The scale of data generated by the complex and huge power system during operation is also very large. With the data acquisition of various information systems, it is easy to form the situation of incomplete power data information, which cannot guarantee the efficiency and quality of work, and reduce the security and reliability of the entire power grid. When incomplete data and incomplete data sets are caused by data storage failure or data acquisition errors, fuzzy clustering of data will face great difficulties. The fuzzy clustering of incomplete data of the power equipment is divided into the processing of incomplete data and the clustering analysis of "recovered" complete data. This paper proposes an IVAEGAN-IFCM interval fuzzy clustering algorithm, which uses interval data sets to fill in the incomplete data, and then completes the clustering of interval data. At the same time, the whole numerical data set is transformed into a complete interval data set. The final clustering result is obtained by interval fuzzy mean clustering analysis of the whole interval data set. Finally, the algorithm proposed in this paper and other machine learning training data sets is made for experimental analysis. The experimental results show that the algorithm proposed in this paper can complete incomplete data sets with high precision clustering. Compared with other contrast methods, it shows higher clustering accuracy. Compared with the numerical clustering algorithm, the clustering accuracy is improved by more than 4.3%, and it has better robustness. It also shows better generalization on the artificial data sets and other complex data sets. It is helpful to improve the technical level of the existing power grid and has important theoretical research value and engineering practice significance.
With the further construction and development of the smart grid, in the process of power development, production, and use, as well as the process of power distribution and use, each link will produce some high-dimensional data on the power grid with huge volume, complex structure, and complex correlation among them. The distribution of high-dimensional data in space is different from that in low-dimensional space, and the computational cost increases dramatically, which increases the complexity of visualization of high-dimensional power consumption data. Clustering analysis is a way to cluster a large number of users and summarize the typical load characteristics of different types of users. How to determine the prior information conditions of data and how to select the clustering criteria become the key to clustering. Aiming at the problems of traditional clustering algorithms in the current feature clustering analysis, this paper first deals with the load through t-SNE dimensional reduction technology, then combines the GSA elbow criterion and dichotomous K-means algorithm to cluster the load, and finally summarizes three typical load features according to the clustering results. Effective data mining technology is used to cluster and divide the massive load characteristics efficiently, which will dynamically respond to and manage the demand side. The error of classification results is less than 4.28% through the example of load characteristics. The classification accuracy of the test is 12.2% higher than that of the traditional method. According to the experimental results, the characteristics of typical load patterns and the corresponding load curve characteristics are analyzed. It overcomes the dependence of the traditional K-means algorithm on the initial centroid, avoids the algorithm falling into local optimum, and plays an important role in the defect data mining of power consumption law in power enterprises.
With the rapid development of the economy, the scale of the power grid is expanding. The number of power equipment that constitutes the power grid has been very large, which makes the state data of power equipment grow explosively. These multi-source heterogeneous data have data differences, which lead to data variation in the process of transmission and preservation, thus forming the bad information of incomplete data. Therefore, the research on data integrity has become an urgent task. This paper is based on the characteristics of random chance and the Spatio-temporal difference of the system. According to the characteristics and data sources of the massive data generated by power equipment, the fuzzy mining model of power equipment data is established, and the data is divided into numerical and non-numerical data based on numerical data. Take the text data of power equipment defects as the mining material. Then, the Apriori algorithm based on an array is used to mine deeply. The strong association rules in incomplete data of power equipment are obtained and analyzed. From the change trend of NRMSE metrics and classification accuracy, most of the filling methods combined with the two frameworks in this method usually show a relatively stable filling trend, and will not fluctuate greatly with the growth of the missing rate. The experimental results show that the proposed algorithm model can effectively improve the filling effect of the existing filling methods on most data sets, and the filling effect fluctuates greatly with the increase of the missing rate, that is, with the increase of the missing rate, the improvement effect of the model for the existing filling methods is higher than 4.3%. Through the incomplete data clustering technology studied in this paper, a more innovative state assessment of smart grid reliability operation is carried out, which has good research value and reference significance.
Substation equipment is an important part of the power grid, which undertakes the function of power transmission and conversion and directly affects the operation status of the whole substation and power system. When the substation equipment is in an abnormal working state, the temperature will change. Therefore, the temperature information of the substation equipment is used as the judgment basis to complete the judgment of the working state of the equipment, which can realize the fault diagnosis of the substation equipment and ensure that the power system works in a safe and reliable environment. In this paper, according to the characteristics of the transformer equipment shape stability, the invariant moment is used to extract the infrared image feature of the transformer equipment. The support vector machine is used to complete the classification and recognition of the image. Hu invariant moments and Zernike invariant moments are used to extracting features respectively, and the results of feature extraction are used as training samples to train support vector machines for recognition. Using the Lazy Snapping algorithm to complete the infrared image segmentation processing of substation equipment, the target region is extracted from the background completely, and the segmentation image information is completed. In the experimental test, through the test of the recognition model constructed by invariant moments, it is proved that the recognition accuracy of Zernike invariant moments combined with support vector machine in this paper is higher, and the Lazy Snapping algorithm has obvious advantages in segmentation quality compared with other methods. Therefore, the method of intelligent identification and diagnosis of the thermal fault of substation equipment studied in this paper has important practical significance for the establishment of an online diagnosis system.
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