大葉大學圖書館 |

Machine learning for solar array monitoring, optimization, and control[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	621.31/244
書名/作者:	Machine learning for solar array monitoring, optimization, and control/ Sunil Rao, Sameeksha Katoch, Vivek Narayanaswamy, Gowtham Muniraju, Cihan Tepedelenlioglu, Andreas Spanias, Pavan Turaga, Raja Ayyanar, Devarajan Srinivasan.
作者:	Rao, Sunil,
其他作者:	Katoch, Sameeksha,
面頁冊數:	1 online resource (93 p.)
標題:	Photovoltaic power systems - Automatic control.
標題:	Intelligent control systems.
標題:	Machine learning.
ISBN:	9781681739076
ISBN:	9781681739083
ISBN:	9781681739090
書目註:	ncludes bibliographical references (pages 65-78).
摘要、提要註:	The 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
電子資源:	https://portal.igpublish.com/iglibrary/search/MCPB0006578.html

Machine learning for solar array monitoring, optimization, and control[electronic resource] /
Rao, Sunil,

Machine learning for solar array monitoring, optimization, and control[electronic resource] /Sunil Rao, Sameeksha Katoch, Vivek Narayanaswamy, Gowtham Muniraju, Cihan Tepedelenlioglu, Andreas Spanias, Pavan Turaga, Raja Ayyanar, Devarajan Srinivasan. - 1 online resource (93 p.) - Synthesis lectures on engineering, science, and technology ;13. - Synthesis lectures on engineering, science, and technology ;39..

ncludes bibliographical references (pages 65-78).

Access restricted to authorized users and institutions.

The 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

Mode of access: World Wide Web.

ISBN: 9781681739076Subjects--Topical Terms:

752895
Photovoltaic power systems
--Automatic control.Index Terms--Genre/Form:

336502
Electronic books.

LC Class. No.: TK1087

Dewey Class. No.: 621.31/244

Machine learning for solar array monitoring, optimization, and control[electronic resource] /
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520 3 $a The 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
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700 1 $a Narayanaswamy, Vivek, $e author. $3 752890
700 1 $a Muniraju, Gowtham, $e author. $3 752891
700 1 $a Tepedelenlioglu, Cihan, $e author. $3 714573
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700 1 $a Srinivasan, Devarajan, $e author. $3 752894
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