基于数据挖掘算法的电网调度信号异常数据提取方法

Abnormal data extracting from power grid dispatching signals based on data mining algorithms

在获取电网调度信号后,大多采用传统深度置信型辨识制度提取异常数据,只能获取低维数据包含的异常信息参量,使得最终数据提取结果曲线下面积(AUC)值较低。因此,为了提高电网调度信号异常数据提取结果的AUC值,提出基于数据挖掘算法的电网调度信号异常数据提取方法。应用独立成分分析算法处理电网调度信号,去除信号中的噪声信息。并对去噪后的信号进行小波分解,得到多个子信号数据集。运用数据挖掘算法中的聚类算法分析子信号数据集,得到数据样本特征,并在考虑属性特征密度指标的情况下完成数据特征分类,获取异常数据特征。最后,在支持向量数据描述的辅助下,检测出电网调度信号异常数据,汇总这部分数据即可完成异常数据提取。实验结果表明,所提方法应用后得到的异常数据提取结果AUC值总是大于0.85,证明了其具优越的应用效果。

After acquiring the power grid dispatching signals,traditional deep confidence identification systems are mostly used for anomaly data extraction,which can only obtain the anomaly information parameters contained in low-dimensional data,resulting in a lower Area Under the Curve(AUC)value of the final data extraction result.Therefore,in order to improve the AUC value of the anomaly data extraction results of the power grid dispatching signals,an anomaly data extraction method for power grid dispatching signals based on data mining algorithms is proposed.The power grid dispatching signals are processed using the Independent Component Analysis(ICA)algorithm to remove noise from the signals.The denoised signals are then subjected to wavelet decomposition to obtain multiple sub-signal datasets.Clustering algorithms in data mining algorithms are employed to analyze the sub-signal datasets to obtain the characteristics of the data samples,and data feature classification is completed considering the attribute feature density index to obtain the anomaly data characteristics.Finally,with the assistance of the Support Vector Data Description(SVDD),the abnormal data in the power grid dispatching signals are detected,and summarizing this part of the data can complete the anomaly data extraction.The experimental results show that the AUC value of the anomaly data extraction results obtained after applying the proposed method is always greater than 0.85,proving its superior application effect.