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024 7 _a10.1007/978-981-15-0275-0
_2doi
050 4 _aTA352-356
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072 7 _aTEC009070
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082 0 4 _a620.3
_223
100 1 _aS. Dhiman, Harsh.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_939332
245 1 0 _aDecision and Control in Hybrid Wind Farms
_h[electronic resource] /
_cby Harsh S. Dhiman, Dipankar Deb.
250 _a1st ed. 2020.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2020.
300 _aXXII, 140 p. 64 illus., 62 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v253
505 0 _aFundamentals of Wind Turbine and Wind Farm Control Systems -- Multi-Criteria Decision Making: An Overview -- Decision Making in Hybrid Wind Farms -- Fuzzy based Decision Making in Hybrid Wind Farms -- Control Applications in Hybrid Wind Farms -- BESS Life Enhancement for Hybrid Wind Farms.
520 _aThis book focuses on two of the most important aspects of wind farm operation: decisions and control. The first part of the book deals with decision-making processes, and explains that hybrid wind farm operation is governed by a set of alternatives that the wind farm operator must choose from in order to achieve optimal delivery of wind power to the utility grid. This decision-making is accompanied by accurate forecasts of wind speed, which must be known beforehand. Errors in wind forecasting can be compensated for by pumping power from a reserve capacity to the grid using a battery energy storage system (BESS). Alternatives based on penalty cost are assessed using certain criteria, and MCDM methods are used to evaluate the best choice. Further, considering the randomness in the dynamic phenomenon in wind farms, a fuzzy MCDM approach is applied during the decision-making process to evaluate the best alternative for hybrid wind farm operation. Case studies from wind farms in the USA are presented, together with numerical solutions to the problem. In turn, the second part deals with the control aspect, and especially with yaw angle control, which facilitates power maximization at wind farms. A novel transfer function-based methodology is presented that controls the wake center of the upstream turbine(s); lidar-based numerical simulation is carried out for wind farm layouts; and an adaptive control strategy is implemented to achieve the desired yaw angle for upstream turbines. The proposed methodology is tested for two wind farm layouts. Wake management is also implemented for hybrid wind farms where BESS life enhancement is studied. The effect of yaw angle on the operational cost of BESS is assessed, and case studies for wind farm datasets from the USA and Denmark are discussed. Overall, the book provides a comprehensive guide to decision and control aspects for hybrid wind farms, which are particularly important from an industrial standpoint.
650 0 _aMultibody systems.
_96018
650 0 _aVibration.
_96645
650 0 _aMechanics, Applied.
_93253
650 0 _aEnergy policy.
_914226
650 0 _aEnergy and state.
_931819
650 0 _aElectric power production.
_927574
650 0 _aAlgorithms.
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650 0 _aEnergy harvesting.
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650 1 4 _aMultibody Systems and Mechanical Vibrations.
_932157
650 2 4 _aEnergy Policy, Economics and Management.
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650 2 4 _aElectrical Power Engineering.
_931821
650 2 4 _aMechanical Power Engineering.
_932122
650 2 4 _aAlgorithms.
_93390
650 2 4 _aEnergy Harvesting.
_92541
700 1 _aDeb, Dipankar.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_939333
710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9789811502774
830 0 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v253
_939335
856 4 0 _uhttps://doi.org/10.1007/978-981-15-0275-0
912 _aZDB-2-ENG
912 _aZDB-2-SXE
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