Diagnosis and Prediction for Loss of Coolant Accidents in Nuclear Power Plants Using Deep Learning Methods

She, Jingke; Shi, Tianzi; Xue, Shi-Yu; Zhu, Yada; Lu, Shaofei; Sun, Peiwei; Cao, Huasong

doi:10.3389/fenrg.2021.665262

Cited by 32 publications

(12 citation statements)

References 22 publications

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“…Convolution neural network is a feedforward neural network with convolution calculation and depth structure, which is one of the representative algorithms of depth learning (Li et al, 2021b;She et al, 2021). Convolutional neural network has the ability of representation learning.…”

Section: Overall Design Of Modelmentioning

confidence: 99%

Security feedback trust model of power network demand response terminal triggered by hacker attacks

Chen

Zhao²,

Sun³

et al. 2023

Front. Energy Res.

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With the rapid expansion of power system scale, demand response business is promoted to develop. More and more demand response terminals are connected to the smart grid, smart grid is an intelligent system that allows the grid to effectively perform its functions. Its data can be used in intelligent decision-making during grid operation, which may be attacked by hackers in practical applications, causing security problems of demand response terminals of the power network. The security feedback trust model establishes trust relationship through trust mechanism, which can effectively ensure the security of interaction between nodes and demand response terminals of the smart grid. Therefore, a security feedback trust model of power network demand response terminal triggered by hacker attacks is proposed. Analyze the role of smart grid in power grid, and use convolutional neural network in artificial intelligence technology to enhance the flexibility of smart grid. Aiming at the security problem of the demand response terminal of the power network being attacked by hackers, based on the trust theory, the security feedback trust model of the demand response terminal of the power network is designed through the main services provided by the security feedback trust model, the trust information storage of the power network nodes and the summary of the main work. Establish the identity trust relationship, adopt the distributed verifiable signature scheme, update the power grid node certificate, update the identity trust relationship, and revoke the identity trust relationship based on the trust evaluation and threshold value to prevent hackers from attacking the power grid demand response terminal. Based on information theory, trust is established and measured. Entropy is used to represent the trust value. Behavior trust evaluation and composition mechanism are introduced into the security feedback trust model of power network demand response terminals to achieve the credibility of identity and behavior among power network nodes. The experimental results show that the proposed method can judge the hacker attacks, reduce the impact of hacker attacks on the trust of power grid nodes, and improve the interaction security between power grid demand response terminals and power grid nodes.

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Section: Overall Design Of Modelmentioning

confidence: 99%

Security feedback trust model of power network demand response terminal triggered by hacker attacks

Chen

Zhao²,

Sun³

et al. 2023

Front. Energy Res.

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“…The best performing parameter configuration is determined via both historic experience and repeated tests. The successful experience of applying deep learning models in previous studies (She et al, 2019;She et al, 2021;Gong et al, 2022) provide a basic configuration of the model parameters. The newly conducted tests using current datasets then help improve the basic configuration to be more suitable for the proposed framework.…”

Section: Core Inlet Temperature Prediction With Lstmmentioning

confidence: 99%

“…To tackle the challenge of uncertainty, a combined structure of CNN and LSTM is employed to build a prediction model (She et al, 2021). CNN utilizes its convolutional structure of weight sharing and pooling operations to effectively extract the data features of operation conditions.…”

Section: Core Outlet Temperature Prediction With Cnn + Lstmmentioning

confidence: 99%

A deep learning-based framework for the operation prediction of primary heat transfer loop in nuclear power plants

Shi

She²,

Li³

et al. 2023

Front. Energy Res.

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A deep learning-based multi-node framework is constructed in this work to provide a data-driven platform that provides predictions for the operation condition of the primary heat transfer (PHT) loop in nuclear power plants (NPPs). Several deep learning models that have been verified and demonstrated in previous researches, such as Long-Short Term Memory (LSTM), Convolutional Neural Network (CNN), and zigmoid-based LSTM (zLSTM), are applied to modeling critical system parameters at three important nodes in the PHT loop. The feature extraction and process memory are enhanced via the collaborative work of CNN and LSTM. zLSTM, on the other hand, is successfully utilized to strengthen the long-term memory, especially for predictions of a node with multivariate inputs such as the steam generator. The node prediction results are also adopted for a polynomial fitting that generates an additional input to the next node, allowing each node to select a more accurate input. According to the verification experiments based on Loss of Coolant Accident (LOCA), the Mean Squared Error (MSE) result (1.29 × 10−3) and the Mean Absolute Error (MAE) result (1.37 × 10−2) of 0.7 cm2 LOCA case demonstrate the functionality and accuracy of the proposed framework. It is found that the fitting error (MSE) in the outlet node at 0.7 cm2 case is 38.5% lower than the prediction, showing the advantage of applying both deep learning and fitting methods. The best performance, in term of MSE, is obtained at SG node in the 0.7 cm2 case, where its processing error (0.001285) is 93.2% lower than that of the baseline models. Both the validation and verification experiments successfully proved the feasibility and advantages of the proposed framework, which offers an alternative option for the operation analysis of PHT performance.

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“…Over the past few years, a large number of methods were explored to predict the failure, such as the grey model [24,25], the BP neural network [26,27], the RBF neural network [28,29], the data-driven model [30][31][32][33], deep learning [34], and the grey relational analysis method [35]. Although the grey models in [24,25] were effective to a certain extent, they only considered the development of a single or several characteristic parameters independently.…”

Section: Introductionmentioning

confidence: 99%

Grey Relational Analysis-Based Fault Prediction for Watercraft Equipment

Feng

Chen²,

Guan³

et al. 2022

Front. Phys.

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In this study, we aim to investigate the fault prediction for watercraft equipment by using grey relational analysis. At first, the healthy degree model of watercraft equipment is proposed, and then two main theorems are derived to determine the health condition criteria for equipment. Lastly, the relevant simulation results are provided to verify the validity and accuracy of the healthy degree model. Current results can be helpful to effectively design the supporting mode of watercraft equipment and realize the transformation of watercraft equipment support from planned maintenance to predictive maintenance.

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Diagnosis and Prediction for Loss of Coolant Accidents in Nuclear Power Plants Using Deep Learning Methods

Cited by 32 publications

References 22 publications

Security feedback trust model of power network demand response terminal triggered by hacker attacks

Security feedback trust model of power network demand response terminal triggered by hacker attacks

A deep learning-based framework for the operation prediction of primary heat transfer loop in nuclear power plants

Grey Relational Analysis-Based Fault Prediction for Watercraft Equipment

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