# A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers
## 2 Related Works  
Recent advancement in field-deplorable computer technology has revamped interest in the deployment of computing technology for mundane tasks especially those that are labor-intensive. In this regard, various research scholars have undertaken several studies to explore novel approaches of automated faultfinding on the electrical grid. For instance in [24], [PERSON] et al. evaluated the \(k\)NN algorithm and Naive Bayes to diagnose transformer oil insulation through the analysis of DGA data. Five input vectors namely the [PERSON] triangle reports, Dornrenberg ratios, Rogers ratios, DGA data (in percentages and ppm) were used in the study to map to five output classes. The rate of accuracy for both classifiers was determined using 155 samples. Results showed that the kNN algorithm generally performed better than the Naive Bayes technique with an accuracy score of 92%, when [PERSON]'s triangle reports were considered. [PERSON] et al. [25] undertook a study that described the application of two fault determination techniques; k-Nearest Neighbor(kNN) and Bayesian classifier algorithms. A data set with 1340 entries of seven gases was used to build the classifier. The study discovered that after \(k\) was set to 13 (\(k=13\)), there was no further improvement in the accuracy. [PERSON] et al. in [PERSON] et al. (2019) proposed a new DGA procedure which works by combining the gas ratio method with the [PERSON]'s Pentagon 1; the two techniques that are known to have a high prediction accuracy. In essence, the technique is a blend of the gains of the ratio and graphical methods. The faulty zones were identified based on the level of confidence in the prediction. The study resulted in an overall prediction accuracy of 83%. An interesting observation was made in [PERSON] et al. (2019) where it was noted that interpretation methods based on ratios may fail to make accurate inferences when multiple fault conditions exist in the data set. To correct this, [PERSON] et al. (2019) introduced an enhanced approach to overcome the limitations of conservative DGA analysis practices, in addition to automating and standardizing the DGA interpretation techniques. An expert system was built using Gene Expression Programming. The results showed that the proposed approach provided greater reliability than others that use individual conventional techniques that are presently embraced in industrial practice. [PERSON] et al. (2019) made a proposition that entailed installing a network of sensors to overlay the main power cables of the distribution network. The setup involved installing a network node in each maintenance hole along the line. The node comprised a current transformer that encircles every cable individually. The current transformer permits simultaneous the continuous measurement and communication non-intrusively. This kind of setup permits the system operator to monitor the currents of each phases of the feeder system underground. A probable use-case was proposed whereby multiple variables (particularly humidity and temperature) that are obtained can be used to model the aging of the cables. In [PERSON] et al. (2019), swarm optimization techniques and artificial neural networks are combined and used to predict incipient transformer faults. It was noted that artificial neural networks are a good approach for modeling relationships that are difficult to describe explicitly. Evolutionary Particle Swarm Optimization techniques mimic the natural behavior of how birds flock or fish school together. Implementation of the ANN and PSO algorithms was done in the MATLAB programming language. The efficacy of several PSO techniques when combined with ANN were compared with the experimental outcome from the real fault diagnosed. The results showed that evolutionary PSO yielded the highest accuracy score at 98%. In yet another study [PERSON] and [PERSON] (2018) modified evolutionary particle swarm optimization then combined it with Support Vector Machine. This proposed hybrid algorithm was proposed to identify incipient faults.