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Structural Engineering and Mechanics Volume 88, Number 6, December25 2023 , pages 569-581 DOI: https://doi.org/10.12989/sem.2023.88.6.569 |
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Multi-sensor data-based anomaly detection and diagnosis of a pumped storage hydropower plant |
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Sojin Shin, Cheolgyu Hyun, Seongpil Cho and Phill-Seung Lee
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| Abstract | ||
| This paper introduces a system to detect and diagnose anomalies in pumped storage hydropower plants. We collect data from various types of sensors, including those monitoring temperature, vibration, and power. The data are classified according to the operation modes (pump and turbine operation modes) and normalized to remove the influence of the external environment. To detect anomalies and diagnose their types, we adopt a multivariate normal distribution analysis by learning the distribution of the normal data. The feasibility of the proposed system is evaluated using actual monitoring data of a pumped storage hydropower plant. The proposed system can be used to implement condition monitoring systems for other plants through modifications. | ||
| Key Words | ||
| anomaly detection; anomaly diagnosis; multivariate analysis; prognostics and health management; pumped storage hydropower plants; pump-turbine | ||
| Address | ||
| Sojin Shin, Cheolgyu Hyun, Phill-Seung Lee: Department of Mechanical Engineering, Korean Advanced Institute for Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea Seongpil Cho: School of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang 10540, Republic of Korea | ||