Fault location in distribution network by solving the optimization problem using genetic algorithm based on the calculating voltage changes.

Due to the extent and existence of multiple branches in the distribution network, one of the main challenges in this network will be the issue of fault location. Gradually, with the expansion of smart grids and the use of smart meters in the distribution network, various methods are proposed to use these devices to the fault location. In this paper, fault location in the distribution network is defined as an optimization problem with an objective function based on network voltage changes before and after the fault. The objective function of the problem consists of the difference between the two approaches. In the first approach, the combination of the power flow (PF) method with smart meter data is used to calculate voltage changes before and after the fault in each network node. In the second approach, by calculating the network impedance matrix per the hypothetical fault location in each node of the network, these voltage changes are calculated, and by comparing the results of these two approaches, when the objective function of the problem is minimized, the optimal answer will be the fault location in the network. Therefore, in this issue, two objective functions of the difference between these two approaches are defined, one to identify the faulty section and the other to fault location has been used. The required data are extracted from the network partly before the fault and partly after the fault. Due to the need to process these data, the volume of calculations and the wide distribution network, a genetic algorithm (GA) has been used to solve the problem. To evaluate the speed and accuracy of the proposed method in fault location, the proposed method has been implemented on the largest distribution network, namely the IEEE 123-node distribution test system through MATLAB software. The results showed that the proposed method had a good performance for types of faults and different fault resistance so that it was able to detect the location of the fault with an accuracy of 1.501% in less than 60 s. [ABSTRACT FROM AUTHOR]