Fault Location Method for DC Distribution Network Based on Particle Swarm Optimization

With the development of new power system construction, distribution network as an important part, its safe and stable operation is more important. In order to make the distribution network have self-identification, self-positioning and self-healing functions, it is necessary to study the decentralized autonomous technology of the distribution network, including the state assessment and fault location technology of distributed power supply. In this paper, the autonomous intelligent agent unit (IA) is designed based on digital twin technology, which has the functions of state assessment, fault location and isolation. Through IEEE33-node with distributed power supply and MA-PSO algorithm, the internal autonomy of IA is simulated and analyzed. The results show that the proposed scheme and algorithm are fast, accurate and referable.

Section: Algorithm Theorymentioning

confidence: 99%

Research on decentralized autonomous fault processing of distribution network based on data twin Technology

yang

Bai

Huo

et al. 2022

“…The disadvantage of this method is that it may be affected by the changes in the structure and parameters of the power system. In addition, other methods, including the transient characteristics method [9], signal injection method [10], and so on, have been proposed successively.…”

Section: Introductionmentioning

confidence: 99%

A New Fault Distance Measurement Scheme for DC Distribution Network

Li,

Wang,

Han

et al. 2023

To solve the problem of fault location in the DC distribution network, this paper proposes a new fault distance measurement scheme based on the impedance measurement algorithm against fault transition resistance and the improved Moore voting algorithm. Firstly, signals of different frequencies are injected into the DC circuit in different periods, after the converter is blocked due to the DC side fault. Then, through the distance measurement algorithm against fault transition resistance, several groups of fault distance data are obtained. The improved Moore voting algorithm is used to process these fault distance values so that the accurate fault distance values are determined. Finally, simulation tests verify the reliability and accuracy of the proposed scheme. The proposed scheme requires a low sampling frequency, and it can not be interfered with by the fault transition resistance and occasional large errors. These advantages help to improve the efficiency and accuracy of troubleshooting.

“…At present, there are many types of research on this method of fault location, including matrix algorithms and various swarm intelligence algorithms. The reference [2] uses a particle swarm optimization algorithm to locate the fault section, which has poor local search ability and search accuracy, Literature [3] uses a binary cuckoo algorithm to locate the fault zone of a distribution network with distributed generators, which has the advantage that it is not easy to fall into local optimization, but its convergence speed is relatively slow. Literature [4] proposes a linear integer programming method with high fault tolerance, It has certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Fault location method based on Beetle swarm algorithm

Zhang

2022

With the increasing complexity of the distribution network, the requirements for fault location are becoming higher and higher. In this paper, the Beetle swarm algorithm, which combines the particle swarm optimization algorithm with the Beetle whisker algorithm, is introduced to locate the fault of the multi-source power system with distributed generators through the python simulation ieee33 node example. Compared with the single particle swarm optimization algorithm, the Beetle swarm algorithm has the characteristics of high stability and high fault tolerance.