Temperature Error Compensation Method for FBG Sensor in Electric Power System Based on Fireworks Algorithm

Guo, Yan; Kong, Weiheng; Wang, Lihui

doi:10.1109/ic2ecs57645.2022.10088047

Cited by 1 publication

(1 citation statement)

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“…To address this issue, optimization algorithms are utilized to optimize the weights and thresholds of ELM, thereby reducing prediction error values, improving network performance, and enhancing the ELM prediction accuracy. In recent years, the field of metaheuristic algorithms has seen rapid development, with examples including particle swarm optimization (PSO) [ 20 ], the sparrow search algorithm (SSA) [ 21 ], the whale optimization algorithm (WOA) [ 22 ], the fireworks algorithm (FWA) [ 23 ], and the gray wolf optimization (GWO) [ 24 , 25 ], among others. GWO offers a faster optimization process compared to other algorithms, as it first derives the solutions and then ranks and compares them to obtain the best solution.…”

Section: Introductionmentioning

confidence: 99%

Temperature Compensation of Wind Tunnel Balance Signal Detection System Based on IGWO-ELM

Dong,

Xu,

Cao

et al. 2023

Sensors

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The wind tunnel balance signal detection system is widely employed in aerospace applications for the accurate and automated measurement of aerodynamic forces and moments. However, measurement errors arise under different environmental temperature. This paper addresses the issue of measurement accuracy under different temperature conditions by proposing a temperature compensation method based on an improved gray wolf optimization (IGWO) algorithm and optimized extreme learning machine (ELM). The IGWO algorithm is enhanced by improving the initial population position, convergence factor, and iteration weights of the gray wolf optimization algorithm. Subsequently, the IGWO algorithm is employed to determine the optimal network parameters for the ELM. The calibration decoupling experiment and high-low temperature experiment are designed and carried out. On this basis, ELM, GWO-ELM, PSO-ELM, GWO-RBFNN and IGWO-ELM are used for temperature compensation experiments. The experimental results show that IGWO-ELM has a good temperature compensation effect, reducing the measurement error from 20%FS to within 0.04%FS. Consequently, the accuracy and stability of the wind tunnel balance signal detection system under different temperature environments are enhanced.

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