Summary
A new spectral energy differential protection scheme using sparse Fourier kernel fast time‐frequency transform is proposed for the detection, classification, and location of faults either on the grid‐connected or islanded AC microgrid. Initially, the three‐phase average and differential components of the current samples measured on either side of the distribution line are processed through an alteration detection filter, which identifies the fault incipient and consequently registers an alteration index for the particular phase, which identifies the fault. The spectral energy of the differential current components are than computed to classify the type of the fault under a number of intrinsic operating conditions like the meshed and radial architectures and grid‐connected or islanded mode of operation and varying fault distance ratios. Extensive numerical experimentation illustrates satisfactory results for all the cases investigated in this paper, which include detection, classification, and location of faults on the microgrid.
This paper proposes a pattern recognition based differential spectral energy protection scheme for ac microgrids using a Fourier kernel based fast sparse time-frequency representation (SST or simply the sparse S-Transform). The average and differential current components are passed through a change detection filter, which senses the instant of fault inception and registers a change detection point (CDP). Subsequently, if CDP is registered for one or more phases, then half cycle data samples of the average and differential currents on either side of the CDP are passed through the proposed SST technique, which generates their respective spectral energies and a simple comparison between them detects the occurrence and type of the fault. The SST technique is also used to provide voltage and current phasors and the frequency during faults which is further utilized to estimate the fault location. The proposed technique as compared to conventional differential current protection scheme is quicker in fault detection and classification, which is least effected from bias setting, has a faster relay trip response (less than one cycle from fault incipient) and a better accuracy in fault location. The significance and accuracy of the proposed scheme have been verified extensively for faults in a standard microgrid system, subjected to a large number of operating conditions and the outputs vindicate it to be a potential candidate for real time applications.
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