Abnormal power detection model based on digital twin

Qionglan Na; Dan Su; Huimin He; Xin Li; Na Xiao; Yixi Yang

doi:10.1117/12.2660574

3 February 2023 Abnormal power detection model based on digital twin

Qionglan Na, Dan Su, Huimin He, Xin Li, Na Xiao, Yixi Yang

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125112I (2023) https://doi.org/10.1117/12.2660574
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

With the proposal of energy-saving economy, smart grid is developing in the direction of green and environmental protection, and the abnormal power consumption behavior of users causes serious loss of power resources. Traditional power consumption anomaly detection methods have problems of low accuracy and slow operation efficiency. We have built a digital twin for fast and high-precision abnormal power consumption detection. The virtual model includes an LSTM model to achieve effective extraction and detection of abnormal power consumption characteristics. We update the historical database at the same time through multi-dimensional sensors (such as electricity meters) and various twin data of the surrounding environment. Then, based on the collected twin data, anomaly prediction is made. The proposed digital twin model achieves synchronization and real-time updates with the physical entities of the power system, resulting in more accurate detection results than traditional prediction methods. The results show that, compared with traditional detection methods, this method can detect abnormal users quickly and effectively, with a detection accuracy of 98.4%.

Citation Download Citation

Qionglan Na, Dan Su, Huimin He, Xin Li, Na Xiao, and Yixi Yang "Abnormal power detection model based on digital twin", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125112I (3 February 2023); https://doi.org/10.1117/12.2660574

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KEYWORDS

Data modeling

Sensors

Computer simulations

Instrument modeling

Environmental sensing

Space mirrors

Systems modeling

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