Abstract:

The decomposition components inside SF₆ electrical equipment can be detected by tunable absorption spectroscopy technique, in which the concentration of CO₂ reflects the insulation defect situation inside the equipment. Therefore, potential insulation faults of the equipment can be found in time by measuring the CO₂ concentration accurately. To overcome the problem of poor stability of traditional least squares concentration inversion model, ISFO-KELM gas concentration inversion model based on ISFO (Improved Sailed Fish Optimizer) and KELM (Kernel Based Extreme Learning Machine) is established in this study. The optimization ability and the ability to jump out of local optimal solution of ISFO are improved by using multi-strategy initialization method, Levy random step length, Cauchy mutation and adaptive t-distribution mutation techniques. The experimental results show that this model has high accuracy and robustness, and is superior to traditional methods such as least squares method, extreme learning machine, BP (Back Propagation) neural network in stability and generalization ability, which has important significance for evaluating the operation state of SF₆ electrical equipment.

Key words: tunable diode laser absorption spectroscopy, SF₆ electrical equipment, CO₂ concentration inversion, denoising, curve fitting, variational mode decomposition, kernel based extreme learning machine, sailfish optimizer