# Novel Faulted-Section Location Method for Active Distribution Networks of New-Type Power Systems
## 4 Genetic Tabu Algorithm
### Genetic Tabu Algorithm
#### 4.5.1 Genetic Tabu Algorithm Overall Design  
A genetic tabu algorithm suitable for fault location of the large-scale active distribution network is proposed by combining the high search efficiency of the GA and the high search accuracy of TS to complete the optimization calculation together so that the algorithm can improve the accuracy based on ensuring the calculation speed and meet the requirements of power supply reliability of distribution network.  
Firstly, according to the practical problem of fault location in the active distribution network, the genetic coding rules of the GA are designed. The suspicious fault section is encoded as a one-dimensional matrix. The value of each element in the matrix represents whether the section is faulty, where \"1\" represents that the section is faulty, and \"0\" represents that the section is not faulty. The individual in the GA is represented as such a binary gene string. In this design, once the fault section is detected, it will cause a rapid decrease in the fitness function's value. The GA can retain this dominant gene, thus avoiding the repeated search of the algorithm, which greatly improves the optimization efficiency. At the same time, individuals can accurately reflect the fault situation.  
Secondly, the natural selection rules are designed according to the idea of TS. In natural selection, the top 50% of fitness of the current population individuals are selected as the parent generation. Then, one individual gene is selected for the mutation to generate a neighborhood. The mutated individuals are also added to the parent generation to obtain the parent population of the same size. This natural selection rule corresponds to the actual fault location. The fault situation represented by individual neighborhoods is close to the current fault situation. In addition, to ensure the running speed of the algorithm, several individuals with the highest fitness in the child generation are updated into the tabu table after natural selection to prevent the algorithm from repeatedly searching and falling into the local optimum.  
Then, the initial population is generated by the greedy algorithm. Considering that the probability of single faults is higher than that of multiple faults, the initial population preferentially generates all individuals of single faults. Then, it fills the initial population with a random method. On the one hand, it can quickly screen out the dominant individuals with smaller fitness function values in the initial population. On the other hand, it can also avoid the phenomenon that the algorithm converges too late or does not converge, thus accelerating the convergence speed of the algorithm.  
Finally, according to the designed natural selection rules and genetic algorithm, the iteration is carried out to attain the fault section location result.  
The GTA can accurately locate various complex fault conditions. Section 5 uses the IEEE-34 node system to simulate and verify the algorithm's performance. However, simulation experiments need to be carried out in larger-scale systems to make the algorithm more mature and adapt to the operation mode of large-scale systems.