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Expert System for Predictive Maintenance Transformer using J48 Algorithm
Corresponding Author(s) : Erna Alimudin
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 8, No. 1, February 2023
Abstract
Predictive maintenance can reduce the risk of sudden transformer failure which causes the risk of plant to stop operating. One of transformer predictive maintenance technique is the Dissolved Gas Analysis (DGA) Test Oil Transformer. The gas is interpreted and analyzed to find out and get conclusions about the health condition and also possible problems in the transformer based on IEEE Standards and IEC Standards. To facilitate monitoring, a Decision Support System for Interpretation of Test Results of DGA Oil Immersed Transformer was created to form a database containing transformer data with the amount of main gas from the DGA test results. Next, decision tree was made using the J48 algorithm. The decision tree simplifying and speed up the decision-making process for recommended actions that are displayed on the system. The system also displays a trending graph of the last transformer test and quickly displays a dashboard of transformer status, i.e. normal, alarm, or danger. Engineer will get notification email if any transformer is in danger status. In addition, the system is able to provide information on possible fault types for each transformer. The benefits of this system are that the health condition of the transformer can be monitored properly and corrective action can be taken immediately on a problem based on the results of the decision support system. This will reduce the risk of shutdown and support the reliability of plant operations.
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- M. Kuncoro, Transformasi Pertamina: dilema antara orientasi bisnis & pelayanan publik. Galangpress Group, 2009.
- K. Rhenald and PT PERTAMINA (Persero), Mutasi DNA Powerhouse Pertamina on The Move. Jakarta: Gramedia, 2008.
- C. Zhang, Y. He, B. Du, L. Yuan, B. Li, and S. Jiang, “Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning,” Futur. Gener. Comput. Syst., vol. 108, pp. 533–545, 2020.
- S. C. Ko, T. H. Han, and S. H. Lim, “Dc current limiting operation and power burden characteristics of a flux-coupling type sfcl connected in series between two windings,” Electron., vol. 10, no. 9, 2021.
- C. Fjellstedt, M. I. Ullah, J. Forslund, E. Jonasson, I. Temiz, and K. Thomas, “A Review of AC and DC Collection Grids for Offshore Renewable Energy with a Qualitative Evaluation for Marine Energy Resources,” Energies, vol. 15, no. 16, 2022.
- A. Kadir, Transformator. 1989.
- D. M. Mehta, P. Kundu, A. Chowdhury, and V. K. Lakhiani, “DGA diagnostics save transformers - Case studies,” 2015 Int. Conf. Cond. Assess. Tech. Electr. Syst. CATCON 2015 - Proc., pp. 116–120, 2016.
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- N. Naibaho and (Teknik Elektro/Universitas Krisnadwipayana), “Analisis Kegagalan Transformator berdasarkan Hasil Pengujian DGA,” Semin. Nas. Energi dan Teknol., pp. 98–106, 2018.
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- P. Mirowski and Y. Lecun, “Statistical machine learning and dissolved gas analysis: A review,” IEEE Trans. Power Deliv., vol. 27, no. 4, pp. 1791–1799, 2012.
- Transformers Committee of the IEEE Power Engineering Society, “IEEE Std C57.104TM-2008, IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformer,” 2008.
- International Electrotechnical Commission, “IEC Std 60599-1999, Mineral oil-impregnated electrical equipment in service - Guide to the interpretation of dissolved and free gases analysis,” 1999.
- N. Pattanadech, K. Sasomponsawatline, J. Siriworachanyadee, and W. Angsusatra, “The conformity of DGA interpretation techniques: Experience from transformer 132 units,” Proc. - IEEE Int. Conf. Dielectr. Liq., vol. 2019-June, no. Icdl, pp. 1–4, 2019.
- N. Mahmoudi, M. H. Samimi, and H. Mohseni, “Experiences with transformer diagnosis by DGA: case studies,” IET Gener. Transm. & Distrib., vol. 13, no. 23, pp. 5431–5439, 2019.
- R. Syahputra, Transmisi dan Distribusi Tenaga Listrik. 2016.
- Teguh Sulistyo (Pusat Reaktor Serba Guna/BATAN), “Pengkajian Kondisi Transformator BHT03 Pada RSG - Gas Menggunakan Metoda Dissolved Gas Analysis,” Sigma Epsil., vol. 18, no. 3, pp. 105–113, 2014.
- A. Pramono et al., “Analisis Minyak Transformator Daya Berdasarkan Dissolved Gas Analysis (DGA) Menggunakan Data Mining dengan Algoritma J48,” Telematika, vol. 9, no. 2, pp. 78–91, 2016.
- U. M. Rao, I. Fofana, A. Betie, M. L. Senoussaoui, M. Brahami, and E. Briosso, “Condition monitoring of in-service oil-filled transformers: Case studies and experience,” IEEE Electr. Insul. Mag., vol. 35, no. 6, pp. 33–42, 2019.
- A. D. Ashkezari, H. Ma, T. Saha, and C. Ekanayake, “Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers,” IEEE Trans. Dielectr. Electr. Insul., vol. 20, no. 3, pp. 965–973, 2013.
- B. Zeng, J. Guo, W. Zhu, Z. Xiao, F. Yuan, and S. Huang, “A transformer fault diagnosis model based on hybrid grey wolf optimizer and LS-SVM,” Energies, vol. 12, no. 21, 2019.
- A. G. Karegowda, A. S. Manjunath, G. Ratio, and C. F. Evaluation, “Comparative Study oOf Attribute Selection Using Gain Ratio,” vol. 2, no. 2, pp. 271–277, 2010.
- M. E. A. Senoussaoui, M. Brahami, and I. Fofana, “Combining and comparing various machinelearning algorithms to improve dissolved gas analysis interpretation,” IET Gener. Transm. Distrib., vol. 12, no. 15, pp. 3673–3679, 2018.
- 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.
- S. R. Siva and D. Prabha, “Prediction of customer behaviour analysis using classification algorithms,” in AIP Conference Proceedings 1952, 2018.
References
M. Kuncoro, Transformasi Pertamina: dilema antara orientasi bisnis & pelayanan publik. Galangpress Group, 2009.
K. Rhenald and PT PERTAMINA (Persero), Mutasi DNA Powerhouse Pertamina on The Move. Jakarta: Gramedia, 2008.
C. Zhang, Y. He, B. Du, L. Yuan, B. Li, and S. Jiang, “Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning,” Futur. Gener. Comput. Syst., vol. 108, pp. 533–545, 2020.
S. C. Ko, T. H. Han, and S. H. Lim, “Dc current limiting operation and power burden characteristics of a flux-coupling type sfcl connected in series between two windings,” Electron., vol. 10, no. 9, 2021.
C. Fjellstedt, M. I. Ullah, J. Forslund, E. Jonasson, I. Temiz, and K. Thomas, “A Review of AC and DC Collection Grids for Offshore Renewable Energy with a Qualitative Evaluation for Marine Energy Resources,” Energies, vol. 15, no. 16, 2022.
A. Kadir, Transformator. 1989.
D. M. Mehta, P. Kundu, A. Chowdhury, and V. K. Lakhiani, “DGA diagnostics save transformers - Case studies,” 2015 Int. Conf. Cond. Assess. Tech. Electr. Syst. CATCON 2015 - Proc., pp. 116–120, 2016.
A. (Teknik E.-I. Chumaidy, “Analisis Kegagalan Minyak Isolasi pada Transformator Daya berbasis Kandungan Gas Terlarut,” J. FT, vol. 8, no. 2, pp. 41–54, 2012.
N. Naibaho and (Teknik Elektro/Universitas Krisnadwipayana), “Analisis Kegagalan Transformator berdasarkan Hasil Pengujian DGA,” Semin. Nas. Energi dan Teknol., pp. 98–106, 2018.
J. R. Jung, H. D. Seo, S. J. Kim, and S. W. Kim, “Advanced dissolved gas analysis (DGA) Diagnostic methods with estimation of fault location for power transformer based on field database,” CIGRE Sess. 46, vol. 2016-Augus, no. September, 2016.
P. Mirowski and Y. Lecun, “Statistical machine learning and dissolved gas analysis: A review,” IEEE Trans. Power Deliv., vol. 27, no. 4, pp. 1791–1799, 2012.
Transformers Committee of the IEEE Power Engineering Society, “IEEE Std C57.104TM-2008, IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformer,” 2008.
International Electrotechnical Commission, “IEC Std 60599-1999, Mineral oil-impregnated electrical equipment in service - Guide to the interpretation of dissolved and free gases analysis,” 1999.
N. Pattanadech, K. Sasomponsawatline, J. Siriworachanyadee, and W. Angsusatra, “The conformity of DGA interpretation techniques: Experience from transformer 132 units,” Proc. - IEEE Int. Conf. Dielectr. Liq., vol. 2019-June, no. Icdl, pp. 1–4, 2019.
N. Mahmoudi, M. H. Samimi, and H. Mohseni, “Experiences with transformer diagnosis by DGA: case studies,” IET Gener. Transm. & Distrib., vol. 13, no. 23, pp. 5431–5439, 2019.
R. Syahputra, Transmisi dan Distribusi Tenaga Listrik. 2016.
Teguh Sulistyo (Pusat Reaktor Serba Guna/BATAN), “Pengkajian Kondisi Transformator BHT03 Pada RSG - Gas Menggunakan Metoda Dissolved Gas Analysis,” Sigma Epsil., vol. 18, no. 3, pp. 105–113, 2014.
A. Pramono et al., “Analisis Minyak Transformator Daya Berdasarkan Dissolved Gas Analysis (DGA) Menggunakan Data Mining dengan Algoritma J48,” Telematika, vol. 9, no. 2, pp. 78–91, 2016.
U. M. Rao, I. Fofana, A. Betie, M. L. Senoussaoui, M. Brahami, and E. Briosso, “Condition monitoring of in-service oil-filled transformers: Case studies and experience,” IEEE Electr. Insul. Mag., vol. 35, no. 6, pp. 33–42, 2019.
A. D. Ashkezari, H. Ma, T. Saha, and C. Ekanayake, “Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers,” IEEE Trans. Dielectr. Electr. Insul., vol. 20, no. 3, pp. 965–973, 2013.
B. Zeng, J. Guo, W. Zhu, Z. Xiao, F. Yuan, and S. Huang, “A transformer fault diagnosis model based on hybrid grey wolf optimizer and LS-SVM,” Energies, vol. 12, no. 21, 2019.
A. G. Karegowda, A. S. Manjunath, G. Ratio, and C. F. Evaluation, “Comparative Study oOf Attribute Selection Using Gain Ratio,” vol. 2, no. 2, pp. 271–277, 2010.
M. E. A. Senoussaoui, M. Brahami, and I. Fofana, “Combining and comparing various machinelearning algorithms to improve dissolved gas analysis interpretation,” IET Gener. Transm. Distrib., vol. 12, no. 15, pp. 3673–3679, 2018.
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.
S. R. Siva and D. Prabha, “Prediction of customer behaviour analysis using classification algorithms,” in AIP Conference Proceedings 1952, 2018.