基于自适应VMD和A-SVD的MOA在线监测方法

阻性电流是反映金属氧化物避雷器(MOA)绝缘状态的关键参数。电力系统正常运行时的MOA阻性电流非常小，阻性电流实测有效值数据易受到高频噪声、白噪声、随机脉冲等干扰影响，造成报警装置误报警。然而，现有方法难以有效消除上述干扰对阻性电流的影响，因此提出了一种基于自适应变分模态分解 (adaptive variational mode decomposition，A-VMD)和自适应奇异值分解(adaptive singular value decomposition，A-SVD)的MOA在线监测方法。首先，通过顺序递增二次惩罚因子和分解层数值，以能量和损失指标衡量VMD分解的效果，寻找分解层数和二次惩罚因子的最优参数值。然后，采用A-SVD消除经A-VMD初步去噪后阻性电流中少量残留白噪声，为监测MOA绝缘状态提供可靠依据。仿真试验和实测数据均证明了该方法的有效性，处理结果满足实际工程的需求。

A New Online Monitoring Method for MOA Based on A-VMD and A-SVD

Resistive current is a key parameter to judge the working state of a metal oxide arrester (MOA). The resistive current of a MOA is very small when the power system is working normally, and the measured resistive current is prone to be affected by the interference of high-frequency noise, white noise and random pulses, leading to the false alarms of alarming devices. At present, none of the existing denoising methods can completely eliminate the influence of the above interference on the resistive current of MOA. Therefore, this paper proposes a new method for eliminating the interference of resistive current of MOA based on adaptive variational mode decomposition (A-VMD) and adaptive singular value decomposition (A-SVD). Firstly, by sequentially changing the secondary penalty factor and the decomposition layer, and with the energy and loss indicators to measure the effect of VMD decomposition, the optimal parameters of decomposition layers and secondary penalty factor are searched out. Secondly, the A-SVD is used to eliminate the residual white noise in the resistive current after preliminary denoising by A-VMD, which provides a reliable basis for judging the insulating state of a MOA. The effectiveness of the proposed method is verified by simulation and measured data, and the processing results meet the requirements of actual projects.