Predicting Missing Value Data on IEC TC10 Datasets for Dissolved Gas Analysis using Tertius Algorithm
IEC TC10 is the most widely used Dissolved Gas Analysis (DGA) measurement dataset nowadays. Many DGA-based studies have been carried out using conventional methods and methods based on Artificial Intelligence Techniques (AITs). DGA is a diagnostic test performed on power transformers to detect and diagnose potential faults. The test involves analyzing the gases that are dissolved in the transformer oil, which can provide important information about the condition of the transformer. DGA is a widely used technique for transformer monitoring and maintenance in the power industry. However, this dataset is not perfect. There are still many problems in this dataset, one of which is the problem of missing value data. This problem will be significant if not appropriately handled. More reliable data from DGA measurement results is an in-dispensable reference in diagnosing faults in power transformers. This study focuses on dealing with the problem of missing value data using the Tertius algorithm, then testing the results using the J48 and Random Forest algorithms. The results obtained are pretty significant. Of the total 56 missing data, 36 could be predicted perfectly. And received the results of measuring accuracy using the J48 method of 62.73% and the Random Forest method of 70.71%. This result shows that the approach we applied is relatively good for handling missing values in IEC TC10 datasets.
M. H. A. Hamid, M. T. Ishak, M. M. Ariffin, N. I. A. Katim, N. A. M. Amin, and N. Azis, “Dissolved gas analysis (DGA) of vegetable oils under electrical stress,” Int. Conf. High Volt. Eng. Power Syst. ICHVEPS 2017 - Proceeding, vol. 2017-Janua, pp. 29–34, 2017, doi: 10.1109/ICHVEPS.2017.8225862.
H. Malik, “Extreme Learning Machine Based Fault Diagnosis of Power Transformer Using IEC TC10 and Its Related Data,” Annu. IEEE India Conf., pp. 1–5, 2015.
N. Ardi, N. A. Setiawan, and T. Bharata Adji, “Analytical incremental learning for power transformer incipient fault diagnosis based on dissolved gas analysis,” Proc. - 2019 5th Int. Conf. Sci. Technol. ICST 2019, pp. 3–6, 2019, doi: 10.1109/ICST47872.2019.9166441.
N. A. Setiawan, Sarjiya, and Z. Adhiarga, “Power transformer incipient faults diagnosis using Dissolved Gas Analysis and Rough Set,” Proc. 2012 IEEE Int. Conf. Cond. Monit. Diagnosis, C. 2012, no. September, pp. 950–953, 2012, doi: 10.1109/CMD.2012.6416311.
A. Pramono, M. Haddin, and D. Nugroho, “Analisis Minyak Transformator Daya Berdasarkan Dissolved Gas Analysis ( Dga ) Menggunakan Data Mining Dengan Algoritma,” J. Telemat., vol. 9, no. 2, pp. 78–91, 2016.
Mukarromah, S. Martha, and Ilhamsyah, “Perbandingan Imputasi Missing Data Menggunakan Metode Mean Dan Metode Algoritma K-Means,” Bul. Ilm. Mat. Stat. dan Ter., vol. 04, no. 3, pp. 305–312, 2015.
Y. Benmahamed, Y. Kemari, M. Teguar, and A. Boubakeur, “Diagnosis of Power Transformer Oil Using KNN and Naïve Bayes Classifiers,” 2018 IEEE 2nd Int. Conf. Dielectr., no. 3, pp. 1–4, 2018.
A. Abu-Siada and S. Islam, “A new approach to identify power transformer criticality and asset management decision based on dissolved gas-in-oil analysis,” IEEE Trans. Dielectr. Electr. Insul., vol. 19, no. 3, pp. 1007–1012, 2012, doi: 10.1109/TDEI.2012.6215106.
S. A. I. Alfarozi, N. A. Setiawan, T. B. Adji, K. Woraratpanya, K. Pasupa, and M. Sugimoto, “Analytical incremental learning: Fast constructive learning method for neural network,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 2016, doi: 10.1007/978-3-319-46672-9_30.
M. Duval and A. DePablo, “Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases,” IEEE Electr. Insul. Mag., vol. 17, no. 2, pp. 31–41, 2001, doi: 10.1109/57.917529.
N. Ardi and Isnayanti, “Structural Equation Modelling-Partial Least Square to Determine the Correlation of Factors Affecting Poverty in Indonesian Provinces,” IOP Conf. Ser. Mater. Sci. Eng., vol. 846, no. 1, pp. 0–13, 2020, doi: 10.1088/1757-899X/846/1/012054.
A. Huda and N. Ardi, “Predictive Analytic on Human Resource Department Data Based on Uncertain Numeric Features Classification,” Int. J. Interact. Mob. Technol., vol. 15, no. 8, pp. 172–181, 2021, doi: 10.3991/ijim.v15i08.20907.
E. Society, IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers, vol. 2008, no. February. 2009.
J. Kaur and N. Madan, “Association Rule Mining: A Survey,” Int. J. Hybrid Inf. Technol., vol. 8, no. 7, pp. 239–242, 2015, doi: 10.14257/ijhit.2015.8.7.22.
P. A. Flach and N. Lachiche, “Confirmation-guided discovery of first-order rules with Tertius,” Mach. Learn., vol. 42, no. 1–2, pp. 61–95, 2001, doi: 10.1023/A:1007656703224.
J. Nahar, K. S. Tickle, A. B. M. S. Ali, and Y. P. P. Chen, “Significant cancer prevention factor extraction: An association rule discovery approach,” J. Med. Syst., vol. 35, no. 3, pp. 353–367, 2011, doi: 10.1007/s10916-009-9372-8.
S. A. Kumar and V. M.N, “Discerning Learner’s Erudition Using Data Mining Techniques,” Int. J. Integr. Technol. Educ., vol. 2, no. 1, pp. 9–14, 2013, doi: 10.5121/ijite.2013.2102.
M. Supriyamenon and P. Rajarajeswari, “A review on association rule mining techniques with respect to their privacy preserving capabilities,” Int. J. Appl. Eng. Res., vol. 12, no. 24, pp. 15484–15488, 2017.
Copyright (c) 2023 Noper Ardi, Supardianto S, Ahmadi Irmansyah Lubis
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).