# A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers
## 1 Introduction
It is an investigative process in which oil samples collected from oil-immersed transformers are inspected for fault gases. The concentration, ratio, and type of the gases tell a tale about the internal condition of the transformer. Insulating oil not only performs functions such as cooling the unit and housing arcs, but more significantly, it dissolves any gases produced as a result of cellulose deterioration or ambient moisture from the environment. A common cause of early failure of transformer units is the deterioration of insulating oil. It is estimated that about 70-80% of the faults in a transformer are incipient in nature which implies that any  
Figure 1: Arrangement of a Single-Phase Transformer Windings and Core. effort to detect these faults can potentially reduce the manifestation of faults on the grid by a similar margin [15; 17]. Classical methods of DGA interpretation include Key Gases, Duval and Nomography [18]. The interpretation of DGA data has for decades relied on these classical approaches. These classical approaches however tend to rely extensively on the experience, judgment, and intuition of a human expert rather than technical formulation. This reliance on a human benefactor has often led to an inconclusive assessment of the severity of the faults or in the extreme case, a misidentification altogether [19]. Numerous studies have been conducted that propose methods by which the severity of incipient power transformers faults can be determined. In [20] for instance, [PERSON] et al. studied fuzzy logic to determine faults on power transformers created by using gas levels and gas rates as well as interpreted DGA data. The Duval Pentagon Method (DPM) was assimilated with the Support Vector Machine (SVM) algorithm being the chosen interpretation method. The study resulted in an accuracy score of 97.5%. In [21] a study was conducted which designed a smart fuzzy reinforcement learning-based fault classifier for transformers. DGA data, collected from actual transformers and other credible secondary sources, were used. The results showed that the suggested fuzzy reinforcement learning technique is superior as compared to other contemporary approaches attaining an accuracy score of 99.7%. Fuzzy logic models however tend to perform poorly when subjected to new data. In [22] a multilayer perceptron (MLP) network is discussed with boundaries defined using a fuzzy class. The model is centered on the Duval Pentagon and gas ratio combination. A multilayer perceptron network was trained on actual DGA records with fault conditions specifically marked. The proposed method had a good region of certainty for some fault conditions while for others the uncertainty was high. Generally, the method yielded an overall accuracy of only 83%. In addition, the method has a low performance when dealing with multinomial data. It is within this perspective that we propose to develop a model that elegantly handles multinomial DGA data while simultaneously maintain high accuracy. The proposed model shall be installed on a cloud-based server that will perform DGA data interpretation. This cloud-based machine shall act as a data aggregator. Sensors installed on the transformer shall send real-time DGA data readings which will then be analyzed and displayed at the monitoring and control center as proposed in [23]. This solution is designed to use low-cost devices and only requires the purchase of an inexpensive IoT monitoring device that costs about $100 each. The installation of the monitoring device physically on the transformer will be done by using an inlet and outlet valve fashioned as a closed loop. This article is hereafter structured in the following fashion: in Section 2, related research is discussed; Section 3 offers an account of the methods and modeling used; in Section 4 the setup used to simulate the model is explained; Section 5 gives the results and discussion, where the outcomes are lengthily espoused; and, in Section 6, the conclusion is explained, and future work is presented.