# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 5 Conclusions  
The accurate and precise detection of the islanding condition plays a very significant part in DGS. This paper highlights a relatively new approach of using the auto-grounding method and data mining decision tree technique to correctly detect the islanding condition of DG. The low-cost auto-grounding method was designed and simulated, and it was observed that the accurate anti-islanding can be \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classes**} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Testing Set**} \\ \cline{2-5}  & **Total Cases** & **Correctly Classified** & **Total Cases** & **Correctly Classified** \\ \hline Islanding & 44 & 44(100\%) & 18 & 17 (94.4\%) \\ \hline Non-Islanding & 44 & 44(100\%) & 19 & 18(94.7\%) \\ \hline Total & 88 & 88(100\%) & 37 & 35 (94.5\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of the model. Figure 7: Accuracy comparison between auto-grounding and data-mining-decision-tree algorithm methods. done within 1.706 s. Moreover, the use of the data mining decision tree algorithm based on a machine learning approach successfully identified the anti-islanding condition with high accuracy and reliability within 0.162309 s. The comparison between auto-islanding and the machine learning technique shows that using the data mining decision tree technique presents the fastest operation of the anti-islanding condition. Furthermore, the data mining decision tree approach was also found to be more reliable and accurate than the auto-grounding method. An auto-grounding method, which is a type of remote method for islanding detection, and a data mining-decision tree method, which is a type of machine learning methods for islanding detection, were developed, simulated and evaluated. Auto-grounding methods used four passive methods for islanding detection: over/under voltage, over/under frequency, rate of change of power and rate of change of frequency. Forty-two individual events were used to simulate the islanding detection using the auto-grounding method, out of which 21 events were in islanding state and 21 events were in non-islanding conditions. Similarly, 37 events were used in the data mining-decision tree method, where 18 events were in islanding states and 19 events were in non-islanding states. The proposed islanding detection methods were simulated in the computer and the following conclusions were derived:  
* The data mining-decision tree method has a higher overall accuracy (94.5%) when compared to the auto-grounding method (92.8%). Thus, comparing between the two, the data mining-decision tree method is the more reliable method of islanding detection. * In comparison between the two discussed methods, the data mining-decision tree method presents the fastest and more accurate operation of anti-islanding condition. Auto-grounding being the older and the data mining-decision tree method being the comparatively newer method of islanding detection, these simulations highlight increased accuracy and speed of the newer islanding detection method when compared to the older one. The power grid system's complexity is growing on a yearly basis. The introduction of DGs and increased power capacity of the power grid has led to the requirement of a faster protection system. Two seconds is not a fast-enough islanding detection speed in this scenario. As such, micro-grids and smart-grids are relying more on newer and faster islanding detection methods. In successful simulations of the data mining-decision tree method, the research has shown, with positive results, a newer form of islanding detection method with short detection speed and high accuracy. This method promises to satisfy the meticulous islanding detection criteria of the future grids comprised of micro-grids and smart grids. To further the findings of this paper, the following explorations can be conducted:  
* The detections of islanding conditions were done in 1.705 s and 0.162 s by auto-grounding and data mining-decision tree methods.