Dwiputranto, Teguh Handjojo and Setiawan, Noor Akhmad and Adji, Teguh Bharata (2021) DGA-Based Early Transformer Fault Detection using GA-Optimized ANN. In: 2021 International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP), 29-30 September 2021, Jakarta.
Full text not available from this repository. (Request a copy)Abstract
In the electricity transmission and distribution network, power transformer is one of the most important and expensive equipment. The transformers determine the availability and reliability of the power grid, and will have a direct impact on the electricity distribution to consumers and have an impact on the financial sector. One of the efforts to maintain the availability of the transformers is to identify potential faults earlier. Dissolved gas-in-oil analysis (DGA) is a popular technique for this. However, the DGA data is not linear, so need to find an analysis method that is capable to handle this characteristic. The purpose of this experiment is to develop an artificial intelligence based method that can accurately and reliably classify the type of transformer fault based on DGA data with three performance metrics, namely accuracy, precision, and recall. The method proposed is to combine Genetic Algorithm and Artificial Neural Network which is called GA-ANN. GA is used for attribute selection, while ANN is the fault classifier. It was found that the proposed method has superior performance compared to conventional methods. The accuracy of proposed method is 0.95, while for conventional methods, the highest is 0.72. In terms of precision and recall, in general the value is higher with a minimum of 0.89 compared to 0.04 of the conventional methods. The high values of precision and recall are the contribution of Synthetic Minority Over-sampling Technique which is applied in the pre-processing data. © 2021 IEEE.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Cited by: 6 |
Uncontrolled Keywords: | Data handling; Electric power transmission networks; Electric utilities; Fault detection; Genetic algorithms; Oil filled transformers; Power transformers; Artificial neural network; Conventional methods; Dissolved gas in oil analysis; Electricity transmission networks; Genetic algorithm; Power transformer dissolved gas-in-oil analyse; Precision and recall; Synthetic minority over-sampling technique; Synthetic minority over-sampling techniques; Transformer fault detections; Neural networks |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Faculty of Engineering > Electrical and Information Technology Department |
Depositing User: | Sri JUNANDI |
Date Deposited: | 30 Sep 2024 00:57 |
Last Modified: | 30 Sep 2024 00:57 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/4320 |