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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 1948 2.2. TheWindPowerModel The WT used in this paper was a commercial product, JPS-200, which is equipped with apermanentmagnet synchronousgenerator thathasa ratingpowerof200W[21]. Awindpower system and a theoretical model are developed to estimatewind power fromwind speed based on experimental responses. The experimentswere conductedusing an industrial fan,whichhad amaximumwindspeedof about 10m/s. Thewindpower systemstructure is shown inFigure2. Wemeasured theACcurrent andvoltage fromawind turbine and recorded theDCcurrent and voltage fromawindcontroller. ŘŪůťġŵŶųţŪůŦġ ńŰůŵųŰŭŭŦųġ ŃŢŵŵŦųźġ ŎŦŢŴŶųŦŮŦůŵġ ġ ńŪųŤŶŪŵŴġ Ĵġ IJġ ĵġ ijġ DF,IJ烉 GF,ij烉 DF9Ĵ烉 GF9ĵ烉 Figure2.Measurementof thewindpower. TheWTwas testedunder steadywindandvaryingwindconditions. The time responses are showninFigure3a,where theresponseschangeslowlywithsteadywind,butquicklywithvarying wind. Fromthecomparisonof thewindspeedandACpower,as illustrated inFigure3b, thewind powercanbetheoreticallydescribedusingthe followingequation: Pac=0.11574V3wind (1) where Pac andVwind represent the power and speed, respectively, of thewind. The experimental resultsshowthat thewindpowercanbepredictedfromthewindspeedwithmaximumrootmean square errors of 7.64Wand17.32Wfor steady andvaryingwind, respectively. TheWTreached itsmaximumtheoretical power of 200Wwhen thewind speedwasgreater than 12m/s. We set thebatteryvoltageat12V.TheenergyconversionrelationshipbetweenACandDCwindpower is showninFigure3c,where thechargingoperation isdividedinto threezonesaccordingto thewind turbinevoltageVac: (1)nocharging(whenVac<4.3V),where thewindcontrollerdoesnotcharge the battery; (2) linearcharging(when4.3V≤Vac<8V),where theDCchargingvoltage increases linearly; and(3)stablecharging(whenVac≥8V),where theDCchargingvoltage is14.3V.Theconversionof ACandDCpowercanbedescribedas follows: PDC=0.70973Pac−3.0958=0.0821V3wind−3.0958 (2) as illustrated inFigure 3c. Therefore, givenwind speeddata, thewind turbineDCpower canbe calculatedby (1) and (2). Equations (1) and (2) canbeapplied tobuild thewindpowermodule in Figure2 for thesimulationandoptimizationanalyses. 197
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Short-Term Load Forecasting by Artificial Intelligent Technologies
Titel
Short-Term Load Forecasting by Artificial Intelligent Technologies
Autoren
Wei-Chiang Hong
Ming-Wei Li
Guo-Feng Fan
Herausgeber
MDPI
Ort
Basel
Datum
2019
Sprache
englisch
Lizenz
CC BY 4.0
ISBN
978-3-03897-583-0
Abmessungen
17.0 x 24.4 cm
Seiten
448
Schlagwörter
Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
Kategorie
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Short-Term Load Forecasting by Artificial Intelligent Technologies