%0 Journal Article %1 david_chigozie_madumere_2024_10951826 %A Madumere, David Chigozie %A Ashigwuike, Evans C. %A Gafai, N.B. %D 2024 %J Global Journal of Engineering and Technology Advances %K Acidicity Oil Transformer %N 3 %P 066–074 %R 10.30574/gjeta.2024.18.3.0194 %T Expert system for improving and controlling insulation system of service transformers using fuzzy logic controller %U https://gjeta.com/content/expert-system-improving-and-controlling-insulation-system-service-transformers-using-fuzzy %V 18 %X Implementation of the smart transformer concept is critical for the deployment of IOT-based smart grids. Top manufacturers of power electrics develop and adopt online monitoring systems. Such systems become part of high-voltage grid and unit transformers. However, furnace transformers are a broad category that this change does not affect yet. At the same time, adoption of diagnostic systems for furnace transformers is relevant because they are a heavy-duty application with no redundancy. Creating any such system requires a well-founded mathematical analysis of the facility’s condition, carefully selected diagnostic parameters, and set points thereof, which serve as the condition categories. The goal hereof was to create an expert system to detect insulation breach and its expansion as well as to evaluate the risk it poses to the system; the core mechanism is mathematical processing of trends in partial discharge (PD). This research work examined the acidity of distribution transformer oil in service through laboratory tests using a case study of installed distribution transformers at Abuja metropolis network comprising ten Feeders. The result shows the minimum breakdown voltage of 40KV/mm and maximum breakdown voltage of 58KV/mm. The maximum transformer oil Acidity is 0.1143mgKOH/g and the minimum transformer oil Acidity level is 0.0954mgKOH/g. The transformer efficiency without fuzzy is 82.89% and transformer efficiency with fuzzy controller is 98.10%.

@article{david_chigozie_madumere_2024_10951826, abstract = {Implementation of the smart transformer concept is critical for the deployment of IOT-based smart grids. Top manufacturers of power electrics develop and adopt online monitoring systems. Such systems become part of high-voltage grid and unit transformers. However, furnace transformers are a broad category that this change does not affect yet. At the same time, adoption of diagnostic systems for furnace transformers is relevant because they are a heavy-duty application with no redundancy. Creating any such system requires a well-founded mathematical analysis of the facility’s condition, carefully selected diagnostic parameters, and set points thereof, which serve as the condition categories. The goal hereof was to create an expert system to detect insulation breach and its expansion as well as to evaluate the risk it poses to the system; the core mechanism is mathematical processing of trends in partial discharge (PD). This research work examined the acidity of distribution transformer oil in service through laboratory tests using a case study of installed distribution transformers at Abuja metropolis network comprising ten Feeders. The result shows the minimum breakdown voltage of 40KV/mm and maximum breakdown voltage of 58KV/mm. The maximum transformer oil Acidity is 0.1143mgKOH/g and the minimum transformer oil Acidity level is 0.0954mgKOH/g. The transformer efficiency without fuzzy is 82.89% and transformer efficiency with fuzzy controller is 98.10%.}, added-at = {2024-04-10T13:00:03.000+0200}, author = {Madumere, David Chigozie and Ashigwuike, Evans C. and Gafai, N.B.}, biburl = {https://www.bibsonomy.org/bibtex/27d8a3ad6475399061e410041ca572e14/gjetajournal}, doi = {10.30574/gjeta.2024.18.3.0194}, interhash = {161bb1e3be9c32dd8de291a4a98ad3b3}, intrahash = {7d8a3ad6475399061e410041ca572e14}, issn = {2582-5003}, journal = {{Global Journal of Engineering and Technology Advances}}, keywords = {Acidicity Oil Transformer}, month = apr, number = 3, pages = {066–074}, timestamp = {2024-04-10T13:00:03.000+0200}, title = {Expert system for improving and controlling insulation system of service transformers using fuzzy logic controller}, url = {https://gjeta.com/content/expert-system-improving-and-controlling-insulation-system-service-transformers-using-fuzzy}, volume = 18, year = 2024 }