Control Aid Implementation -Modelling and simulation of triple extruder-

Sammanfattning: In the production of their High Voltage Direct Current Cables (HVDC) and High Voltage Alternating Current Cables (HVAC), NKT uses triple extruders to create layers of insulation and semi-conduction. A model to predict the effect of extruder inputs on the cable’s insulation and semi-conducting layers has been created and trained to predict the extruder in discrete time. The project developed a deep learning extreme learning machine algorithm and proved that it has good enough accuracy and generalization to predict the cable states as a function of extruder motor and line speeds as well as distributor screw positions. The project also explored how to evaluate and test a model, despite the fact it has insufficient data, by dividing it into two parts and letting them be trained independently, with a subset of unknown inputs. The results were satisfactory. A way of combining the models was also proposed but was not further explored. While the model shows that it can accurately describe the extruder in the ranges of where it is trained, the data acquisition was poor and hindered the collection of good enough training sets to let the model predict over the whole input range. The model does, however, act as a proof of concept that can be further developed into a finished state. It also showed that it can still predict the correct trends of the extruder even if the model is acting outside of the range where it is trained.