Fault Location Approach for Distribution Network with Dynamic Environment

Abstract

This paper proposes a continuous tracing framework for fault location in distribution network, which is divided into two stages: parameter correction and fault location. On the stage of parameter correction, it is capable of correcting line parameters based on data in the steady state. On the stage of fault location, it can locate the fault according to the one cycle signal after it occurs based on the corrected parameters in the former stage. The stage of parameter correction is beneficial to the fault location process. The core algorithm of the framework is parameter adaptive group search optimizer (PAGSO), which is a kind of improved metaheuristic optimization algorithm. The adjustment strategy and regulatory mechanism are introduced in exploitation, whereas the adaptive mechanism based on reinforcement learning (RL) is introduced in exploration. Improvements could enhance search capability and help the algorithm converge faster. The proposed framework is tested in IEEE 34-bus model. Other meta-heuristic algorithms are adopted for comparison, including genetic algorithms (GA), particle swarm optimization (PSO), grey wolf optimization (GWO), and the standard group search optimizer (GSO). For fault location, PAGSO has been verified under three cases, including ideal condition, noisy condition, and the system with renewable penetration. For line parameters, experiments are designed to prove the algorithm feasibility and verify the necessity of the correction.