東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Modeling and Control of Photovoltaic...

Mitra, Sagnik.

FindBook

Google Book

Amazon

博客來

Modeling and Control of Photovoltaic Array and Wind Turbine for Hybrid Power Generation Using Artificial Neural Network.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling and Control of Photovoltaic Array and Wind Turbine for Hybrid Power Generation Using Artificial Neural Network./
作者:	Mitra, Sagnik.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	107 p.
附註:	Source: Masters Abstracts International, Volume: 58-02.
Contained By:	Masters Abstracts International58-02(E).
標題:	Energy. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10981094
ISBN:	9780438685345

Modeling and Control of Photovoltaic Array and Wind Turbine for Hybrid Power Generation Using Artificial Neural Network.
Mitra, Sagnik.

Modeling and Control of Photovoltaic Array and Wind Turbine for Hybrid Power Generation Using Artificial Neural Network. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 107 p.

Source: Masters Abstracts International, Volume: 58-02.

Thesis (M.S.)--California State University, Fresno, 2018.

This thesis work concentrates on adaptive control of power transmission from the renewable energy resources such as photovoltaic (PV) system, and wind turbine (WT) system to the grid with the help of artificial neural network (ANN). In this study, a maximum power point (MPP) tracking algorithm from the photovoltaic system and the wind turbine system utilizing the feed-forward artificial neural network were evaluated. Real-time values of solar irradiance and atmospheric temperature were given as inputs to design the neural network algorithm used in the tracking the maximum power of the photovoltaic system. Variation in wind speed was considered to formulate the extraction of the maximum power of the wind turbine. The lithium-ion battery was used as a backup power system. A controlled Battery Energy Storage System (BESS) and bi-directional power flow from the renewable energy resources to the grid were studied. Also, a three-phase inverter control based on the Space Vector Pulse Width Modulation (SVPWM) technique was designed to convert the DC power to the three-phase alternating power and was transmitted to the grid. Lastly, the maximum power from the photovoltaic system, wind turbine system and the state of charge (SOC) of the battery were used as inputs to the neural network for power tracking of the overall hybrid power system with total harmonic distortion analysis. MATLAB/Simulink software was used to validate the proposed system.

ISBN: 9780438685345Subjects--Topical Terms:

876794
Energy.

Modeling and Control of Photovoltaic Array and Wind Turbine for Hybrid Power Generation Using Artificial Neural Network.
LDR:02481nmm a2200325 4500 001 2205092
005 20190718114220.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438685345
035 $a (MiAaPQ)AAI10981094
035 $a (MiAaPQ)csu:10633
035 $a AAI10981094
040 $a MiAaPQ $c MiAaPQ
100 1 $a Mitra, Sagnik. $3 3431956
245 1 0 $a Modeling and Control of Photovoltaic Array and Wind Turbine for Hybrid Power Generation Using Artificial Neural Network.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 107 p.
500 $a Source: Masters Abstracts International, Volume: 58-02.
500 $a Advisers: Woonki Na; Kerry McBee.
502 $a Thesis (M.S.)--California State University, Fresno, 2018.
520 $a This thesis work concentrates on adaptive control of power transmission from the renewable energy resources such as photovoltaic (PV) system, and wind turbine (WT) system to the grid with the help of artificial neural network (ANN). In this study, a maximum power point (MPP) tracking algorithm from the photovoltaic system and the wind turbine system utilizing the feed-forward artificial neural network were evaluated. Real-time values of solar irradiance and atmospheric temperature were given as inputs to design the neural network algorithm used in the tracking the maximum power of the photovoltaic system. Variation in wind speed was considered to formulate the extraction of the maximum power of the wind turbine. The lithium-ion battery was used as a backup power system. A controlled Battery Energy Storage System (BESS) and bi-directional power flow from the renewable energy resources to the grid were studied. Also, a three-phase inverter control based on the Space Vector Pulse Width Modulation (SVPWM) technique was designed to convert the DC power to the three-phase alternating power and was transmitted to the grid. Lastly, the maximum power from the photovoltaic system, wind turbine system and the state of charge (SOC) of the battery were used as inputs to the neural network for power tracking of the overall hybrid power system with total harmonic distortion analysis. MATLAB/Simulink software was used to validate the proposed system.
590 $a School code: 6050.
650 4 $a Energy. $3 876794
650 4 $a Engineering. $3 586835
650 4 $a Alternative Energy. $3 1035473
650 4 $a Electrical engineering. $3 649834
690 $a 0791
690 $a 0537
690 $a 0363
690 $a 0544
710 2 $a California State University, Fresno. $b Electrical and Computer Engineering. $3 3431957
773 0 $t Masters Abstracts International $g 58-02(E).
790 $a 6050
791 $a M.S.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10981094