# A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers
## 6 Conclusions and Future Work  
This manuscript endorses an intuitive DGA interpretation algorithm christened KosaNet is proposed. Actual DGA data was obtained from Kenya Power Ltd., the sole utility power company in Kenya. The dataset was used to build and evaluate the performance of KosaNet vis-a-vis other algorithms with multinomial classification abilities. Experimental results have shown that KosaNet has superior classification ability particu \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SN** & **Key Gases** & **Duval** & **Nomography** & **KosaNet** & **Actual Fault** \\ \hline
0 & ARC & D1 & TH and PD & DLE & ARC \\
1 & TH & T3 & TH & TH3 & TH \(>700\)\({}^{\circ}\)C + CD \\
2 & TH & T3 & TH & TH3 & TH \(>700\)\({}^{\circ}\)C \\
3 & TH & T1 & TH and PD & THF & TH \(<300\)\({}^{\circ}\)C \\
4 & ARC & D1 & ARC & DHE & ARC + CD \\
5 & ARC & D1 & ARC & DHE & ARC + CD \\
6 & TH & T3 & TH and PD & TH3 & TH \(>700\)\({}^{\circ}\)C \\
7 & NR & **DT** & **TH** & NOF & NR \\
8 & TH & D1 & ARC & DLE & ARC \\
9 & TH & T2 & TH & TH3 & TH \(>700\)\({}^{\circ}\)C \\
10 & TH & T1 & TH and PD & TH3 & TH\(>700\)\({}^{\circ}\)C + CD \\
11 & NR & T3 & **TH** & NR & NR \\
12 & TH + ARC & T3 & TH & THF & TH \\
13 & TH & T3 & TH & THF & TH \\
14 & **ARC** & **D2** & TH & **NOF** & DP \\
15 & TH & T1 & TH & TH3 & TH \(<300\)\({}^{\circ}\)C \\
16 & ARC & DT & DP et TH & DHE & ARC + CD \\
17 & **ARC** & T3 & ARC & THF & TH \\
18 & TH & T1 & TH & TH3 & TH \(<300\)\({}^{\circ}\)C \\
19 & ARC & T1 & **ARC** & TH3 & TH\(>700\)\({}^{\circ}\)C + CD \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of classification results using Key Gases, Duval Triangle, Nomography and KosaNet. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Decision** & **Naïve** & **Gradient** & **k-NN** & **Random Forest** & **KosaNet** \\  & **Tree** & **Bayes** & **Boosting** & **k-NN** & **Random Forest** & **KosaNet** \\ \hline Accuracy & 0.685 & 0.70 & 0.83 & 0.8967 & 0.9241 & 0.9998 \\ Precision & 0.685 & 0.67 & 0.83 & 0.8967 & 0.9241 & 0.9998 \\ Recall & 0.685 & 0.80 & 0.83 & 0.8967 & 0.9241 & 0.9998 \\ F1 Score & 0.685 & 0.73 & 0.82 & 0.8851 & 0.9241 & 0.9998 \\ Error & 0.315 & 0.30 & 0.17 & 0.1033 & 0.0759 & 0.0002 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average performance rate for each model. larly for multiclass DGA data. With a classification accuracy of 99.98%, KosaNet would greatly enhance the detection of incipient power transformer faults hence giving technicians and managers the opportunity to plan for reparative maintenance. The deployment of KosaNet has the potential to increase network uptime by more than 75%, resulting in fewer disruptions to the supply of electrical energy. We advocate for the use of KosaNet in the analysis and classification of DGA data to enhance the early discovery of silently active fault s in transformers units. In a future study, the authors will explore the use of KosaNet for time series data where IoT-enabled smart sensors will be deployed for real-time observation of transformers. Conceptualization, data collection, methodology, formal analysis, validation, software, investigation, and writing original draft, G.O. ; writing, review, and editing, [PERSON], [PERSON] and D.H.; supervision, [PERSON] and D.H. All authors have read and agreed to the published version of the manuscript. This research study received no funding. Not applicable. [1]**Data Availability Statement:** 3 rd Party Data Restrictions apply to the availability of these data. Data was obtained from Kenya Power Ltd. and are available from the authors upon request with the permission of Kenya Power Ltd.  
G.O. thanks [PERSON] and [PERSON] for the tireless support. The seemingly endless cycle of reviewing and editing this manuscript is highly appreciated. G.O. further thanks the management of Kenya Power Ltd. for providing the data used in this work. The authors have no conflict of interest to declare.