The Experts below are selected from a list of 37023 Experts worldwide ranked by ideXlab platform
Yannan Guo - One of the best experts on this subject based on the ideXlab platform.
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power system fault classification and prediction based on a three layer Data Mining Structure
IEEE Access, 2020Co-Authors: Yunliang Wang, Xiaodong Wang, Yannan GuoAbstract: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.
Yunliang Wang - One of the best experts on this subject based on the ideXlab platform.
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power system fault classification and prediction based on a three layer Data Mining Structure
IEEE Access, 2020Co-Authors: Yunliang Wang, Xiaodong Wang, Yannan GuoAbstract: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.
Xiaodong Wang - One of the best experts on this subject based on the ideXlab platform.
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power system fault classification and prediction based on a three layer Data Mining Structure
IEEE Access, 2020Co-Authors: Yunliang Wang, Xiaodong Wang, Yannan GuoAbstract: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.
Yun Zhang - One of the best experts on this subject based on the ideXlab platform.
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ahp construct Mining component strategy applied for Data Mining process
International Conference on Information Science and Technology, 2012Co-Authors: Yun ZhangAbstract:AHP Construct Mining Component (ACMC), which is a new term, is an enhancement of applicable Structure for multidimensional and multi-level complex Dataflow. ACMC is applied into Data Mining framework and different processing components with the purpose are improvement on numerous aspects in multiply level. ACMC provides not only an integrated platform to support different processing components with comprehensive and systemic methodology but also provides controllable strategy for whole processing. The instances of KPI (key performance indicator) and CSF (critical success factor) are the key points and foundation of the whole Data Mining Structure. Mode-Refresh and Model-Evaluation are recognized as engines of the Data Mining machine. Influencing factor that come from these engines will influence decision constrictions. ACMC supports combination of different Mining component from strategy level, tactical level to abstractive level, and then provide the successful model component for the whole Data Mining processing. ACMC is a new direction of the decision of KDD (Knowledge Discovery in Database).
