Anomaly Perception Method of Substation Scene Based on High-Resolution Network and Difficult Sample Mining

Yunhai Song, Sen He, Liwei Wang, Zhenzhen Zhou, Yuhao He, Yaohui Xiao, Yi Zheng () and Yunfeng Yan
Additional contact information
Yunhai Song: Overhaul and Test Center of UHV Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China
Sen He: Overhaul and Test Center of UHV Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China
Liwei Wang: Overhaul and Test Center of UHV Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China
Zhenzhen Zhou: Overhaul and Test Center of UHV Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China
Yuhao He: Overhaul and Test Center of UHV Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China
Yaohui Xiao: Overhaul and Test Center of UHV Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China
Yi Zheng: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Yunfeng Yan: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Sustainability, 2023, vol. 15, issue 18, 1-13

Abstract: The perception of anomalies in power scenarios plays a crucial role in the safe operation and fault prediction of power systems. However, traditional anomaly detection methods face challenges in identifying difficult samples due to the complexity and uneven distribution of power scenarios. This paper proposes a power scene anomaly perception method based on high-resolution networks and difficult sample mining. Firstly, a high-resolution network is introduced as the backbone for feature extraction, enhancing the ability to express fine details in power scenarios and capturing information on small target anomaly regions. Secondly, a strategy for mining difficult samples is employed to focus on learning and handling challenging and hard-to-recognize anomaly samples, thereby improving the overall anomaly detection performance. Lastly, the method incorporates GIOU loss and a flexible non-maximum suppression strategy to better adapt to the varying sizes and dense characteristics of power anomaly targets. This improvement enables higher adaptability in detecting anomalies in power scenarios. Experimental results demonstrate significant improvements in power scene anomaly perception and superior performance in handling challenging samples. This study holds practical value for fault diagnosis and safe operation in power systems.

Keywords: high-resolution network; difficult sample mining; power scene anomaly perception; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/18/13721/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/18/13721/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:18:p:13721-:d:1239889

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().