Outlier detection for hydroelectric power plant operation data.
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- WATANABE Risa
- Aoyama Gakuin University
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- NISHIGAKI Takahiro
- Aoyama Gakuin University
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- ONODA Takashi
- Aoyama Gakuin University
Bibliographic Information
- Other Title
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- 水力発電所運転データにおける外れ値検知
- ~Comparison of characteristics of various outlier detection methods.~
- ~様々な外れ値検知手法の特性比較~
Abstract
<p>In recent years, electric power companies collects different types of sensor data and weather information to maintain the safety of hydroelectric power plants while the plants are in operation. Although the power plant operation data is mostly normal state data, there is little accumulation of abnormal state data, and it is not easy to observe data related to abnormal states. Therefore, we have to identify malfunction signs from among the collected sensor data. In this paper, we detected outliers from hydropower plant operation data using five outlier detection methods including one-class SVM and compared the characteristics of each outlier.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2020 (0), 4M3GS1301-4M3GS1301, 2020
The Japanese Society for Artificial Intelligence
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Keywords
Details 詳細情報について
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- CRID
- 1390566775143092480
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- NII Article ID
- 130007857360
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed