@article{MAKHILLIJSC20149121181,
    title = {Hybrid ANN Based Optimal Power Flow and its Validation for Deregulated Environment},
    journal = {International Journal of Soft Computing},
    volume = {9},
    number = {1},
    pages = {51-57},
    year = {2014},
    issn = {1816-9503},
    doi = {ijscomp.2014.51.57},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2014.51.57},
    author = {R. and},
    keywords = {artificial neural network,gradient descent,Optimal power flow,deregulation,error weightage},
    abstract = {Optimal Power Flow (OPF) is essential and is employed to attain desired performance 
  in any field of engineering. It plays an important role in power system operation 
  and planning. In deregulated environment of power sector, it is of increasing 
  importance for determination of electricity prices. Traditional optimization 
  techniques have limitations in obtaining the global solution. With a non-monotonic 
  solution surface, classical methods are highly sensitive to starting points 
  and frequently converge to local optimal solution or diverge altogether. This 
  study describes an artificial neural network-GD based optimal power flow which 
  is highly constrained non convex optimization problems with multiple objective, 
  the fuel cost and compares its results with well known classical methods in 
  order to prove its validity for present deregulated power system analysis.}
    }