In this paper empirical mode decomposition (EMD) with adaptive neuro fuzzy information system (ANFIS) is proposed to detect and classifying the power system disturbances (faults) for an off shore wind farm connected with voltage source converter (VSC) based high voltage DC (HVDC) transmission system. The voltage and current samples near the target DC transmission link for the VSC based HVDC system along with double fed induction generator (DFIG), whose attributes considered as speed is taken at higher and lower value, varying the fault resistance and different locations of the faults are passed through the EMD. This signal processing algorithm gives features as entropy, kurtosis and standard deviation. The features from the EMD algorithm has been taken as training data sets for the ANFIS, which is updating the initial conditions by using the gradient descent algorithm. The proposed method classifying the different faults such as positive to ground, negative to ground and positive to negative faults. The whole model has been simulated in the MATLAB/Simulink environment. In order to prove the efficacy of the proposed technique, performance results are verified in terms of the efficiency as compared with some existing methods.
Volume 11 | 02-Special Issue
Pages: 1154-1167