@article{1468023,
      author = {El-Hameid, Amal M. Abd, and Elbaset, Adel A., and Ebeed,  Mohamed, and Abdelsattar, Montaser,},
      url = {http://library.usi.edu/record/1468023},
      title = {Enhancement of grid-connected photovoltaic systems using  artificial intelligence /},
      abstract = {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.},
      doi = {https://doi.org/10.1007/978-3-031-29692-5},
      recid = {1468023},
      pages = {1 online resource (xxiii, 226 pages) :},
}