DINAMIČKA REKONFIGURACIJA DISTRIBUTIVNE MREŽE PRIMENOM UČENJA POTKREPLJIVANJEM
Ključne reči:
dinamička rekonfiguracija, učenje potkrepljivanjem
Apstrakt
U radu je obrađena dinamička rekonfiguracija distributivne mreže primenom učenja potkrepljivanjem. Problem je modelovan višekriterijumskom funkcijom cilja koja minimizira gubitke energije i gubitke usled manipulacija prekidačima. Broj informacija neophodnih za izvršenje algoritma je redukovan, s obzirom da su topološke informacije i informacije o tokovima snaga smeštene u jedan skup promenljivih. Ovo smanjuje količinu telemetrisanih merenja potrebnih za potencijalno izvršenje algoritma na realnoj mreži i pogodno je sa aspekta treninga, zbog toga što je smanjena potrebna veličina neuronske mreže.
Reference
[1] G.Švenda: Specijalizovani softveri u elektroenergetici, skripta sa predavanja iz istoimenog predmeta na master studijama, Fakultet Tehničkih Nauka, Novi Sad, 2019.
[2] Taleski R, Rajicic D. Distribution network reconfiguration for energy loss reduction. IEEE Transactions on Power Systems 1997; 1:398–406.
[3] V.Fathi, H.Seyedi, B.M.Ivatloo: Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties; Int. Trans Electr Energ Systems; February 2020;pp. 1-25; DOI: 10.1002/2050-7038.12346
[4] Kovački NV, Vidović PM, Sarić AT. Scalable algorithm for the dynamic reconfiguration of the distribution network using the Lagrange relaxation approach. International Journal of Electrical Power & Energy Systems 2018; 94:188–202.
[5] Sutton RS, Barto AG. Reinforcement learning: An introduction. MIT press, 2018.
[6] https://searchenterpriseai.techtarget.com/definition/deep-learning-agent 10.09.2020.
[7] Nikolić M, Zečević A. Mašinsko učenje, Matematički fakultet, Univerzitet u Beogradu, 2019.
[8] R. Dugan and T. McDermott. An open source platform for collaborating on smart grid research. Power and Energy Society General Meeting 2011 IEEE 2011; 1–7.
[9] Ramos ER, Exposito AG, Santos JR, Iborra FL. Path-based distribution network modeling: application to reconfiguration for loss reduction. IEEE Transactions on Power Systems 2005; 2:556–564.
[2] Taleski R, Rajicic D. Distribution network reconfiguration for energy loss reduction. IEEE Transactions on Power Systems 1997; 1:398–406.
[3] V.Fathi, H.Seyedi, B.M.Ivatloo: Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties; Int. Trans Electr Energ Systems; February 2020;pp. 1-25; DOI: 10.1002/2050-7038.12346
[4] Kovački NV, Vidović PM, Sarić AT. Scalable algorithm for the dynamic reconfiguration of the distribution network using the Lagrange relaxation approach. International Journal of Electrical Power & Energy Systems 2018; 94:188–202.
[5] Sutton RS, Barto AG. Reinforcement learning: An introduction. MIT press, 2018.
[6] https://searchenterpriseai.techtarget.com/definition/deep-learning-agent 10.09.2020.
[7] Nikolić M, Zečević A. Mašinsko učenje, Matematički fakultet, Univerzitet u Beogradu, 2019.
[8] R. Dugan and T. McDermott. An open source platform for collaborating on smart grid research. Power and Energy Society General Meeting 2011 IEEE 2011; 1–7.
[9] Ramos ER, Exposito AG, Santos JR, Iborra FL. Path-based distribution network modeling: application to reconfiguration for loss reduction. IEEE Transactions on Power Systems 2005; 2:556–564.
Objavljeno
2020-12-26
Sekcija
Elektrotehničko i računarsko inženjerstvo
