A Neural Networks Design Methodology for Detecting Loss of Coolant Accidents in Nuclear Power Plants

This article presented an improvement of marine steam turbine conventional exergy analysis by application of neural networks. The conventional exergy analysis requires numerous measurements in seven different turbine operating points at each load, while the intention of MLP (Multilayer Perceptron) neural network-based analysis was to investigate the possibilities for measurements reducing. At the same time, the accuracy and precision of the obtained results should be maintained. In MLP analysis, six separate models are trained. Due to a low number of instances within the data set, a 10-fold cross-validation algorithm is performed. The stated goal is achieved and the best solution suggests that MLP application enables reducing of measurements to only three turbine operating points. In the best solution, MLP model errors falling within the desired error ranges (Mean Relative Error) MRE < 2.0% and (Coefficient of Correlation) R2 > 0.95 for the whole turbine and each of its cylinders.

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A Neural Networks Design Methodology for Detecting Loss of Coolant Accidents in Nuclear Power Plants

Cited by 2 publications

References 18 publications

Data-theoretic approach for socio-technical risk analysis: Text mining licensee event reports of U.S. nuclear power plants

Data-theoretic approach for socio-technical risk analysis: Text mining licensee event reports of U.S. nuclear power plants

Improvement of Marine Steam Turbine Conventional Exergy Analysis by Neural Network Application

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