An Intelligent Data Mining-Based Fault Detection and Classification Strategy for Microgrid

Abstract

The specific characteristics and operations of microgrid cause protection problems due to high penetration of distributed energy resources. To resolve these issues, the proposed scheme employs the Hilbert transform and data mining approach to protect the microgrid. First, the Hilbert transform is used to preprocess the faulted voltage and current signals to extract the sensitive fault features. Then, the obtained data set of the extracted features is input to the logistic regression classifier for fault detection. Later, fault classification is done by training the AdaBoost classifier. In the proposed scheme, the simulation results for feature extractions are evaluated on a standard International Electrotechnical Commission (IEC) medium voltage microgrid, compatible with MATLAB/SIMULINK software environment, whereas, Python is used for training and testing of data mining model. The results are evaluated under grid-connected and islanded modes for both looped and radial configurations by simulating various fault and no-fault cases. The results show that the accuracy of the proposed logistic regression and AdaBoost classifier is higher when compared to decision tree, support vector machine, and random forest methods. The results further validate the robustness of the proposed method against the measurement noise.

Keywords

• AdaBoost classifier
• data mining
• fault protection
• feature extraction
• Hilbert transform
• logistic regression