Automatic Calibration Method for Nonlinear Measurement Errors of Current Transformers Based on Quantum Particle Swarm Optimization Algorithm

Due to various factors, current transformers often have nonlinear measurement errors in practical applications. To effectively solve this problem, this paper proposes an automatic calibration method for nonlinear measurement errors of current transformers based on a quantum particle swarm optimization algorithm. This method first analyzes the causes of nonlinear measurement errors, calculates the nonlinear measurement errors of current transformers, and uses the global search ability and optimization characteristics of the quantum particle swarm optimization algorithm to model and analyze the nonlinear measurement errors of current transformers. In quantum space, the position of particles is no longer described by traditional coordinates and velocity vectors but is represented by wave functions, enabling algorithms to more accurately describe and simulate the nonlinear characteristics of current transformers. A fitness function is set and parameters are automatically adjusted to minimize errors. During the search process, the algorithm utilizes the individual and group extremum update mechanisms of particle swarm optimization to enable particles to quickly converge to the optimal solution. The experimental results show that the average nonlinear measurement error of this method is 0.9 A, which can effectively reduce the measurement error of current transformers and improve the measurement accuracy of power systems.