GWO-elman based composite detection of transformer oil flow temperature

This paper addresses the issue of nonlinear coupling error caused by cross-sensitivity of strain and temperature in the composite detection of flow rate and temperature in transformer oil using a Fiber Bragg Grating (FBG) sensor. This paper focuses on the FBG sensor as the research object, elucidating the underlying principles of composite detection. Subsequently, a composite detection experimental platform is established for the purpose of analyzing the coupling error of flow rate and temperature. Ultimately, a nonlinear decoupling algorithm based on the Grey Wolf Optimizer (GWO) is proposed to enhance the nonlinear decoupling algorithm of the Elman neural network (simple recurrent neural network, Elman). The findings demonstrate that within the flow rate range of 0–5 m/s and the temperature range of 30 °C–150 °C, the maximum error is reduced by 72.0 % and 81.3 %, and the average error is reduced by 74.4 % and 79.4 %. This significantly enhances the precision and reliability of the sensor’s detection capabilities.