# Novel Faulted-Section Location Method for Active Distribution Networks of New-Type Power Systems
###### Abstract  
This paper puts forward a fault location method combining the improved matrix algorithm and the genetic tabu algorithm based on multi-source information in view of the limitation of existing fault location methods in active distribution networks, such as long-time consumption, low accuracy, and low tolerance performance. Firstly, the fault region location method is proposed to simplify the active distribution network model and reduce the matrix calculation dimension. Secondly, the improved matrix algorithm is proposed to locate the fault section, and a positioning result verification method is proposed to improve the tolerance performance. Finally, the genetic tabu algorithm is proposed to optimize suspicious fault sections when the verification is incorrect and obtain the positioning result. The simulation results show that the fault location method proposed in this paper locates faster and performs better in both accuracy and tolerance in different fault conditions than others. active distribution networks; fault location; improved matrix algorithm; genetic tabu algorithm 2020 IEEEexample:BSTcontrol of Integrated Circuits and Networks 2020 The principle of the matrix algorithm is to obtain the fault judgment matrix by matrix calculation to locate the fault sections [13]. Reference [14] adopts a specific correction method for different location results. However, this correction method can only adapt to one-third of cases of the error fault information. In Reference [15], a method combining a matrix algorithm and an optimization algorithm is proposed. However, the \"minimum set\" cannot contain all suspected fault sections. Reference [16] proposes a fault location matrix algorithm based on voltage magnitude comparison, determining the fault sections by comparing the voltage magnitudes at both ends of the suspected fault sections. Based on the \"minimum set\" theory, the intelligent algorithm calculates the fault sections that best explain the fault information by solving the fitness function [17]. Reference [18] proposes to evaluate population individuals by calculating antibody affinity. However, the evaluation has to be realized between individuals in each generation, which seriously affects the convergence speed. Reference [19] proposes adaptive crossover and mutation probabilities, adjusted nonlinearly according to the Sigmoid function. However, the parameter setting of this method is complicated. Reference [20] is applied to fault location in the internet of things (IoT) power distribution. The improved unmanned aerial vehicle (UAV) model and particle swarm optimization algorithm are used for fault location, improving the operational efficiency and adaptability of the algorithm. In summary, the following limitations exist in the research of active distribution network fault location methods: (1) With the increase in distribution network scale and the access of a large number of DGs, the existing fault location methods will find it hard to meet the requirements of fault location accuracy and rapidity. (2) The intelligent algorithm generally has the limitations of premature convergence, making it difficult to search for the correct fault sections. (3) When multiple elements of fault information are incorrect, fault location methods have the problem of balancing positioning speed and accuracy. In response to the above shortcomings, this paper advances a fault section location method of the active distribution network based on multi-source information. The main contents of this paper are as follows:  
1. The fault region location algorithm is proposed to ensure that the fault region contains all possible faulted sections, simplifying the fault system and reducing the matrix calculation dimension. 2. An improved matrix algorithm using a non-square matrix form of computation is proposed to adapt to the access of multiple DGs. The algorithm can accurately locate single and multiple faults in non-T-connected sections. 3.