基于欧氏距离最佳K均值聚类的超级电容组故障在线鉴别方法

为了提高超级电容组运行可靠性需要对故障电容进行在线鉴别,针对现有超级电容故障鉴别方法参数识别难度高和采集数据量大的问题,该文采用最佳K均值聚类方法在线检测故障超级电容器,并提出了最佳聚类的欧氏距离指标。该方法首先对在线电压信号数据进行预处理,采用奇异值分解提取特征值进行K-Means动态聚类并计算相应的欧氏距离指标,由最佳聚类结果鉴别出故障单体。针对该文提出方法设计了超级电容组充放电仿真试验进行验证。试验结果表明基于欧氏距离指标最佳K均值动态聚类的超级电容组故障在线鉴别方法可以根据串联单体电压信号进行故障检测。该文可为超级电容在线故障检测系统的开发与研制提供参考。

Online fault identification method for supercapacitor group of optimal K-means cluster based on Euclidean distance

Abstract: In order to keep the energy storage system which is based on supercapacitor group with series connection work reliably, the fault groups of supercapacitors are necessary to be identified. A fault state identification method of K-means cluster was presented in this paper. A Euclidean distance index was proposed to choose K value automatically. In this method, the voltage signal data are preprocessed to form the sample array. The singular value decomposition is applied to project out a shadow subset of the sample array. The K-means method is used to cluster the shadow subset for fault state identification. The fault subsets are detected in the cluster result. The largest cluster is identified as normal state and the others are abnormal state. The Euclidean distance index was proposed to decide the optimal K value automatically after enumeration of all possible K. This index is based on the Euclidean distance of pairwise data points and pairwise cluster centers. The minimize value of index is bonded to the optimal K value. Adjustable coefficients are used to improve the adaptability of this index. Based on the principle of K-means cluster method and Euclidean distance index, the fault state identification process was introduced. In this process, after sampling the voltage of supercapacitor cells, the difference voltage array is established to form the feature space. The singular value decomposition is used on the difference voltage array to form the sample subset. The variance of sample subset is compared to set limitation. If the variance overrides the limitation, K-means algorithm will be used to cluster the sample subset, and the Euclidean distance index will be used to decide the optimal K value. By counting the group amount of sample subset, the fault state capacitors can be distinguished. An experiment system was designed to verify the efficiency and validity of the method and index. The experiment environment was MATLAB-Simulink. Two experiments were carried out based on the experiment system. The first experiment was for the comparison of different indices. This experiment was set in randomly charging and discharging situation to approach the actual situations. The optimal K value was picked out from the enumerated values by searching the minimum value of Euclidean distance index. The result collections distinguished the normal and abnormal sets. As this result was the same with the given situation, the effective of Euclidean distance index was proved. The result showed that the proposed character vector exacting method correctly reflected the characteristics of supercapacitor state. Other existing indices were computed out. The comparison of efficiencies among different indices was made. The homogeneity-separation (HS), Calinski-Harabasz (CH) and Krzanowski-Lai (KL) index failed to identify the right group of this case. Hartigan index got the right result. But the Hartigan index also had its drawback in utilities, efficiency and complexity. The second experiment was designed to prove the correctness of the method and index in different working scenarios. In this experiment, 3 groups were set. The 1st group included 2 subsets of samples in which the capacitors were charged with different current, the 2nd group included 2 subsets of samples in which the capacitors were discharged with different starting voltage, and the 3rd group of data included 4 subset of samples in which the capacitors were charged and discharged randomly. The Euclidean distance index indicated that the 1st and the 2nd group got the results of 3 subsets. The largest subset was the normal set and the other 2 subsets were abnormal set. The Euclidean distance index showed that the 3rd group got the correct results of 2 subsets. All of the experiment groups got the expected result. The results showed that the fault state could be identified correctly through the dynamic cluster method according to the voltage signal of supercapacitor cell. The validity of Euclidean distance index to select the optimal K value of clusters for fault identification was proved. Two main conclusions were drawn in this paper. The first is that the fault state identification method based on K-means cluster can distinguish the normal and abnormal set of serial connected supercapacitors. The second is that the Euclidean distance index can select the optimal K value automatically. The fault identification method proposed in this paper has 2 advantages. The first advantage is that identification of capacitor parameters is avoided. The second advantage is that this method has low dependency on precise of acquisition data.