Application of Possibilistic C-Means for Fault Detection in Nuclear Power Plant Data

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Annalisa Perasso ◽  
Cristina Campi ◽  
Cristian Toraci ◽  
Francesco Benvenuto ◽  
Michele Piana ◽  
...  
Keyword(s):  
Time Series ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Fault Detection ◽  
Nuclear Power ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Signal Classification ◽  
Classification Method ◽  
Plant Data

This paper describes a classification method for automatic fault detection in nuclear power plant (NPP) data. The method takes as input time series associated to specific parameters and realizes signal classification by using a clustering algorithm based on possibilistic C-means (PCM). This approach is applied to time series recorded in a CANDU® power plant and is validated by comparison with results provided by a classification method based on principal component analysis (PCA).

Data-Driven Fault Diagnosis for Nuclear Power Plant: The Implicit Model Approach

2018 ◽  
Author(s):  
Zhaoxu Chen ◽  
Xianling Li ◽  
Zhiwu Ke ◽  
Mo Tao ◽  
Yi Feng
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Fault Detection ◽  
Nuclear Power ◽  
Model Identification ◽  
Industrial Process ◽  
Data Driven ◽  
Implicit Model ◽  
Complicated Model ◽  
Model Approach

This paper proposes a data-driven fault detection approach for nuclear power plant. The approach starts from input and output (I/O) data obtained from operating data of industrial process. Due to the model is not explicitly appeared, the proposed approach is named as implicit model approach (IMA). Residual generator is obtained directly from I/O data rather than from the mechanism, based which the algorithm of IMA-based fault detection is proposed. The main advantage of IMA-based fault detection is that it can circumvent complicated model identification. The approach generates parameterized matrices of residual signal inspired by subspace relevant technology without any prior knowledge about mechanisms of the plant. Fault information has been injected to a simulating platform of a compact reactor in the simulation part, by which we verify the effectiveness of IMA-based fault detection.

scholarly journals THE USE OF SENTINEL 1/2 VEGETATION INDEXES WITH GEE TIME SERIES DATA IN DETECTING LAND COVER CHANGES IN THE SINOP NUCLEAR POWER PLANT CONSTRUCTION SITE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 701-706
Author(s):  
E. Çolak ◽  
M. Chandra ◽  
F. Sunar
Keyword(s):  
Time Series ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Land Cover ◽  
Nuclear Power ◽  
Vegetation Index ◽  
Developing Economies ◽  
Vegetation Indices ◽  
Series Data ◽  
Plant Construction

Abstract. Recently, the demand for nuclear power plants has been increasing in developing countries in line with global energy demands. Turkey, one of the developing economies, is also making plans for nuclear power generation since 1970. The Sinop Nuclear Power Plant was a planned nuclear plant located in the Turkey's most northern point in an area where 99% of the land is forest, in Sinop Peninsula. If disputes are resolved and its construction continues, the plant is expected to be put into service in 2028. On the other hand, due to the construction of the nuclear power plant, the land cover in and around the plant site has changed, potentially causing major environmental changes. As an example, more than 650000 trees have been cut down so far for the construction of a nuclear power plant, which may have a negative impact on the region's ecological balances by endangering biodiversity and causing ecological damage. The aim of this study is to detect changes in forest areas from the start of nuclear power plant construction through December 2020 using Sentinel 1 SAR and Sentinel 2 optical time series images. For this purpose, different radar and optical vegetation indices such as Modified Radar Vegetation Index (mRVI), Modified Radar Forest Degradation Index (mRFDI), and Normalized Difference Vegetation Index (NDVI) were applied using Google Earth Engine (GEE) Sentinel 1/2 satellite time series for 2015–2020 period. As a result, the indices used were found to yield findings consistent with the reported negative land cover change. In addition, correlation analysis were made between the radar vegetation indices used and a very high negative correlation (−0.99) was found. The annual distributions of the values of the three indices used were statistically evaluated using boxplots.

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