1,834 research outputs found
Bagged ensemble of Fuzzy C-Means classifiers for nuclear transient identification
This paper presents an ensemble-based scheme for nuclear transient identification. The approach adopted to construct the ensemble of classifiers is bagging; the novelty consists in using supervised fuzzy C-means (FCM) classifiers as base classifiers of the ensemble. The performance of the proposed classification scheme has been verified by comparison with a single supervised, evolutionary-optimized FCM classifier with respect of the task of classifying artificial datasets. The results obtained indicate that in the cases of datasets of large or very small sizes and/or complex decision boundaries, the bagging ensembles can improve classification accuracy. Then, the approach has been applied to the identification of simulated transients in the feedwater system of a boiling water reactor (BWR)
Bagged ensemble of Fuzzy C-Means classifiers for nuclear transient identification
This paper presents an ensemble-based scheme for nuclear transient identification. The approach adopted to construct the ensemble of classifiers is bagging; the novelty consists in using supervised fuzzy C-means (FCM) classifiers as base classifiers of the ensemble. The performance of the proposed classification scheme has been verified by comparison with a single supervised, evolutionary-optimized FCM classifier with respect of the task of classifying artificial datasets. The results obtained indicate that in the cases of datasets of large or very small sizes and/or complex decision boundaries, the bagging ensembles can improve classification accuracy. Then, the approach has been applied to the identification of simulated transients in the feedwater system of a boiling water reactor (BWR)
Representation Learning based and Interpretable Reactor System Diagnosis Using Denoising Padded Autoencoder
With the mass construction of Gen III nuclear reactors, it is a popular trend
to use deep learning (DL) techniques for fast and effective diagnosis of
possible accidents. To overcome the common problems of previous work in
diagnosing reactor accidents using deep learning theory, this paper proposes a
diagnostic process that ensures robustness to noisy and crippled data and is
interpretable. First, a novel Denoising Padded Autoencoder (DPAE) is proposed
for representation extraction of monitoring data, with representation extractor
still effective on disturbed data with signal-to-noise ratios up to 25.0 and
monitoring data missing up to 40.0%. Secondly, a diagnostic framework using
DPAE encoder for extraction of representations followed by shallow statistical
learning algorithms is proposed, and such stepwise diagnostic approach is
tested on disturbed datasets with 41.8% and 80.8% higher classification and
regression task evaluation metrics, in comparison with the end-to-end
diagnostic approaches. Finally, a hierarchical interpretation algorithm using
SHAP and feature ablation is presented to analyze the importance of the input
monitoring parameters and validate the effectiveness of the high importance
parameters. The outcomes of this study provide a referential method for
building robust and interpretable intelligent reactor anomaly diagnosis systems
in scenarios with high safety requirements
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Neural networks and their application to nuclear power plant diagnosis
The authors present a survey of artificial neural network-based computer systems that have been proposed over the last decade for the detection and identification of component faults in thermal-hydraulic systems of nuclear power plants. The capabilities and advantages of applying neural networks as decision support systems for nuclear power plant operators and their inherent characteristics are discussed along with their limitations and drawbacks. The types of neural network structures used and their applications are described and the issues of process diagnosis and neural network-based diagnostic systems are identified. A total of thirty-four publications are reviewed
Exhaustive Search-based Model for Hybrid Sensor Network
A new model for a cluster of hybrid sensors network with multi sub-clusters
is proposed. The model is in particular relevant to the early warning system in
a large scale monitoring system in, for example, a nuclear power plant. It
mainly addresses to a safety critical system which requires real-time processes
with high accuracy. The mathematical model is based on the extended
conventional search algorithm with certain interactions among the nearest
neighborhood of sensors. It is argued that the model could realize a highly
accurate decision support system with less number of parameters. A case of one
dimensional interaction function is discussed, and a simple algorithm for the
model is also given.Comment: 6 pages, Proceeding of the International Conference on Intelligent &
Advanced Systems 2012 pp. 557-56
DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS
Steam boilers represent the main equipment in the power plant. Some boiler trips may
lead to an entire shutdown of the plant, which is economically burdensome. An early
detection and diagnosis of the boiler trips is crucial to maintain normal and safe
operational conditions of the plant. Numbers of methodologies have been proposed in
the literature for fault diagnosis of power plants. However, rapid deployment of these
methodologies is difficult to be achieved due to certain inherent limitations such as
system inability to learn or a dynamically improve the system performance and the
brittleness of the system beyond its domain of expertise. As a potential solution to
these problems, two artificial intelligent monitoring systems specialized in boiler trips
have been proposed and coded within the MA TLAB environment in the present work.
The training and validation of the two systems have been performed using real
operational data which was captured from the plant integrated acquisition system of
JANAMANJUNG coal-fired power plant. An integrated plant data preparation
framework for seven boiler trips with related operational variables, has been proposed
for the training and validation of the proposed artificial intelligent systems. The feedforward
neural network methodology has been adopted as a major computational
intelligent tool in both systems. The root mean square error has been widely used as a
performance indicator of the proposed systems. The first intelligent monitoring
system represents the use of the pure artificial neural network system for boiler trip
detection. The final architecture for this system has been explored after investigation
of various main neural network topology combinations which include one and two
hidden layers, one to ten neurons for each hidden layer, three types of activation
function, and four types of multidimensional minimization training algorithms. It has
been found that there was no general neural network topology combination that can
be applied for all boiler trips. All seven boiler trips under consideration had been
detected by the proposed systems before or at the same time as the plant control system. The second intelligent monitoring system represents mergmg of genetic
algorithms and artificial neural networks as a hybrid intelligent system. For this
hybrid intelligent system, the selection of appropriate variables from hundreds of
boiler operation variables with optimal neural network topology combinations to
monitor boiler trips was a major concern. The encoding and optimization process
using genetic algorithms has been applied successfully. A slightly lower root mean
square error was observed in the second system which reveals that the hybrid
intelligent system performed better than the pure neural network system. Also, the
optimal selection of the most influencing variables was performed successfully by the
hybrid intelligent system. The proposed artificial intelligent systems could be adopted
on-line as a reliable controller of the thermal power plant boiler
Nuclear Power - System Simulations and Operation
At the onset of the 21st century, we are searching for reliable and sustainable energy sources that have a potential to support growing economies developing at accelerated growth rates, technology advances improving quality of life and becoming available to larger and larger populations. The quest for robust sustainable energy supplies meeting the above constraints leads us to the nuclear power technology. Today's nuclear reactors are safe and highly efficient energy systems that offer electricity and a multitude of co-generation energy products ranging from potable water to heat for industrial applications. Catastrophic earthquake and tsunami events in Japan resulted in the nuclear accident that forced us to rethink our approach to nuclear safety, requirements and facilitated growing interests in designs, which can withstand natural disasters and avoid catastrophic consequences. This book is one in a series of books on nuclear power published by InTech. It consists of ten chapters on system simulations and operational aspects. Our book does not aim at a complete coverage or a broad range. Instead, the included chapters shine light at existing challenges, solutions and approaches. Authors hope to share ideas and findings so that new ideas and directions can potentially be developed focusing on operational characteristics of nuclear power plants. The consistent thread throughout all chapters is the system-thinking approach synthesizing provided information and ideas. The book targets everyone with interests in system simulations and nuclear power operational aspects as its potential readership groups - students, researchers and practitioners
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