EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 216 (2020) E3S Web Conf., 216 (2020) 01102 References
Open Access
Issue
E3S Web Conf.
Volume 216, 2020
Rudenko International Conference “Methodological problems in reliability study of large energy systems” (RSES 2020)
Article Number 01102
Number of page(s) 6
DOI https://doi.org/10.1051/e3sconf/202021601102
Published online 14 December 2020
  1. Idelchik V.I. Elektricheskie sistemy i seti [Electrical systems and networks]. Moscow: Energoatomizdat Publ., 1989, 592 p. (In Russian). [Google Scholar]
  2. Ortega D., Reynboldt V. Iteracionnye metody resheniya nelinejnyh sistem uravnenij so mnogimi neizvestnymi [Iterative methods for solving nonlinear equation systems with many unknowns]. Moscow: World Publ., 1975, 560 p. (In Russian). [Google Scholar]
  3. Gajendra Sahu and Kuldeep Swarnkar. Review of Reactive Power Optimization Using Evolutionary Computation Techniques. Advance in Electronic and Electric Engineering, 2014, no. 4, pp. 73–82. [Google Scholar]
  4. Solopov R.V., Samulchenkov A.S. The use of a genetic algorithm for calculating the steady state of an electrical network. Bulletin of Irkutsk State Technical University.2018. Т. 22. № 2. С. 131–141. DOI: 10.21285/1814-3520-2018-2-131-141 (In Russian). [Google Scholar]
  5. S. Pourya Hoseini Alinodehi, Sushil J. Louis, Sajjad Moshfe, Mircea Nicolescu. A Modified Steady State Genetic Algorithm Suitable for Fast Pipelined Hardware. 2017 IEEE Congress on Evolutionary Computation (CEC) 5-8 June 2017 DOI:10.1109/CEC.2017.7969612 [Google Scholar]
  6. Draidi Abdellah; Labed Djamel. Power flow analysis using adaptive neuro-fuzzy inference systems 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC) 10-13 Dec. 2015 DOI:10.1109/IRSEC.2015.7455102 [Google Scholar]
  7. Draidi Abdellah; Labed Djamel Power flow analysis using adaptive neuro-fuzzy inference systems. 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC) 10-13 Dec. 2015 DOI:10.1109/IRSEC.2015.7455102 [Google Scholar]
  8. S. G. Ankaliki, S. G. Gollagi Power System Steady State Monitoring Using Artificial Neural Network. Journal of Engineering and Technology, Jan-Jun 2011, Vol 1, Issue 1, Page: 4–9 [CrossRef] [Google Scholar]
  9. N Aishwarya, Manjula S Sureban. Analysis of Steady State Stability of Power System using Artificial Neural Network International Journal of Scientific & Engineering Research Volume 10, Issue 5, May-2019 ISSN 2229-5518 [Google Scholar]
  10. R. E. Bourguet, P. J. Antsaklis, “Artificial Neural Networks in Electric Power Industry,” Technical Report of the ISIS (Interdisciplinary Studies of Intelligent Systems) Group, No. ISIS-94-007, Univ of Notre Dame, April 1994. [Google Scholar]
  11. Krishna J, Srivastava L, Counterpropagation Neural Network for Solving Power Flow Problem, International Journal of Intelligent Systems and Technologies, Winter 2006, Page 57–62 [Google Scholar]
  12. Beale, M.H., Hagan, M.T. and Demuth, H.B. (2014) Neural Network ToolboxTM, User’s Guide. MATLAB®, MathWorks, Natick. [Google Scholar]
  13. T.Kh. Nasyrov “Foundations of the general theory of normal and emergency modes of power systems.” Fan va Technology Publishing House - 2015, 224 p. (In Russian). [Google Scholar]
  14. Yu H. Advanced Learning Algorithms of Neural Networks: PhD. dissertation./H. Yu. - USA: Graduate Faculty of Auburn University, 2011-130 p. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.