A Plant Status Prediction Method by Neural Network Using Simplified Process Data Components Based on Frequencies
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- Shiromaru Isao
- The Chugoku Electric Power Co., Inc.
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- Tominaga Kenji
- Hitachi, Ltd.
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- Seki Hiroshi
- Hitachi, Ltd.
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- Ooga Yukiharu
- Hitachi, Ltd.
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- Maruyama Takekazu
- Hitachi, Ltd.
Bibliographic Information
- Other Title
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- 単純化した時系列データを用いるニューラルネットワーク応用状態予測方式
- タンジュンカ シタ ジケイレツ データ オ モチイル ニューラルネットワーク
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Abstract
A prediction method of plant status using neural network has been proposed for operation support systems in plants. The change of time series data from plant is complex because the data contain many frequency components. It is difficult to improve prediction performance of neural network by using the time series data directly. The proposed method resolves the data into some components based on their change frequencies and a prediction model using a neural network is generated for each components individually.<br>To evaluate the proposed method, two kinds of time series data are trained and predicted. One is the data containing several periodic components and a non-periodic component generated by an equation. The other is the simulation data of a fossile power plant under an abnormal condition. As a result, time averaged prediction error was reduced to 1/4-1/3 compared with a method using time series data directly. Thus, the proposed method is effective to predict plant status precisely.
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 118 (7-8), 1148-1154, 1998
The Institute of Electrical Engineers of Japan
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Keywords
Details 詳細情報について
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- CRID
- 1390001204608340608
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- NII Article ID
- 130006844094
- 10002814645
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- NII Book ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL BIB ID
- 4496735
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed