A subsystem-based fault location method in distribution grids by sparse measurement

With the development of smart meters and other intelligent electronic devices, more and more data-driven fault location methods based on wide area measurement are emerging. However, these diagnostic methods for dealing with the whole tested system often appear complex. This paper presents an innovative subsystems-based fault location strategy in distribution grid by the sparsity promoted Bayesian learning algorithm. To avoid taking measurement for the whole distribution system, the fault-included subsystem is selected according to the distribution characteristics of negative sequence voltage. Then the data for fault location is measured by allocating meters in subsystem, which can reduce the number of required meters. For accurately estimating the fault location, a sparse prior is proposed for the Bayesian learning, which could improve the accuracy of the fault location algorithm by about 4%. The performance is tested on a 12.66-kV, 69-bus distribution system in response to various fault scenarios. The results show that the accuracy of the proposed method for the fault section location can reach 90%. It also verifies the robustness and accuracy for fault line location, faced different fault types, fault resistance, noise, etc. An innovative subsystems-based fault location strategy in distribution grid by the sparsity promoted Bayesian learning algorithm was proposed. This method has advantages of optimizing meter configuration, reducing computational complexity, and improving accuracy in fault location.image

Automated summary

This paper presents an innovative subsystems-based fault location strategy in distribution grid using the sparsity promoted Bayesian learning algorithm. By selecting the fault-included subsystem for measurement, the number of required meters is reduced, and the accuracy of the fault location algorithm is improved.