A morphological filter based disturbance detection and classification technique for DFIG wind farm based microgrid

A new morphological filter based disturbance detection and classification technique for DFIG wind farm based micro grid has been proposed in this paper. The structuring element based mathematical morphology with an efficient de-noising characteristics, accurately estimates the signal characteristics and thus is a striking technique for development of the disturbance detection and classification algorithm. The raw voltage and current signals near the target DG (DFIG wind farm) are passed through the morphological filters. Some computations over the morphological signals generate the target feature sets, which comprises of the kurtosis and energy components. These target feature sets are then passed through a decision tree which precisely classifies some of the disturbances such as islanding, switching faults, load switching(balanced and unbalanced), capacitor bank switching, etc., as well as some power quality events such as voltage sag and voltage swell, respectively, which are simulated, tested and verified on a DFIG wind farm based micro grid model in the MATLAB/ SIMULINK environment.