Work

Anomaly detection method for edge management devices in energy storage systems

Year: 2025

Type: article

Abstract: Accurate identification of anomalies in battery clusters is essential to ensuring the safe and stable operation of energy storage stations. In response to the limitations of the cloud-edge-end architecture—namely, excessive data transmission to the cloud, heavy computational load on cloud platforms, and insufficient real-time control—this study proposes an edge-side analytical approach. Voltage data from battery packs are processed at the edge using a boundary point filtering technique based on the Local Outlier Factor (LOF), in combination with Singular Value Decomposition (SVD) and a dynamic grid partitioning scheme. This method significantly improves the computational efficiency of LOF while preserving anomaly detection accuracy. Experimental evaluations conducted using six months of real-world energy storage station operation data demonstrate that the proposed SVD-based dynamic grid LOF boundary filtering method yields superior detection performance on time-series data compared to conventional LOF clustering.

Source: Journal of Energy Storage

DOI: 10.1016/j.est.2025.118811

Authors: Shuo Dai, Liyong Niu, Binghui Liu, Caixiang Liu

Institution: Beijing Jiaotong University

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

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

Open Access status: hybrid

Estimated APC paid: $3,560