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Comparative study on state analysis of BOP in NPPs

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2 Author(s)
Gyunyoung Heo ; Dept. of Nucl. & Quantum Eng., Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea ; Soon Heung Chang

Under the deregulation environment, the performance enhancement of balance of plant (BOP) or the turbine cycle in nuclear power plants is being highlighted. To analyze performance level of the turbine cycle, we use the performance test procedures provided from an authorized institution such as American Society of Mechanical Engineers. However, through plant investigation, it was proved that the requirements of the performance test procedures about the reliability and quantity of sensors was difficult to be satisfied. As a solution of this weakness, a state analysis method that is the expanded concept of signal validation, was proposed on the basis of the statistical and the neural network approaches. The authors recommended the statistical linear regression model by analyzing correlation among turbine cycle parameters as a reference state analysis method. Its advantage is that its derivation is not heuristic, it is possible to calculate model uncertainty, and it is easy to apply to an actual plant. The error of the statistical linear regression model is below 3% under normal as well as abnormal system states. Additionally the auto-associative neural network (AANN) model was recommended since the statistical model is impossible to apply to the validation of all of the sensors and is sensitive to the outlier that is the signal located out of statistical distribution. Because there are a lot of sensors need to be validated in turbine cycle, principal component analysis (PCA) and wavelet analysis (WA) were applied as a pre-processor for the reduction of input dimension and for the enhancement of training accuracy, and the results were compared. Both are effective to reduce the number of the input and output dimension, but WA is superior to PCA in the compensation of the outliers. The outlier localization capability of WA enhanced the robustness of the AANN. In case of the AANN with WA, the training accuracy of the model was nearly 100% and the trained neural network restored the degraded signals to the values within ±3% of the true signals.

Published in:

IEEE Transactions on Nuclear Science  (Volume:50 ,  Issue: 4 )