# Novel Faulted-Section Location Method for Active Distribution Networks of New-Type Power Systems
## 5 Simulation Analysis
### Algorithm Comparison  
The four algorithms of the immune algorithm (IA), improved quantum genetic algorithm (IQGA), improved genetic algorithm (IGA), and GTA are compared and analyzed from four aspects: algorithm complexity, average positioning accuracy, average convergence algebra, and average execution time, to test the performance of the GTA.  
The calculation complexity of the four algorithms is analyzed. Since this paper uses the fault region location and the improved matrix algorithm to perform two dimensionality-reduction operations, the fault hypothesis contains up to \(2^{15}\) cases or about thirty-thousand cases when performing the GTA. When assuming IA, IQGA, and IGA, calculations are performed in the smallest range of fault regions, and the fault hypothesis contains at least \(2^{22}\) or about four million cases. Moreover, the computational gap between the algorithms increases exponentially with the expansion of the active distribution network model. Therefore, from the perspective of calculation, the GTA is the simplest.  
The conditions of single faults, multiple faults, and faults with fault information errors are simulated. The results of the algorithms are shown in Tables 12-14.  
The average positioning accuracy of the GTA is the highest, and it can still be maintained above 94% in the most complex fault conditions. Under the cooperation of the GA and the TA, the GTA has a fast convergence speed and strong search ability. The GTA can iterate to the optimal value within 20 generations, and the average execution time is kept within 2 s. IQGA converges to the optimal solution of about 25 generations in the case of simple faults and about 35 generations in the case of complex faults, and the convergence speed is significantly slower. IQGA has good accuracy and execution time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fault Sections** & \begin{tabular}{c} **Error Fault** \\ **Information** \\ \end{tabular} & \
\begin{tabular}{c} **Suspicious Fault** \\ **Sections** \\ \end{tabular} &
\begin{tabular}{c} **Genetic Tabu** \\ **Algorithm** \\ \end{tabular} & **Fitness Function** & **Correct or Not** \\ \hline \(L_{8}\) & \(l_{16}\): \(-1\)\(\rightarrow\)\(1\) & \(L_{4}\), \(L_{8}\), \(L_{9}\), \(L_{13}\), \(L_{16}\), \(L_{17}\), & \(L_{8}\) & 2 & correct \\  & & \(L_{20}\), \(L_{  
Since the antibody and antigen in IA are encoded as binary codes, the intimacy calculation between each qubit in each population generation should be realized, which seriously affects the convergence speed. The simulation results show that the IA has slow convergence speed, low positioning accuracy, and long execution time. The iterative results of IGA are random, the iterative process is unstable, and it may not converge to the optimal value correctly. The positioning accuracy is the lowest, and the convergence speed is slow. In summary, compared with other algorithms, the GTA has simpler calculation, higher location accuracy, faster convergence speed, and shorter execution time, which is more suitable for fault section location of active distribution networks.