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020 _a9783031025051
_9978-3-031-02505-1
024 7 _a10.1007/978-3-031-02505-1
_2doi
050 4 _aTK1-9971
072 7 _aTHR
_2bicssc
072 7 _aTEC007000
_2bisacsh
072 7 _aTHR
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082 0 4 _a621.3
_223
100 1 _aRao, Sunil.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981354
245 1 0 _aMachine Learning for Solar Array Monitoring, Optimization, and Control
_h[electronic resource] /
_cby Sunil Rao, Sameeksha Katoch, Vivek Narayanaswamy, Gowtham Muniraju, Cihan Tepedelenlioglu, Andreas Spanias.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aIX, 81 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Power Electronics,
_x1931-9533
505 0 _aAcknowledgments -- Introduction -- Solar Array Research Testbed -- Fault Classification Using Machine Learning -- Shading Prediction for Power Optimization -- Topology Reconfiguration Using Neural Networks -- Summary -- Bibliography -- Authors' Biographies .
520 _aThe efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance. Monitoring utility-scale solar arrays was shown to minimize the cost of maintenance and help optimize the performance of the photo-voltaic arrays under various conditions. We describe a project that includes development of machine learning and signal processing algorithms along with a solar array testbed for the purpose of PV monitoring and control. The 18kW PV array testbed consists of 104 panels fitted with smart monitoring devices. Each of these devices embeds sensors, wireless transceivers, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. The facility enables networked data exchanges via the use of wireless data sharing with servers, fusion and control centers, and mobile devices. We develop machine learning and neural network algorithms for fault classification. In addition, we use weather camera data for cloud movement prediction using kernel regression techniques which serves as the input that guides topology reconfiguration. Camera and satellite sensing of skyline features as well as parameter sensing at each panel provides information for fault detection and power output optimization using topology reconfiguration achieved using programmable actuators (relays) in the SMDs. More specifically, a custom neural network algorithm guides the selection among four standardized topologies. Accuracy in fault detection is demonstrate at the level of 90+% and topology optimization provides increase in power by as much as 16% under shading.
650 0 _aElectrical engineering.
_981355
650 0 _aElectric power production.
_927574
650 0 _aElectronics.
_93425
650 1 4 _aElectrical and Electronic Engineering.
_981356
650 2 4 _aElectrical Power Engineering.
_931821
650 2 4 _aMechanical Power Engineering.
_932122
650 2 4 _aElectronics and Microelectronics, Instrumentation.
_932249
700 1 _aKatoch, Sameeksha.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981357
700 1 _aNarayanaswamy, Vivek.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981358
700 1 _aMuniraju, Gowtham.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981359
700 1 _aTepedelenlioglu, Cihan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981360
700 1 _aSpanias, Andreas.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981361
710 2 _aSpringerLink (Online service)
_981362
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031003264
776 0 8 _iPrinted edition:
_z9783031013775
776 0 8 _iPrinted edition:
_z9783031036330
830 0 _aSynthesis Lectures on Power Electronics,
_x1931-9533
_981363
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02505-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c85162
_d85162