Deterioration identification method of key optical devices of fiber optical current transformer based on neural network

Optical fiber current transformers (FOCTs) are affected by various external factors, resulting in the deterioration or even failure of devices, causing changes in critical state quantities, and reducing the accuracy and reliability of products. To solve this problem, based on the neural network algorithm, this paper starts from the four deterioration characteristics of FOCT SLD junction temperature, SLD output optical power, phase modulator half-wave voltage, and optical fiber sensing ring temperature, and identifies the deterioration of key optical components of FOCT, which provides a basic model and data support for the online monitoring and early warning to improving the stability and reliability of FOCT in long-term operation.