TY  - GEN
N2  - Enhancement of Grid-Connected Photovoltaic Systems Using Artificial Intelligence presents methods for monitoring transmission systems and enhancing distribution system performance using modern optimization techniques considering different multi-objective functions such as voltage loss sensitivity indexes, reducing total annual cost, and voltage deviation. The authors offer a comprehensive survey of distributed energy resources (DERs), explain the backward/forward sweep (BFS) power flow algorithm, and present simulation results on the optimal integration of photovoltaic-based distributed generators (PV-DG) and distribution static synchronous compensators (DSTATCOM) in different transmission and distribution systems. This book will be a valuable academic and industry resource for electrical engineers, students, and researchers working on optimization techniques, photovoltaic systems, energy engineering, and artificial intelligence. Covers developments to enhance the integration of renewable energy sources; Presents simulation results, including standard IEEE bus test systems; Includes MATLAB M-file codes.
DO  - 10.1007/978-3-031-29692-5
DO  - doi
AB  - Enhancement of Grid-Connected Photovoltaic Systems Using Artificial Intelligence presents methods for monitoring transmission systems and enhancing distribution system performance using modern optimization techniques considering different multi-objective functions such as voltage loss sensitivity indexes, reducing total annual cost, and voltage deviation. The authors offer a comprehensive survey of distributed energy resources (DERs), explain the backward/forward sweep (BFS) power flow algorithm, and present simulation results on the optimal integration of photovoltaic-based distributed generators (PV-DG) and distribution static synchronous compensators (DSTATCOM) in different transmission and distribution systems. This book will be a valuable academic and industry resource for electrical engineers, students, and researchers working on optimization techniques, photovoltaic systems, energy engineering, and artificial intelligence. Covers developments to enhance the integration of renewable energy sources; Presents simulation results, including standard IEEE bus test systems; Includes MATLAB M-file codes.
T1  - Enhancement of grid-connected photovoltaic systems using artificial intelligence /
AU  - El-Hameid, Amal M. Abd,
AU  - Elbaset, Adel A.,
AU  - Ebeed, Mohamed,
AU  - Abdelsattar, Montaser,
CN  - TK1087
ID  - 1468023
KW  - Photovoltaic power generation.
KW  - Artificial intelligence
SN  - 9783031296925
SN  - 3031296923
TI  - Enhancement of grid-connected photovoltaic systems using artificial intelligence /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-29692-5
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-29692-5
ER  -