Analysis and compensation method for nonlinear temperature error of fiber optic current transformer considering the influence of conductor current-carrying capacity

As the main equipment for current monitoring in ultra-high voltage transmission systems, the measurement accuracy of the fiber optic current transformer (FOCT) directly affects the process of power grid digitization and intelligence. However, FOCT is significantly affected by temperature perturbations in actual operation, especially under the influence of a non-uniform temperature field triggered by multi-source factors such as conductor heating and environmental temperature, which causes the optical fiber linear birefringence to exhibit non-uniform distribution characteristics and consequently leads to excessive measurement deviation. Therefore, this paper reveals the correlation mechanism between the temperature gradient distribution driven by the conductor current-carrying capacity and the nonlinear error by establishing a mathematical model of the non-uniform temperature characteristics of FOCT. On this basis, an improved PSO-BP nonlinear temperature error compensation method (CTMC-PSO-BP), considering the Current-Thermal Multiphysics Coupling (CTMC), is proposed, which innovatively takes the conductor current-carrying capacity as an input parameter of the error compensation model. The results show that adding the conductor current-carrying capacity as an input parameter to the network can effectively improve the compensation effect of the model. The CTMC-PSO-BP neural network algorithm proposed in this paper demonstrates outstanding performance in the nonlinear temperature error compensation of FOCT, capable of reducing the measurement error of FOCT within the temperature range of 5–65 °C from ±0.4505% to within ±0.0026%, thereby substantially improving the performance of FOCT under the influence of complex temperature fields.