  This paper presents fault detection and classification using Wavelet and ANN
based methods in a DFIG-based series compensated system. The state-of-the art
methods include Wavelet transform, Fourier transform, and Wavelet-neuro fuzzy
methods-based system for fault detection and classification. However, the
accuracy of these state-of-the-art methods diminishes during variable
conditions such as changes in wind speed, high impedance faults, and the
changes in the series compensation level. Specifically, in Wavelet transform
based methods, the threshold values need to be adapted based on the variable
field conditions. To solve this problem, this paper has proposed a Wavelet-ANN
based fault detection method where Wavelet is used as an identifier and ANN is
used as a classifier for detecting various fault cases. This methodology is
also effective under SSR condition. The proposed methodology is evaluated on
various fault and non-fault cases generated on an IEEE first benchmark model
under varying compensation levels from 20% to 55%, impedance faults, and wind
velocity from 6m/sec to 10m/sec using MATLAB/Simulink, OPALRT(OP4510)
manufactured real-time digital simulator environment, Arduino board I/O ports
communicating with external PC in which ANN model dumped, using Arduino support
package of MATLAB. The preliminary results are compared with the
state-of-the-art fault detection method, where the proposed method shows robust
performance under varying field conditions.
