Work

Knowledge Graph-Augmented ERNIE-CNN Method for Risk Assessment in Secondary Power System Operations

Year: 2025

Type: article

Abstract: With the increasing complexity of modern power systems, traditional risk assessment methods relying on expert experience and historical data face challenges in accuracy and adaptability. This study proposes a knowledge graph-augmented ERNIE-CNN method to enhance risk assessment in secondary power system operations. First, we construct a domain-specific knowledge graph by integrating expert knowledge and operational standards, which enhances semantic understanding and logical reasoning capabilities. Second, an improved ERNIE-CNN model is developed, incorporating an attention mechanism to effectively fuse semantic features and spatial patterns from operational texts. The experimental results on a dataset of 3240 secondary operation records demonstrate the model’s superior performance, achieving precision, recall, and F1-scores of 0.878, 0.861, and 0.869, respectively, outperforming benchmarks like BERT. Furthermore, a visualization of the knowledge graph is implemented, providing interpretable decision support for risk management. The proposed method offers a robust framework for automating risk assessment in power systems, with potential applications in smart grid maintenance and safety-critical operational planning.

Source: Energies

DOI: 10.3390/en18082104

Authors: Xiang Huang, Ping Li, Ye Wang, Xuchao Ren, Zhenbing Zhao, Gang Li

Institutions: State Grid Jiangsu Electric Power (China), North China Electric Power University

Language: English

Topic: Evaluation and Optimization Models

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137