Power transformers are considered important and expensive items in electrical power networks. In this regard, the early discovery of potential faults in transformers considering datasets collected from diverse sensors can guarantee the continuous operation of electrical systems. Indeed, the discontinuity of these transformers is expensive and can lead to excessive economic losses for the power utilities. Dissolved gas analysis (DGA), as well as partial discharge (PD) tests considering different intelligent sensors for the measurement process, are used as diagnostic techniques for detecting the oil insulation level. This paper includes two parts; the first part is about the integration among the diagnosis results of recognized dissolved gas analysis techniques, in this part, the proposed techniques are classified into four techniques. The integration between the different DGA techniques not only improves the oil fault condition monitoring but also overcomes the individual weakness, and this positive feature is proved by using 532 samples from the Egyptian Electricity Transmission Company (EETC). The second part overview the experimental setup for (66/11.86 kV–40 MVA) power transformer which exists in the Egyptian Electricity Transmission Company (EETC), the first section in this part analyzes the dissolved gases concentricity for many samples, and the second section illustrates the measurement of PD particularly in this case study. The results demonstrate that precise interpretation of oil transformers can be provided to system operators, thanks to the combination of the most appropriate techniques.

Transformers are expensive but essential pieces of machinery in electric power systems. As a result, the electrical network suffers large financial losses when transformers fail. Therefore, early identification of likely transformer problems has a favorable impact on the dependability and continued functioning of electrical grids. One common chemical test for identifying potential problems with power transformers is the dissolved gas analysis. Conventional DGA approaches include the Rogers ratio, key gas ratio, Dornenburg ratio technique, Duval, and IEC gas ratio procedures. Unconventional DGA techniques include, among others, conditional probability, clustering, Rogers Refined, and IEC Refined. This work offers a novel method for accurately interpreting transformer faults in this regard. The proposed methodology is built around a blend of traditional and non-traditional methods with innovative processes. The Egyptian Electricity Transmission Company (EETC) provided 386 datasets with known defects, which are used to demonstrate the beneficial aspects of the suggested approach. In order to verify the enhanced accuracy of the proposed DGA technique, it is then compared with more contemporary DGA techniques utilizing the same concentration of sample gases. The developed strategy came to the conclusion that the overall accuracy of fault detection is improved by integrating several DGA techniques with varying fault accuracy. Furthermore, a case study on aged transformers with a rating of 66/11Kv has been provided to examine how aging affects dissolved gases.