OVMD-ICA算法用于光纤电流传感器降噪

Objective The fiber current sensor based on the Faraday effect and Ampere's circuital law can measure the current accurately. It has many advantages, such as excellent insulation characteristics, simultaneous measurement of the alternating current (AC) and direct current (DC), flexible sensor diameter, and digital output. However, it can hardly measure the microcurrent because the magnetic field generated by the weak current is small, and the Verdet constant of the sensing fiber is tiny (about 1 mu rad/ A when the wavelength is 1300 nm). Therefore, the current resolution of the fiber current sensor is limited. The methods to improve the current resolution mainly include the following: improving the optical path structure, increasing the number of optical fiber loop turns, and improving the Verdet constant of the sensing fiber. However, these methods have the disadvantages of complex operations and high costs. The data processing method is a promising scheme to improve the current resolution. To meet the requirements of information sources for independent component analysis (ICA) and improve the performance of variational mode decomposition (VMD) to deal with impact noise, this paper proposes the co-clustering algorithms of ICA and VMD with the parameters optimized by the hunter- prey optimization ( HPO) algorithm. Methods This paper proposes the co-clustering algorithms of ICA and VMD with the parameters optimized by the HPO algorithm. Firstly, the random Gaussian noise, shot noise, impact noise, and output signal are measured. The output signal and noise characteristics of the fiber current sensor are analyzed. Secondly, the key parameters of VMD are optimized by the HPO algorithm. With the energy spectrum entropy function as the fitness function, the modal parameter K and the quadratic penalty factor a are obtained by the HPO algorithm, and VMD is realized with the two parameters. Third, the virtual channels of ICA are constructed. The mode functions are classified by the setting of the threshold of the correlation coefficient to construct the virtual channels for ICA. In this way, the application conditions of ICA are satisfied. Finally, the FastICA algorithm is applied for denoising. Results and Discussions Various optimization algorithms are compared and analyzed. When the energy spectrum entropy function is taken as the fitness function, the particle swarm optimization (PSO) algorithm has the best performance, but its time cost is too high. The grey wolf optimization ( GWO) and HPO algorithms are the second best, and the HPO algorithm is better when the time cost and the iterations are taken into account. In this case, the HPO algorithm is better than the other optimization algorithms, as shown in Table 2. In addition, the main data processing methods are compared and discussed. When the signal-to- noise ratio (SNR), mean square error (MSE), and correlation coefficient are taken as the evaluation criterions, the OVMD- ICA has the highest SNR, the minimum MSE, and the largest correlation coefficient. The SNR should be greater than 30 dB according to the applicable standard of the electronic current transformer. The Wavelet (sym10), VMD-wavelet, and OVMD- ICA can suffice for the requirement, as shown in Table 3. The OVMD-ICA can achieve the optimal noise reduction effect, and the current resolution is 3 mA. Conclusions More outstanding performance can be achieved in terms of the operation time, required iterations, and search for the globally optimal solution when the parameters of VMD are optimized by the HPO algorithm. The mode functions are classified by the setting of the threshold of the correlation coefficient to construct the virtual channels for ICA, and the FastICA algorithm is applied for denoising. The SNR of the output signal is enhanced, and the MSE is reduced by OVMD- ICA. By this algorithm, the SNR can be improved by at least 5 dB, and the resolution and measurement of 3 mA weak current can be realized.