Wavelet Based Feature Analysis of Fault Signals in a Microgrid

Integration of renewable energy resources has been facilitated by the evolution of existing electrical power networks into Smartgrid. Increasing penetration of Distributed Energy Resources (DERs) at the distribution network has made it hard to control and manage the grid efficiently. One of the numerous technical issues, related to the integration of DERs in a distribution network is to come up with an effective protection scheme. Traditional protection scheme fails to perform due to significant variations in fault current levels during grid-connected and islanded mode of operation. This paper presents a wavelet based method that does not rely on the fault current level for fault detection in a microgrid. The proposed technique decomposes the fault current signal using wavelet transform to obtain important fault current features. These features include difference in energy, entropy and standard deviation of pre-fault and post-fault current cycles. The proposed method is tested for faults in a modified IEEE 13-Node test feeder, consisting of renewable energy based DERs in islanded and grid-connected modes. The test results indicate that the proposed strategy is able to distinguish between faults and disturbances in the system which can lead to the implementation of an effective protection scheme for microgrids.