Neutron noise source reconstruction using the Adaptive Neuro-Fuzzy Inference System (ANFIS) in the VVER-1000 reactor core

Hosseini, Seyed Abolfazl; Afrakoti, Iman Esmaili Paeen

doi:10.1016/j.anucene.2017.02.015

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…ANFIS algorithm has been applied in many fields due to its ability of modeling complex conditions. ese applications include a study done by [23] where ANFIS was employed in optimizing energy systems; unfolding of the neutron noise source in a nuclear reactor core was accomplished by ANFIS algorithms [13], multiclass event classification using ANFIS approach in a prototype fast breeder reactor [24], estimation of the output of turbinegenerator by ANFIS approach [25], and during radiology image analysis, a combination of ANFIS and Granger Causality was utilized in the detection of both linear and nonlinear causal information [26].…”

Section: Methodsmentioning

confidence: 99%

“…Several machine learning algorithms such as SVR and cascaded fuzzy neural networks were created by [3] to diagnose severe accidents in an NPP while SVR schemes were employed in the diagnosis of incipient cracks at the steam generator [11]. LSTM algorithm was used in accident diagnostics [12] whereas works by [13] applied ANFIS models in neutron noise identification inside reactor cores.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques

Mwaura

Liu

2021

Science and Technology of Nuclear Installations

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Fault diagnosis occurrence and its precise prediction in nuclear power plants are extremely important in avoiding disastrous consequences. The inherent limitations of the current fault diagnosis methods make machine learning techniques and their hybrid methodologies possible solutions to remedy this challenge. This study sought to develop, examine, compare, and contrast three robust machine learning methodologies of adaptive neurofuzzy inference system, long short-term memory, and radial basis function network by modeling the loss of feed water event using RELAP5. The performance indices of residual plots, mean absolute percentage error, root mean squared error, and coefficient of determination were used to determine the most suitable algorithms for accurately diagnosing the loss of feed water transient signatures. The study found out that the adaptive neurofuzzy inference system model outperformed the other schemes when predicting the temperature of the steam generator tubes, the radial basis function network scheme was best suited in forecasting the mass flow rate at the core inlet, while the long short-term memory algorithm was best suited for the estimation of the severities of the loss of the feed water fault.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques

Mwaura

Liu

2021

Science and Technology of Nuclear Installations

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“…Further ML approaches were implemented by [7] in the form of Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of critical heat flux. For unfolding, ANFIS approaches have also been utilised for the localisation of simulated induced neutron noise sources in VVER-100 rectors, given neutron pulse height distributions as training input [8,9].…”

Section: Related Workmentioning

confidence: 99%

“…4. Sample of 12 learnt feature-maps from the output of first dense block for the input of vibrating fuel assembly at (8,16) given all possible detectors. Visually depicting how the differing layers highlight different features of the image.…”

Section: Long Short-term Memory Recurrent Neural Networkmentioning

confidence: 99%

3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection

Durrant

Leontidis

Kollias

2019

EPJ Nuclear Sci. Technol.

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With Europe's ageing fleet of nuclear reactors operating closer to their safety limits, the monitoring of such reactors through complex models has become of great interest to maintain a high level of availability and safety. Therefore, we propose an extended Deep Learning framework as part of the CORTEX Horizon 2020 EU project for the unfolding of reactor transfer functions from induced neutron noise sources. The unfolding allows for the identification and localisation of reactor core perturbation sources from neutron detector readings in Pressurised Water Reactors. A 3D Convolutional Neural Network (3D-CNN) and Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) have been presented, each to study the signals presented in frequency and time domain respectively. The proposed approach achieves state-of-the-art results with the classification of perturbation type in the frequency domain reaching 99.89% accuracy and localisation of the classified perturbation source being regressed to 0.2902 Mean Absolute Error (MAE).

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“…The structure of ANFIS consists of five layers, The function of each layer is presented as follows [10].…”

Section: Adaptive-neuro Fuzzy Inference System (Anfis) Structure and mentioning

confidence: 99%

Intelligent Algorithm based Maximum Power Point Tracking of Photovoltaic Solar Pump

2019

International Journal of Research Studies in Electrical and Ele

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Particle Swarm Optimization Maximum Power Point Tracking (PSOMPPT) controller are subjected to boost converter for optimal switching angle. Solar energy given by photovoltaic cells which is fabricated by different material like silicon and combinations of cells is known as solar panel. Boost converter to supply to constant input voltage to solar pump from photovoltaic module. PV modules have crest power dependent on temperature and irradiance by naturally occurring in atmosphere.PSO intelligent dependent algorithm to perceive a solution to a development problem in search space. PSOMPPT based controller shows good response with high power output for modeling to nonlinear system as PV modules. A comprehensive simulation of the advanced method has been simulated in Matrix laboratory. The simulation result exhibits that this design should be essentially perceive in practical applications like PMDC pump.

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Neutron noise source reconstruction using the Adaptive Neuro-Fuzzy Inference System (ANFIS) in the VVER-1000 reactor core

Cited by 10 publications

References 12 publications

Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques

Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques

3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection

Intelligent Algorithm based Maximum Power Point Tracking of Photovoltaic Solar Pump

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