Dissolved Gas Analysis of Generator Step Up Transformer in Grati Power Plant Using Random Forest Based Method


  • Muhammad Akmal Afibuddin Putra State Polytechnic of Malang
  • Rahman Azis Prasojo State Polytechnic of Malang
  • Anang Dasa Novfowan State Polytechnic of Malang
  • Neelmani Neelmani Comenius University


Transformers, Dissolved Gas Analysis, Duval Triangle Method, Duval Pentagon Method, Random Forest


Transformers are one of the important electrical equipment in the power system. To prevent some electrical contact on the component in transformers, an insulator or dielectric material is needed likely insulating oil. DGA test is important for diagnosis and deciding the maintenance of transformers. Duval Triangle and Duval Pentagon methods are DGA identification methods with the highest of accuracy compared to other methods. The data used in this article is from the DGA measurement test of transformers GT 3.1 Steam and Gas Power Plant Grati. The DGA data was analyzed by Random Forest based-model of Duval Triangle and Pentagon method, in accordance to IEEE C57.104-2019 and IEC 60599-2015 guidelines. Random Forest based-model has the best performance in implemented Duval method than others. The result of DGA identification using Random Forest based-model showed PD and S for Duval Triangle, and S for Duval Pentagon and from the results of identification using the Duval Triangle and Pentagon it does not always show the same results on the same test sample, so it is necessary to identify the history of DGA testing to get accurate results. This article presents the use of the combined Duval Triangle and Pentagon for diagnosis transformers.


I. Fofana and Y. Hadjadj, “Power transformer diagnostics, monitoring and design features,” Energies, vol. 11, no. 12, pp. 1–5, 2018.

X. Wang, C. Tang, B. Huang, J. Hao, and G. Chen, “Review of research progress on the electrical properties and modification of mineral insulating oils used in power transformers,” Energies, vol. 11, no. 3, 2018.

Hermawan, A. Syakur, and I. Irwan, “Analisis Gas Terlarut Pada Minyak Isolasi Transformator Tenaga Akibat Pembebanan dan Penuaan,” Teknik, vol. 32, no. 3, 2011.

K. Diwyacitta, R. A. Prasojo, S. Suwarno, and H. Gumilang, “Effects of lifetime and loading factor on dissolved gases in power transformers,” ICECOS 2017 - Proceeding 2017 Int. Conf. Electr. Eng. Comput. Sci. Sustain. Cult. Herit. Towar. Smart Environ. Better Futur., pp. 243–247, 2017.

S. Bustamante, M. Manana, A. Arroyo, P. Castro, A. Laso, and R. Martinez, “Dissolved gas analysis equipment for online monitoring of transformer oil: A review,” Sensors (Switzerland), vol. 19, no. 19, pp. 4–12, 2019.

J. Faiz and M. Soleimani, “Dissolved gas analysis evaluation in electric power transformers using conventional methods a review,” IEEE Trans. Dielectr. Electr. Insul., vol. 24, no. 2, pp. 1239–1248, 2017.

S. Surawijaya, R. A. Prasojo, W. Riga Tamma, I. G. Ngurah Mahendrayana, and Suwarno, “Diagnosis of Power Transformer Condition using Dissolved Gas Analysis Technique: Case Studies at Geothermal Power Plants in Indonesia,” in Proceedings of the 2nd International Conference on High Voltage Engineering and Power Systems: Towards Sustainable and Reliable Power Delivery, ICHVEPS 2019, 2019, pp. 2–7.

S. Bustamante, M. Manana, A. Arroyo, R. Martinez, and A. Laso, “A methodology for the calculation of typical gas concentration values and sampling intervals in the power transformers of a distribution system operator,” Energies, vol. 13, no. 22, pp. 7–9, 2020.

S. Permana, S. Sumarto, and W. S. Saputra, “Analysis of Transformer Conditions using Triangle Duval Method,” in IOP Conference Series: Materials Science and Engineering, 2018, vol. 384, no. 1.

M. U. Farooque, S. A. Wani, and S. A. Khan, “Artificial neural network (ANN) based implementation of Duval Pentagon,” 2015 Int. Conf. Cond. Assess. Tech. Electr. Syst. CATCON 2015 - Proc., pp. 46–50, 2016.

S. A. Wani, D. Gupta, M. U. Farooque, and S. A. Khan, “Multiple incipient fault classification approach for enhancing the accuracy of dissolved gas analysis (DGA),” IET Sci. Meas. Technol., vol. 13, no. 7, pp. 959–967, 2019.

Ekojono, R. A. Prasojo, M. E. Apriyani, and A. N. Rahmanto, “Investigation on machine learning algorithms to support transformer dissolved gas analysis fault identification,” Electr. Eng., 2022.

IEEE Std C57.104-2019, “IEEE Guide for the Interpretation of Gases Generated in Mineral Oil-Immersed Transformers.” 2019.

T. U. Mawelela, A. F. Nnachi, A. O. Akumu and B. T. Abe, "Fault Diagnosis of Power Transformers Using Duval Triangle," 2020 IEEE PES/IAS PowerAfrica, Nairobi, Kenya, 2020, pp. 1-5.

M. Duval and L. Lamarre, "The new Duval Pentagons available for DGA diagnosis in transformers filled with mineral and ester oils," 2017 IEEE Electrical Insulation Conference (EIC), Baltimore, MD, USA, 2017, pp. 279-281.

IEC 60599, “Mineral oil-filled electrical equipment in service - Guidance on the interpretation of dissolved and free gases analysis.” 2015.

M. Duval and L. Lamarre, “The Duval Pentagon — A New Complementary Tool for the,” IEEE Electrical Insulation Magazine, vol. 30, no. 6, pp. 9–12, 2014.

M. Duval and T. Heizmann, “Identification of stray gassing of inhibited and uninhibited mineral oils in transformers,” Energies, vol. 13, no. 15, 2020.

IEC 60296:2020, Fluids for electrotechnical applications – Mineral insulating oils for electrical equipment, vol. 2014. 2020.




How to Cite

M. A. A. Putra, R. A. Prasojo, A. D. Novfowan, and N. Neelmani, “Dissolved Gas Analysis of Generator Step Up Transformer in Grati Power Plant Using Random Forest Based Method”, Jartel, vol. 13, no. 1, pp. 51-58, Feb. 2023.