and maintenance, this paper proposes a reinforcement learning model that integrates proximal policy optimization and multi-head self-attention mechanism. This model aims to perform real-time identification and classification of anomalies in the power grid data platform, assisting on-site maintenance personnel in quickly locating the causes of anomalies and carrying out targeted anomaly handling. First, a gated recurrent unit network is used to model time-series dependencies. Second, a multi-head self-attention layer is introduced to accurately capture local saliency features. Then, considering the characteristics of anomaly identification scenarios in power grid data platforms, a hybrid activation function is designed to ensure good performance in both identifying known anomalies and autonomously discovering new anomaly types. Finally, the model's effectiveness is verified using actual operational data from a provincial power grid company's data middle platform. Experimental results show that the overall anomaly classification accuracy reaches 98.2%, and the F1 score for identifying unknown new anomaly types reaches 91.5%, significantly outperforming other comparative models.

Key words: power grid data middle platform, anomaly detection, reinforcement learning, proximal policy optimization, attention mechanism

CLC Number:

TM727
TP18

JIANG Guang, XUE Jingyuan, HU Xiang, CAO Jie, FANG Bin. Anomaly Detection and Classification Methods for Power Grid Data Middle Platform Based on Reinforcement Learning[J]. Hunan Electric Power, 2026, 46(3): 121-129.

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