基于改进变分模态分解的电网企业监测数据滤波研究

Filtering of Power Grid Enterprise Monitoring Data Based on Improved Variational Mode Decomposition

电网企业监测数据在采集过程中,常会受到设备误差、环境因素等多种噪声和干扰的影响,这些干扰因素会使得数据呈现出复杂结构和非平稳性的特征。传统的数据滤波方法在处理这类信号时,对于噪声干扰的容忍度相对较低,会过度平滑或削弱信号中的有用信息,导致数据质量无法得到显著提升。为此,设计了一种基于改进变分模态分解(IVMD)的电网企业监测数据滤波方法。在采集电网实时监测数据后,对采集的这些数据进行小波变换。通过对信号进行不同尺度的分解,实现对电网企业监测数据原始信号的重构。设置阈值函数对小波变换后的数据进行处理,去除噪声并保留有用信号,提高信号的纯净度和信噪比。为了进一步提升数据的准确性和可靠性,利用IVMD方法对于去噪后的数据进行进一步的处理,以滤除剩余的噪声或干扰成分。试验结果表明,所设计的方法滤波效果显著,其失真比最小值达到了0.028 dB,这表明通过该方法能够得到更加纯净和准确的电网企业监测数据。

During the collection process of power grid enterprise monitoring data,various noises and interferences such as equipment errors and environmental factors often affect the data.These interfering factors result in the data exhibiting complex structures and non-stationary characteristics.Traditional data filtering methods,when dealing with such signals,have relatively low tolerance for noise interference,leading to excessive smoothing or weakening of useful information in the signals,thereby failing to significantly improve the data quality.Therefore,a filtering method for power grid enterprise monitoring data based on improved variational mode decomposition is designed.After collecting real-time monitoring data from the power grid,wavelet transform is applied to these collected data.By decomposing the signals into different scales,the original signals of power grid enterprise monitoring data are reconstructed.A threshold function is employed to process the data after wavelet transform,removing noise while retaining useful signals,thereby enhancing the purity and signal-to-noise ratio of the signals.To further improve the accuracy and reliability of the data,the improved variational mode decomposition(IVMD)method is utilized for further processing the denoised data to filter out residual noise or interference components,thus enhancing the accuracy and reliability of the signals.Experimental results show that the filtering effect of the proposed method is significant,with the minimum distortion ratio reaching 0.028 dB,indicating that the method can obtain cleaner and more accurate power grid enterprise monitoring data.