System wide MV distribution network technical losses estimation based on reference feeder and energy flow model
Electric losses; Electric power utilization; Energy dissipation; Feeding; Analytical equations; Energy flow modeling; Estimation approaches; Integrated analytical approaches; Load distributions; Medium-voltage distribution networks; MV distribution networks; Reference network; Electric power distrib...
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2023
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my.uniten.dspace-230362023-05-29T14:37:31Z System wide MV distribution network technical losses estimation based on reference feeder and energy flow model Ibrahim K.A. Au M.T. Gan C.K. Tang J.H. 55337003600 9742020600 35179398400 55865690900 Electric losses; Electric power utilization; Energy dissipation; Feeding; Analytical equations; Energy flow modeling; Estimation approaches; Integrated analytical approaches; Load distributions; Medium-voltage distribution networks; MV distribution networks; Reference network; Electric power distribution This paper presents an integrated analytical approach to estimate technical losses (TL) of medium voltage (MV) distribution network. The concept of energy flow in a radial MV distribution network is modelled using representative feeders (RF) characterized by feeder peak power demand, feeder length, load distribution, and load factor to develop the generic analytical TL equations. The TL estimation approach is applied to typical utility MV distribution network equipped with energy meters at transmission/distribution interface substation (TDIS) which register monthly inflow energy and peak power demand to the distribution networks. Additional input parameters for the TL estimation are from the feeder ammeters of the outgoing primary and secondary MV feeders. The developed models have been demonstrated through case study performed on a utility MV distribution network supplied from grid source through a TDIS with a registered total maximum demand of 44.9�MW, connected to four (4) 33�kV feeders, four (4) 33/11�kV 30 MVA transformers, and twelve (12) 11�kV feeders. The result shows close agreement with TL provided by the local power utility company. With RF, the approach could be extended and applied to estimate TL of any radial MV distribution network of different sizes and demography. � 2017 Elsevier Ltd Final 2023-05-29T06:37:31Z 2023-05-29T06:37:31Z 2017 Article 10.1016/j.ijepes.2017.06.011 2-s2.0-85021161623 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021161623&doi=10.1016%2fj.ijepes.2017.06.011&partnerID=40&md5=77afe76b052155de1177fa7926c001a9 https://irepository.uniten.edu.my/handle/123456789/23036 93 440 450 Elsevier Ltd Scopus |
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| description |
Electric losses; Electric power utilization; Energy dissipation; Feeding; Analytical equations; Energy flow modeling; Estimation approaches; Integrated analytical approaches; Load distributions; Medium-voltage distribution networks; MV distribution networks; Reference network; Electric power distribution |
| author2 |
55337003600 |
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55337003600 Ibrahim K.A. Au M.T. Gan C.K. Tang J.H. |
| format |
Article |
| author |
Ibrahim K.A. Au M.T. Gan C.K. Tang J.H. |
| spellingShingle |
Ibrahim K.A. Au M.T. Gan C.K. Tang J.H. System wide MV distribution network technical losses estimation based on reference feeder and energy flow model |
| author_sort |
Ibrahim K.A. |
| title |
System wide MV distribution network technical losses estimation based on reference feeder and energy flow model |
| title_short |
System wide MV distribution network technical losses estimation based on reference feeder and energy flow model |
| title_full |
System wide MV distribution network technical losses estimation based on reference feeder and energy flow model |
| title_fullStr |
System wide MV distribution network technical losses estimation based on reference feeder and energy flow model |
| title_full_unstemmed |
System wide MV distribution network technical losses estimation based on reference feeder and energy flow model |
| title_sort |
system wide mv distribution network technical losses estimation based on reference feeder and energy flow model |
| publisher |
Elsevier Ltd |
| publishDate |
2023 |
| _version_ |
1806426245369430016 |
| score |
13.214268 |