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ニューラルネットワークに基づくHTTR850$$^{circ}$$C 30日間運転時の運転監視

HTTR operation monitoring with neural network in 30 days operation at 850$$^{circ}$$C

清水 厚志; 鍋島 邦彦; 中川 繁昭

Shimizu, Atsushi; Nabeshima, Kunihiko; Nakagawa, Shigeaki

高温工学試験研究炉(HTTR)は、初めて30日間の定格運転(原子炉出口冷却材温度850$$^{circ}$$C)を平成19年3月27日から4月26日にかけて実施した。本運転において、ニューラルネットワークを用いた運転監視モデルによりHTTRの監視を行い、定格出力時における状態量の微少な変動の検知性能について検証した。運転監視に用いたニューラルネットワークは、3層構造の階層型で31入力31出力、隠れ層が20ユニットから形成されるオートアソシアティブネットワークで、学習則には誤差逆伝播法を用いた。運転監視モデルについては、原子炉出力30%$$sim$$定格運転間の出力上昇中のデータをランダムに学習させて初期学習モデルを構築し、定格運転時の原子炉の燃焼等に伴う状態量の変化に合わせて、初期学習モデルの内部構造を変えていく適応学習を行いながら運転監視を行った。その結果、制御系動作による微少な状態量の変動等を検知し、原子炉施設の早期異常診断に適用できる見通しを得た。

The High temperature engineering test reactor (HTTR) executed the rated power driving for 30 days of the first time (850$$^{circ}$$C in temperature of the nuclear reactor exit coolant) until March, 27th through April, 26th, 2007. In this operation, HTTR was observed according to the operation monitoring model with the neural network, and the detection performance of neural network was verified during slight changes of reactor state at rated power. The neural network used for the operation monitoring was an auto-associative network, where 31 input 31 outputs and the hidden layers were connected with 20 units by the hierarchy of three layer structure. Back-propagation algorithm is used for study rule. The operation monitoring model in initial study was constructed by using the power up data between 30% and rated power, which are randomly studied. The adjustment study during the operation monitoring changes the internal structure of the initial study model to follow the changes of reactor status, such as the combustion of the nuclear fuel for the rated power driving. As a monitoring result, slight changes of reactor state by the control system operation were correctly detected, and the on-line application to an early anomaly diagnosis for HTTR facilities will be expected.

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