Abstract
In traditional fault diagnosis methods in power systems, it is difficult to accurately classify and predict the types of faults. With the emergence of big data technology, the fault classification and prediction methods based on big data analysis and processing have been applied in power systems. To make the classification and prediction of the fault types more accurate, this paper proposes a hybrid data mining method for power system fault classification and prediction based on clustering, association rules and stochastic gradient descent. This method uses a three-layer data mining model: The first layer uses the K-means clustering algorithm to preprocess the original fault data source, and it proposes to use self-encoding to simplify the data form. The second layer effectively eliminates the data that have little impact on the prediction results by using association rules, and the highly correlated data are mined to become the regression training data. The third layer first uses the cross-validation method to obtain the optimal parameters of each fault model, and then, it uses stochastic gradient descent for data regression training to obtain a classification and prediction model for each fault type. Finally, a verification example shows that compared with a single data mining algorithm model, the proposed method is more comparative in terms of the data mining, and the established power system fault classification and prediction model has global optimality and higher prediction accuracy, which has a certain feasibility for real-time online power system fault classification and prediction. This method reduces the disturbances from low-impact or irrelevant data by mining the fault data three times, and it uses cross-validation to optimize the multiple regression parameters of the regression model to solve the problems of low accuracy, large errors and easily falling into a local optimum, given the conduct of fault classification and prediction.
Highlights
To ensure the reliability and stability of the power system, predicting power faults in advance and making the corresponding preventive measures can effectively prevent the occurrence of power accidents and reduce economic losses
Based on the above-mentioned considerations, this paper proposes a hybrid data mining algorithm based on K-means clustering, Apriori association rules and stochastic gradient descent (SGD) to classify and predict power system faults
Compared with the single algorithm model, the proposed method has greatly improved the accuracy and reliability of power system fault classification and prediction, which can be used to optimize parameters online and can be applied to different operating states
Summary
To ensure the reliability and stability of the power system, predicting power faults in advance and making the corresponding preventive measures can effectively prevent the occurrence of power accidents and reduce economic losses. After the association rules mining, the samples are highly correlated in their attributes, and the information associated with the fault types is stored, which is helpful for mining valuable results during the SGD data regression training In this way, the result deviation caused by data redundancy is avoided, and the performance and accuracy of the regression analysis are improved. D. THE THIRD LAYER OF THE DATA MINING PROCESSING METHODS AND RULES After the first two layers of data mining, K-means clustering and Apriori association rules have mined the strong correlation samples that correspond to the different types of power system faults. 2) THE THIRD-LAYER DATA MINING RULES BASED ON THE STOCHASTIC GRADIENT DESCENT METHOD The SGD optimization algorithm performs third-layer data mining on sample library II, as shown in Fig. 4: 1) Firstly, a prediction model function is established. The optimal model parameter w can be obtained through the optimal loss function and the optimal regular terms; the fitting law of the samples in CQj to the fault result in Gj is found, in such a way that the optimization model can classify and predict the faults from the new data
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