Abstract
DC distribution network faults seriously affect the reliability of system power supply. Therefore, this paper proposes a fault recovery reconfiguration strategy for DC distribution networks, based on hybrid particle swarm optimization. The original particle swarm algorithm is improved by simplifying the distribution network structure, introducing Lévy Flight, and designing an adaptive coding strategy. First, the distribution network structure is equivalently simplified to reduce the problem dimensionality. Further, the generated branch groups are ensured to satisfy the radial constraints based on the adaptive solution strategy. Subsequently, Lévy flight is introduced to achieve intra-group optimality search for each branch group. The method is simulated in several distribution systems and analyzed in comparison with the particle swarm algorithm, genetic algorithm, and cuckoo algorithm. Finally, the results validate the accuracy and efficiency of the proposed method.
Highlights
With the increasing demand for electricity, the size and complexity of modern distribution networks have increased significantly [1]
Compared with the traditional AC distribution network, the flexible DC distribution network based on voltage source converter (VSC) technology has the advantages of easy access to distributed energy and multiple loads, and flexible control [6]
The constraints that should be satisfied for fault recovery reconfiguration mainly include the tidal current constraint of the distribution network, the upper and lower voltage constraints of the nodes, the upper and lower branch current constraints, and the topological constraints of the distribution network structure: obj : min f kj Ij2rj j=1 s.t
Summary
With the increasing demand for electricity, the size and complexity of modern distribution networks have increased significantly [1]. Inspired by the fractal theory for solving optimization problems, implementation of a stochastic fractal search (SFS) algorithm is proposed to solve the distribution network reconstruction problem [11]. Ref [13] uses the discrete network reconfiguration of the data set method, which can significantly improve the effectiveness of the distribution network This type of method has good convergence performance, and can obtain a global optimal solution for single-objective optimization problems. AI algorithms for solving reconstruction problems are mainly based on swarm optimization methods. The reconstruction method based on the stochastic algorithm can solve the optimal solution more efficiently when solving large-scale distribution network problems.
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