21 Ergebnisse
Sortierung:
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 131, S. 103558
ISSN: 0149-1970
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 129, S. 103487
ISSN: 0149-1970
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 81, S. 203-216
ISSN: 0149-1970
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 155, S. 104514
ISSN: 0149-1970
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 157, S. 104571
ISSN: 0149-1970
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 146, S. 104172
ISSN: 0149-1970
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 113, S. 135-144
ISSN: 0149-1970
In: ETFS-D-22-00058
SSRN
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 169, S. 105086
ISSN: 0149-1970
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 166, S. 104962
ISSN: 0149-1970
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 160, S. 104679
ISSN: 0149-1970
In: Science and technology of nuclear installations, Band 2023, S. 1-14
ISSN: 1687-6083
A deep-learning model was proposed for predicting the remaining time to automatic scram during abnormal conditions of nuclear power plants (NPPs) based on long short-term memory (LSTM) and dropout. The proposed model was trained by simulated condition data of abnormal conditions; the input of the model was the deviation of the monitoring parameters from the normal operating state, and the output was the remaining time from the current moment to the upcoming reactor trip. The predicted remaining time to the reactor trip decreases with the development of abnormal conditions; thus, the output of the proposed model generates a predicted countdown to the reactor trip. The proposed prediction model showed better prediction performance than the Elman neural network model in the experiments but encountered an overfitting problem for testing data containing noise. Therefore, dropout was applied to further improve the generalization ability of the prediction model based on LSTM. The proposed automatic scram prediction model can provide NPP operators with an alert to the automatic scram during abnormal conditions.
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 157, S. 104595
ISSN: 0149-1970
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 117, S. 103046
ISSN: 0149-1970
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 115, S. 52-61
ISSN: 0149-1970