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000932719 019__ $$a1154339022$$a1155874515$$a1156775686$$a1157694629$$a1158355813
000932719 020__ $$a9783030474430$$q(electronic book)
000932719 020__ $$a3030474437$$q(electronic book)
000932719 020__ $$z9783030474423
000932719 020__ $$z3030474429
000932719 0247_ $$a10.1007/978-3-030-47
000932719 035__ $$aSP(OCoLC)on1154510812
000932719 035__ $$aSP(OCoLC)1154510812$$z(OCoLC)1154339022$$z(OCoLC)1155874515$$z(OCoLC)1156775686$$z(OCoLC)1157694629$$z(OCoLC)1158355813
000932719 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dEBLCP$$dYDX$$dLQU$$dOCLCF
000932719 049__ $$aISEA
000932719 050_4 $$aTK1007
000932719 08204 $$a621.31$$223
000932719 1001_ $$aSanchez, Edgar N.
000932719 24510 $$aNeural control of renewable electrical power systems /$$cEdgar N. Sánchez, Larbi Djilali.
000932719 260__ $$aCham :$$bSpringer,$$c2020.
000932719 300__ $$a1 online resource (221 pages).
000932719 336__ $$atext$$btxt$$2rdacontent
000932719 337__ $$acomputer$$bc$$2rdamedia
000932719 338__ $$aonline resource$$bcr$$2rdacarrier
000932719 4901_ $$aStudies in Systems, Decision and Control ;$$vv.278
000932719 506__ $$aAccess limited to authorized users.
000932719 520__ $$aThis book presents advanced control techniques that use neural networks to deal with grid disturbances in the context renewable energy sources, and to enhance low-voltage ride-through capacity, which is a vital in terms of ensuring that the integration of distributed energy resources into the electrical power network. It presents modern control algorithms based on neural identification for different renewable energy sources, such as wind power, which uses doubly-fed induction generators, solar power, and battery banks for storage. It then discusses the use of the proposed controllers to track doubly-fed induction generator dynamics references: DC voltage, grid power factor, and stator active and reactive power, and the use of simulations to validate their performance. Further, it addresses methods of testing low-voltage ride-through capacity enhancement in the presence of grid disturbances, as well as the experimental validation of the controllers under both normal and abnormal grid conditions. The book then describes how the proposed control schemes are extended to control a grid-connected microgrid, and the use of an IEEE 9-bus system to evaluate their performance and response in the presence of grid disturbances. Lastly, it examines the real-time simulation of the entire system under normal and abnormal conditions using an Opal-RT simulator.
000932719 588__ $$aDescription based on print version record.
000932719 650_0 $$aElectric power systems$$xControl.
000932719 650_0 $$aRenewable energy sources.
000932719 7001_ $$aDjilali, Larbi.
000932719 77608 $$iPrint version:$$aSánchez, Edgar N.$$tNeural Control of Renewable Electrical Power Systems$$dCham : Springer International Publishing AG,c2020$$z9783030474423
000932719 830_0 $$aStudies in systems, decision and control ;$$vv. 278.
000932719 852__ $$bebk
000932719 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-47443-0$$zOnline Access$$91397441.1
000932719 909CO $$ooai:library.usi.edu:932719$$pGLOBAL_SET
000932719 980__ $$aEBOOK
000932719 980__ $$aBIB
000932719 982__ $$aEbook
000932719 983__ $$aOnline
000932719 994__ $$a92$$bISE