An effective power system protection scheme has to be able to detect and locate all occurring faults corresponding to low and high impedance values. The latter category poses the greatest challenge for the protection schemes due to the low values of the related fault current. This paper extends previous work by the authors on the subject, aiming to achieve detection and location of high impedance faults (HIFs) in multiconductor overhead distribution networks utilizing power line communication (PLC) devices. Fault detection is proposed to be performed by a PLC device installed at the starting point of the monitored line and by using differences to the values of metrics related to input impedance at frequencies utilized by narrowband systems. Moreover, fault location can be derived by a response to impulse injection procedure utilized by all installed PLC devices along the line. The method is evaluated and validated in various simulation test cases concerning its ability to effectively detect and locate HIFs.
Modern Information Retrieval Systems match the terms included in a user's query with available documents, through the use of an index. A fuzzy thesaurus is used to enrich the query with associated terms. In this work, we use semantic entities, rather than terms; this allows us to use knowledge stored in a semantic encyclopedia, specifically the ordering relations, in order to perform a semantic expansion of the query. The process of query expansion takes into account the query context,which is defined as a fuzzy set of semantic entities. Furthermore, we integrate our approach with the user's profile.
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.