基于SDP和2DLNMF的变压器偏磁状态识别方法

Recognition Method of Transformer Magnetic BiasState Based on SDP and 2DLNMF

为了有效检测变压器直流偏磁状态,从多通道振动信号融合的角度出发,提出了一种基于对称点模式(SDP)和二维局部非负矩阵分解(2DLNMF)的变压器偏磁状态识别方法。利用SDP算法将采集的多通道振动信号融合成SDP图像特征;然后应用2DLNMF算法对其进行了降维优化,据此构建了基于支持向量机(SVM)算法变压器偏磁状态识别模型。研究结果表明:基于SDP-2DLNMF的信息融合方法充分了展现不同信号间的特征差异,获取的低维特征可有效反映变压器直流偏磁程度,据此建立的SVM状态识别模型具有较高的识别精度,为变压器的状态监测提供了技术支撑。

In order to effectively detect the transformer DC magnetic bias state,this paper starts from the perspective of multi-channel information fusion,and proposes a new method for identifying the bias state of a transformer based on symmetrized dot pattern(SDP)and 2-dimensionlal local nonngeative matrix factorzization(2DLNMF).Firstly,SDP algorithm is used to fuse the multi-channel vibration signals into SDP image features.Then,the 2DLNMF algorithm was used to optimize its dimensionality reduction,according to which the transformer magnetic bias state recognition model based on the support vector machine(SVM)algorithm was built.The research results show that information fusion method based on the SDP-2DLNMF fully shows the characteristics of the differences between different signal,the obtained low-dimensional characteristics can effectively reflect the degree of DC magnetic bias of the transformer,and the SVM state recognition model established on this basis has high recognition accuracy,which provides technical support for transformer state monitoring.