Summary
The paper presents a Stockwell transform (ST) and artificial neural network‐based approach for the detection and localization of faults in distribution systems considering the complexities of network architecture and the distributed generation (DG) integration. Firstly, a faulty‐line detection technique is developed based upon the total harmonic distortion of the fault current signal captured from the line ends. Then, the ST coefficients of the faulty signal are used as an attribute to classify the fault. Finally, the root‐mean‐square values calculated from the ST coefficients are used for the fault localization. The algorithm is tested on an IEEE 13‐bus unbalanced and a 52‐bus practical Indian balanced distribution system with DG. The results are compared with some existing algorithms. The comparisons and the test results show that the proposed technique can be a useful tool in detecting the faults in complicated fault scenarios.
Summary
The high impedance arc fault (HIAF) poses a significant threat to the living being as it involves arcing. The enormous amount of heat generation during arc is a major concern in this regard. There are different types of arc that may occur depending on the arcing conditions and involved surfaces. The severity of the arc is determined by the involved arcing surface. In this study, arc in metallic (sphere gap, rod gap) and nonmetallic (leaning tree, concrete) surfaces on a distribution system is considered for the analysis. An empirical mode decomposition (EMD)‐based approach is applied along with k‐nearest neighbor (KNN) for the characterization and classification of the real‐time arc of different arcing conditions. The results obtained using EMD and KNN algorithm on arc signals successfully characterize and classify different HIAF by their harmonic signature. Along with KNN, the cross‐validation data‐mining algorithm is also applied to check the robustness of the approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.