提升数据处理能力是实现光纤电流传感器（FOCS）在微弱电流检测领域中应用的重要支撑。针对独立成分分析（ICA）算法对信源数量的要求和变分模态分解（VMD）对冲击噪声处理能力不足的问题，采用优化参数的变分模态分解与独立成分分析联合算法（OVMD-ICA算法），提升微弱电流检测能力。首先，在分析全光纤电流传感器输出信号的特征和噪声特性的基础上，以能量谱熵为目标函数，采用捕食者算法（HPO算法）获取模态参数K和二次惩罚因子α，完成变分模态分解。然后，通过设置相关系数阈值，对各模态函数分类并构建虚拟通道，以满足ICA对信源数量的要求，并采用FastICA算法实现盲源分离。最后，通过对比实验确定了该方法的有效性，发现采用所提方法能够实现3 mA微弱电流的识别检测。

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.

The fiber current sensor based on the Faraday effect and Ampère'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 μrad/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.

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 α 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.

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.