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Non-intrusive efficiency estimation of induction machines under various power supplies
Considering that 45% of the world's generated electricity is consumed by induction machines, determining an induction motors efficiency non-intrusively is of great importance in that it enables the machine to operate productively whilst ensuring that the energy consumed by the machine is utilized ef...
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| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2015
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| _version_ | 1867613221092851712 |
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| access_status_str | Open Access |
| author | Gajjar, Chetan Sudhir |
| author2 | Khan, Azeem |
| author_browse | Gajjar, Chetan Sudhir Khan, Azeem |
| author_facet | Khan, Azeem Gajjar, Chetan Sudhir |
| author_sort | Gajjar, Chetan Sudhir |
| collection | Thesis |
| description | Considering that 45% of the world's generated electricity is consumed by induction machines, determining an induction motors efficiency non-intrusively is of great importance in that it enables the machine to operate productively whilst ensuring that the energy consumed by the machine is utilized efficiently. International efficiency testing methods such as the IEEE 112-B can determine a motors efficiency accurately at the cost of hindering the machines productivity. Alternatively, various methods used to determine a machines efficiency in-situ do so at the cost of accuracy. This research proposes a method that determines an induction machines efficiency over a range of load conditions from tests conducted and centered around one thermally stable load point in the least intrusive manner possible. Coupled with vibration sensors used to determine a motor's speed, measured input voltages and currents are used to deduce a machine efficiency-load profile through the use of a modified evolutionary algorithm, the Non-Intrusive Efficiency Estimation using Population-Based Incremental Learning(NIEE-PBIL) algorithm. Five temporal load measurements are taken, centered around one thermally stable load point, to determine the machines efficiency profile from two equivalent circuit implementations; the Standard Circuit NIEE-PBIL and the Iron-Loss NIEE-PBIL. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/14138 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:41.376Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/14138 Non-intrusive efficiency estimation of induction machines under various power supplies Gajjar, Chetan Sudhir Khan, Azeem Barendse, Paul Stanley Electrical Engineering Considering that 45% of the world's generated electricity is consumed by induction machines, determining an induction motors efficiency non-intrusively is of great importance in that it enables the machine to operate productively whilst ensuring that the energy consumed by the machine is utilized efficiently. International efficiency testing methods such as the IEEE 112-B can determine a motors efficiency accurately at the cost of hindering the machines productivity. Alternatively, various methods used to determine a machines efficiency in-situ do so at the cost of accuracy. This research proposes a method that determines an induction machines efficiency over a range of load conditions from tests conducted and centered around one thermally stable load point in the least intrusive manner possible. Coupled with vibration sensors used to determine a motor's speed, measured input voltages and currents are used to deduce a machine efficiency-load profile through the use of a modified evolutionary algorithm, the Non-Intrusive Efficiency Estimation using Population-Based Incremental Learning(NIEE-PBIL) algorithm. Five temporal load measurements are taken, centered around one thermally stable load point, to determine the machines efficiency profile from two equivalent circuit implementations; the Standard Circuit NIEE-PBIL and the Iron-Loss NIEE-PBIL. 2015-10-06T13:59:17Z 2015-10-06T13:59:17Z 2013 Master Thesis Masters MSc (Eng) http://hdl.handle.net/11427/14138 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering Gajjar, Chetan Sudhir Non-intrusive efficiency estimation of induction machines under various power supplies |
| thesis_degree_str | Master's |
| title | Non-intrusive efficiency estimation of induction machines under various power supplies |
| title_full | Non-intrusive efficiency estimation of induction machines under various power supplies |
| title_fullStr | Non-intrusive efficiency estimation of induction machines under various power supplies |
| title_full_unstemmed | Non-intrusive efficiency estimation of induction machines under various power supplies |
| title_short | Non-intrusive efficiency estimation of induction machines under various power supplies |
| title_sort | non intrusive efficiency estimation of induction machines under various power supplies |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/14138 |
| work_keys_str_mv | AT gajjarchetansudhir nonintrusiveefficiencyestimationofinductionmachinesundervariouspowersupplies |