Intelligent compensation method for measurement errors in optical fiber current sensor caused by temperature...

Temperature variations significantly degrade the measurement accuracy of fiber optic current sensors (FOCS) in critical power systems applications such as high-voltage transmission and renewable energy integration. To address this, we propose an intelligent error compensation method based on an improved Quantum-behaved Particle Swarm Optimization-Neural Network (Levy-Weighted-QPSO-NN) algorithm. The approach leverages easily measurable state parameters-sensing ring temperature, received optical power, half-wave voltage, SLD temperature, and SLD current-as inputs to predict temperature-induced current ratio difference. Experimental validation involved three sensing rings subjected to temperature cycling (-45 °C to 70 °C), emulating harsh substation environments. The Levy-Weighted-QPSO-NN model achieved 91.11% average prediction accuracy for ratio difference with a correlation coefficient (R²) of 0.9223, outperforming QPSO-NN (85.69%) and Weighted-QPSO-NN (88.31%). Key metrics (MAE: 0.0784; RMSE: 0.0819) confirmed superior stability and accuracy. Robustness testing demonstrated consistent performance across varying population sizes (25-70) and iterations (90-150). Using predicted ratio differences for real-time compensation reduced measurement errors from 0.82% to 0.13%, meeting IEC 61869-6/8 and GB/T standards for Class 0.2S accuracy. This method eliminates reliance on complex hardware modifications, offering a generic, algorithm-driven solution for temperature-dependent FOCS errors.