An insulation board is an important insulator used between damping capacitors in a converter valve. The potential creeping discharge phenomenon on the insulation board will affect the insulation between the damping ca...
详细信息
An insulation board is an important insulator used between damping capacitors in a converter valve. The potential creeping discharge phenomenon on the insulation board will affect the insulation between the damping capacitors and even the safe operation of the converter valve;therefore, a modified chaos particle swarm optimization multiple signal classification (MCSPO-MUSIC) localization algorithm based on a sparsearray was proposed. The performance of the localization algorithm and the sparsearray was analyzed by MATLAB simulation, and a test platform was established to detect the insulation board discharge position localization. The simulation results showed that the calculation time of this algorithm is about 1.5 s, which is an order of magnitude less than traditional MUSIC algorithm, and it is found that when the sparsity of the 4 x 4 array is 4 (the sparse array elements are 5, 9, 14 and 15), the localization accuracy remains high. Ten groups of experimental data were put into the MCSPO-MUSIC algorithm;the root mean square errors (RMSE) of the localization errors are 1.91 degrees (non-sparsearray) and 3.12 degrees (sparsearray), respectively. Finally, the blind source separation algorithm was used to remove the field noise, which verifies the algorithm and sparse concept accurate and economical in practical application.
In this study, a 2D adaptive beamforming algorithm for sparsearray is proposed. Firstly, a signal model based on matrix completion theory for adaptive beamforming in sparsearray is established, which is proved to sa...
详细信息
In this study, a 2D adaptive beamforming algorithm for sparsearray is proposed. Firstly, a signal model based on matrix completion theory for adaptive beamforming in sparsearray is established, which is proved to satisfy null space property. Secondly, in order to enhance the performance of reconstructing complete received signal matrix, genetic algorithm is used to optimise the sparse sampling array. Thirdly, the accelerated proximal gradient algorithm is adopted to reconstruct the complete received signal matrix. Finally, the adaptive beamforming weight is provided directly to form beam patterns, which can be obtained as a result of reconstructing complete received signal matrix. The proposed method could improve the utilisation rate of the sparse array elements and reduce the computational complexity in interference suppression. Simulation results show the effectiveness of the method.
暂无评论