The power output at the Forsmark nuclear power plant sometimes deviates from the expected value. The causes of these deviations are sometimes known and sometimes unknown. Three types of machine learning methods (k-nearest neighbors, support vector machines and linear regression) were trained to predict whether or not the power deviation would be outside an expected interval. The data used to train the models was gathered from points in the power production process and the data signals consisted mostly of temperatures, pressures and flows. A large part of the project was dedicated to preparing the data before using it to train the models. Temperature signals were shown to be the best predictors of deviation in power, followed by pressure and flow. The model type that performed the best was k-nearest neighbors, followed by support vector machines and linear regression. Principal component analysis was performed to reduce the size of the training datasets and was found to perform equally well in the prediction task as when principal component analysis was not used.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-443225 |
| Date | January 2021 |
| Creators | Björn, Albin |
| Publisher | Uppsala universitet, Institutionen för fysik och astronomi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC ES, 1650-8300 ; 21007 |
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