Hydrophobicity Classification of RTV Silicone Rubber-Coated Insulators Using Deep Learning Algorithms
| dc.contributor.author | Mustafa, Farook Abdalla Hussein | |
| dc.date.accessioned | 2022-08-18T19:10:37Z | |
| dc.date.available | 2022-08-18T19:10:37Z | |
| dc.date.issued | 2022-08-18 | |
| dc.date.submitted | 2022-08-16 | |
| dc.description.abstract | Silicone rubber-based outdoor polymeric insulators are widely employed in electric power transmission and distribution networks to replace conventional ceramic insulators, owing to their superior performance in contaminated and wet environments. Silicone rubber (SIR) insulators offer several advantages like high hydrophobicity, low cost, vandalism resistance, and lightweight. However, when exposed to electrical (dry band arcing and partial discharge) and environmental stresses (humidity, ultraviolet radiation, acid rain and pollution) they suffer from different forms of aging. The first form of aging is the temporal loss of hydrophobicity. However, SIR insulators can recover the hydrophobicity property due to the diffusion of the low molecular weight (LMW) from the bulk of the insulating material to the insulators’ surface. Hence, it is important to classify the hydrophobicity status of SIR insulators as an indication of the aging degree. Different methods have been implemented to classify the hydrophobicity of the insulator surface including static contact angle measurement, dynamic contact angle measurement, and hydrophobicity class (HC). The later technique is the most practical method that can be used in the field and can assess a wide surface area. The surface wetting tendency is manually classified using one of six classes, i.e. HC1-HC6, where HC1 refers to a completely hydrophobic surface and HC6 is a completely hydrophilic surface. The main objective of this thesis is to automatically assess the hydrophobicity classes of non-ceramic insulators under a variety of conditions using deep learning techniques. A dataset of hydrophobicity classes (HC1-HC6) was created and prepared including 4197 images each having 2242×24 pixels size to train the proposed model. Several deep learning techniques, including Convolutional Neural Networks (CNN), Transfer Learning (TL), and Object Detection (OD), were used in this thesis to categorize and assess the hydrophobicity classes of ceramic insulators coated with room temperature vulcanized silicone rubber (RTV-SIR). MobileNet model was found to have the highest accuracy and less training time after comparing with other CNN pre-trained models. This model was then trained and tested under several conditions, including indoor, bright, and dark lighting conditions, and achieved accuracy of 97.77%, 89.44%, and 95%, respectively. Moreover, the proposed model achieved a recognition rate of 96.11% when tested on a full-scale silicone rubber insulator. The developed model was then deployed as a web application for convenience in the assessment of hydrophobicity classes. The proposed model could be utilized to evaluate SIR insulators surface conditions in an effective and automatic way under different conditions. | en |
| dc.identifier.uri | http://hdl.handle.net/10012/18575 | |
| dc.language.iso | en | en |
| dc.pending | false | |
| dc.publisher | University of Waterloo | en |
| dc.subject | Object Detection | en |
| dc.subject | Silicone Rubber | en |
| dc.subject | Hydrophobicity Classification | en |
| dc.subject | Dataset | en |
| dc.subject | Deep learning | en |
| dc.subject | Image Processing | en |
| dc.subject | CNN | en |
| dc.subject | Transfer Learning | en |
| dc.title | Hydrophobicity Classification of RTV Silicone Rubber-Coated Insulators Using Deep Learning Algorithms | en |
| dc.type | Master Thesis | en |
| uws-etd.degree | Master of Applied Science | en |
| uws-etd.degree.department | Electrical and Computer Engineering | en |
| uws-etd.degree.discipline | Electrical and Computer Engineering | en |
| uws-etd.degree.grantor | University of Waterloo | en |
| uws-etd.embargo.terms | 0 | en |
| uws.contributor.advisor | El-Hag, Ayman | |
| uws.contributor.affiliation1 | Faculty of Engineering | en |
| uws.peerReviewStatus | Unreviewed | en |
| uws.published.city | Waterloo | en |
| uws.published.country | Canada | en |
| uws.published.province | Ontario | en |
| uws.scholarLevel | Graduate | en |
| uws.typeOfResource | Text | en |