000			04175nam a22004815i 4500
999			_c387557 _d387557
001			387557
003			ES-MaUEC
005			20230326092812.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220601s2020 sz | s |||| 0|eng d
020			_a9783031025051
024	7		_a10.1007/978-3-031-02505-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK1087 _b2020 EB
100	1		_aRao, Sunil _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687607
245	1	0	_aMachine Learning for Solar Array Monitoring, Optimization, and Control _cby Sunil Rao, Sameeksha Katoch, Vivek Narayanaswamy, Gowtham Muniraju, Cihan Tepedelenlioglu, Andreas Spanias
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (IX, 81 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2020
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9407082 _aSistemas de control inteligente
700	1		_aKatoch, Sameeksha _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687608
700	1		_aNarayanaswamy, Vivek _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687609
700	1		_aMuniraju, Gowtham _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687610
700	1		_aTepedelenlioğlu, Cihan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686553
700	1		_aSpanias, Andreas _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686104
776	0	8	_iPrinted edition: _z9783031003264
776	0	8	_iPrinted edition: _z9783031013775
776	0	8	_iPrinted edition: _z9783031036330
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02505-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI