## 5 Conclusions  
This paper presents the application and expert knowledge of the C\({}_{2}\)H\({}_{2}\)/H\({}_{2}\) ratio collected in [6,7] in order to determine transformer oil contamination from the OLTC gases in a group of power transformers of a DSO. The 175 power transformers studied based on 388 DGA results have different ages, voltage classes, and power ratings. Based on the application of the ratio, thirteen transformers are defined as contaminated, and one transformer is classified as uncontaminated due to low acetylene values, in the 8-11 ppm range. The expert interpretations of the DGA results for 13 power transformers define them as contaminated based on a study of the rest of the DGA results for each of them. As expected, the trend in the results is that old transformers are contaminated by the OLTC gases, and sixty-three percent of the contaminated transformers have ages in the range of 30-60 years. Based on these results, it is observed that the youngest contaminated transformers have their tap selector and diverter switch in different compartments, whereas the oldest contaminated transformers have their tap selector and diverter switch in the same compartment. It should be noted that the methodology performed in this study is an assumption of transformer oil contamination based on the study of the DGA samples. Neither the application of the ratio, nor the EK can guarantee this contamination. The evaluation of the future DGA results of the transformers defined as contaminated will have to be performed taking into account the number of operations of the OLTCs, in order to discard the existence of faults in the insulation of the transformer. In addition, this paper proposes the application of ML techniques in the developed classification methodology. The proposed approach achieves an effective technique to distinguish whether or not there is transformer oil contamination from the OLTC gases using a DT. The algorithm is trained using different percentages of training and test data to obtain the optimal DT. Based on the proposed approach, a trained classifier is obtained to make it possible to perform the classification automatically. The results of the trained classifier versus the classifications made in this study show an accuracy of 99.76%. From the classification of transformers as contaminated or uncontaminated performed in this study, the calculation results for the limit values of the C\({}_{2}\)H\({}_{2}\) concentration [39] are improved and divided into two groups, depending on the OLTC communication between the main tank and the OLTC compartment, similar to the IEC classification. In future work, the model trained from the ML techniques will be used with new DGA results. Its accuracy will be checked, and the model will be improved if necessary. Conceptualisation, S.B., M.M. and A.A.; methodology and software, S.B. ; validation, all authors contributed equally; formal analysis, S.B., R.M. and A.L. ; investigation and resources, all authors contributed equally; data analysis, S.B. ; writing, original draft preparation, review and editing, all authors contributed equally; and funding acquisition, M.M. All authors read and agreed to the published version of the manuscript. This work was partially financed by the EU Regional Development Fund (FEDER) and the Spanish Government under RETOS-COLABORACION RTC-2017-6782-3 and by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 864579 (FLEXIGRID). The authors acknowledge the support received from Viesgo. The authors thank CIGRE for permission to reproduce information from its Technical Brochures. The authors thank the International Electotechnical Commission (IEC) for permission to reproduce information from its International Standards. All such extracts are copyrighted by IEC, Geneva, Switzerland. All rights reserved. Further information on the IEC is available from www:iec.ch.