Fault section diagnosis of power systems with logical operation binary gaining‐sharing knowledge‐based algorithm
Mathematics & Actuarial Science Department
Fault section diagnosis (FSD) is a critical part of the power system dispatching and control. To diagnose the faulty section(s) correctly, an improved binary variant of gaining-sharing knowledge-based algorithm (GSK) named LOBGSK is presented in this paper. It stretches the original GSK over binary search space so as to solve the 0-1 integer programming FSD problem. In LOBGSK, individuals are encoded by binary numbers and logical operations instead of real arithmetic operations are designed to update the individuals. By this, LOBGSK can avoid transcoding in solving the FSD problem. To validate the effectiveness of LOBGSK, it is first applied to a 4-substation test system considering various fault scenarios. Then it is further implemented to the IEEE 118-bus system and an actual fault event occurred in a practical power grid in Jilin province of China. In addition, the influence of three key parameters of LOBGSK is also investigated. Simulation results show that LOBGSK is robust against its key parameters and can offer a 100% successful rate to diagnose different faults quickly, which is demonstrated by the reported results of some published FSD methods. Furthermore, it outperforms seven state-of-the-art metaheuristic algorithms and the original GSK in solving the FSD problem of power systems.
(2021). Fault section diagnosis of power systems with logical operation binary gaining‐sharing knowledge‐based algorithm. 1057–1080.
Xiong, Guojiang, et al.
"Fault section diagnosis of power systems with logical operation binary gaining‐sharing knowledge‐based algorithm." 2021, pp. 1057–1080.