Fault Location of Distribution Network Based on the Quantum Ant Colony Algorithm

Abstract

With the continuous expansion of the distribution network and the access of distributed generation, the problem of distribution networks with distributed generation (DG) fault location has become complex and difficult to solve. In order to locate faults accurately and quickly, in this paper, the quantum ant algorithm is applied to solve this problem for the first time. Firstly, we establish a reasonable mathematical model. To greatly avoid the adverse effects of information loss, we propose an information self-correction method. Secondly, a hierarchical positioning model is proposed to shorten the positioning time. Then considering the requirements of quickness and accuracy for fault location, the algorithm is improved. We update the quantum rotation gate mechanism and dynamically adjust the rotation angle. In addition, we add the mutation mechanism to expand the population diversity. Finally, the effectiveness and performance of the improved quantum ant colony algorithm are reflected by comparing with other algorithms. Simulation results demonstrate that the improved quantum ant colony algorithm has substantial global optimization ability, high fault location accuracy and fault tolerance in different cases, and the hierarchical positioning model can shorten the algorithm location time.

Active Distribution Network Fault Diagnosis Based on Improved Northern Goshawk Search Algorithm

“…The switching function can realize the conversion of line fault state and switch fault information. Considering the switching of distributed power supply, the switching function can be defined as follows [19]:…”

“…The smaller the sum of the difference, the higher the similarity between the solved fault situation and the actual fault situation. The objective function was defined by Equation (3) [19].…”

“…Nevertheless, the convergence and stability of the algorithm need to be further considered since they determine whether the method could be effectively applied in ADNs. The authors of [19] apply an improved algorithm based on the quantum ant colony algorithm to solve the distribution network fault location problem; the authors of [20] verify that introducing the improved sine-cosine algorithm into the local development stage of the algorithm increases the population diversity at the late iteration stage, prevents the algorithm from falling into a local optimum, and effectively improves the algorithm's solution accuracy and convergence speed; the authors of [21] propose an improved differential evolution algorithm, self-adaptive differential evolution with Gaussian-Cauchy mutation (SDEGCM), which introduces two strategies, Gaussian-Cauchy mutation and parameter self-adaptation, to improve the performance of the algorithm; the authors of [22] employ a Hunger Games search algorithm based on Gaussian-Cauchy variants, however, the selection and adjustment of the parameters of the above four algorithms have a large impact on the results, and sufficient parameter optimization and testing are needed for specific problems.…”