A Study of an Anomaly Detection System for Small Hydropower Data considering Multivariate Time Series
Data anomaly detection in small hydropower stations is an important research area because it positively affects the reliability of optimal scheduling and subsequent analytical studies of small hydropower station clusters. Although many anomaly detection algorithms have been introduced in the data pr...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2024/8108861 |
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| Summary: | Data anomaly detection in small hydropower stations is an important research area because it positively affects the reliability of optimal scheduling and subsequent analytical studies of small hydropower station clusters. Although many anomaly detection algorithms have been introduced in the data preprocessing stage in various research areas, there is still little research on effective and highly reliable anomaly detection systems for practical applications in small hydropower stations. Therefore, this paper proposes a real-time data anomaly detection system for small hydropower clusters (RDADS-SHC) considering multiple time series. It addresses the difficulties of timely detection, alerting, and management of real-time data anomalies (errors, omissions, and so on) in existing small hydropower stations. It proposes a real-time data anomaly detection algorithm for small hydropower stations integrated with the Z-score and dynamic time warping, which can detect and process abnormal information more accurately and efficiently, thereby improving the stability and reliability of data sampling. The paper proposes a Keepalived-based hot-standby RDADS-SHC deployment model with m (m ≥ 2) units. It can automatically remove and restart faulty services and switch to their standbys, which significantly improve the reliability of the proposed system, ensuring the safe and stable operation of related functional services. This paper can detect anomalous data more accurately, and the system is more stable and reliable in a cluster detection environment. The actual operation has shown that compared with existing anomaly detection systems, the architecture and algorithms proposed in this paper can detect anomalous data more accurately, and the system is more stable and reliable in the small hydropower cluster detection environment. It solves abnormal data management in small hydropower stations and provides reliable support for subsequent analysis and decision-making. |
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| ISSN: | 2050-7038 |