Year: 2026
Type: article
Abstract: In smart substations, long-term operation and environmental disturbances accelerate equipment aging, often leading to abnormal operating states and frequent alarm events. These alarms provide important early indications of potential equipment faults. Situational awareness technologies offer effective means for real-time monitoring and early warning in substations. Meanwhile, Structural Causal Models (SCMs) can uncover underlying causal relationships in operational data, improving prediction stability and interpretability compared with conventional correlation-based methods. This study proposes a novel situational awareness framework for smart substations that integrates deep learning-based causal inference with expert domain knowledge. By guiding the model with the causal diagram derived from substation alarm data as a strong prior, our method learns causal relationships that are statistically significant. Compared with traditional correlation-based statistical approaches, causal inference enables the explicit modeling and adjustment of potential confounding effects under given assumptions, leading to more reliable relationship estimation and a more interpretable model structure. Finally, a case study using real substation data shows improved predictive performance of the proposed method relative to conventional correlation analysis.
Source: Energies
DOI: 10.3390/en19102296
Authors: Xiang Lu, Youwei Chen, Yijia Fu, Fang Ren, Zhonggui Ma
Language: English
Topic: Smart Grid and Power Systems
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G373896013, G437743288
OpenAlex ID: W7160846029