Alamgeer (2017) Maintenance optimization of wind farms in Australia. [USQ Project]
Abstract
Wind energy is acceptable growing source of energy for Australia and for region whose struggle to minimize the emission of greenhouse gases and lessen the output of global warming. The always rise energy need and the commitment from authority all over the countries, coupled with universal agreements like the Kyoto Protocol to minimize the emission of greenhouse gases, has outcome in a quick growth in wind power production. As the operation and maintenance stage remains for long term period during the life cycle of asset, the efficient maintenance strategies are critical to keep the asset life cycle cost to minimal. The usual maintenance procedure applied to wind farm industry are insufficient to support the present commercial drivers of the wind farm so a comprehensive model is proposed. Maintenance optimization is a way to decide the most financially savvy support procedure. The three functional failure type are used to identify the condition of wind farm components either they are critical or failed, which are; (1): Overall loss of power/energy, (2): Partial loss of power/energy, (3): Irregular power/energy due to over speeding. Almost all electrical components function, field failure, failure mood and cause are discussed and suitable maintenance task are suggested. Related failure and maintenance information of wind farm are gathered and examined utilizing by Reliability Centered Maintenance (RCM) technique called Failure Mode and Effect Analysis (FMEA) and Preventive Maintenance techniques called Time Based Maintenance (TBM) and Conditioned Based Maintenance (CBM). The components failure information are received from SCADA system then the failure and critical components data are analysis and select suitable approach for maintenance. The whole outcome shows that FMEA approach are suitable for failure components, TBM for periodic maintenance and CBM are effective approach for critical vi components maintenance. By comparing of these three approaches it shows that CBM is the most effective approach.
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| Item Type: | USQ Project |
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| Item Status: | Live Archive |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021) |
| Supervisors: | Nateque Mahmood, Narottam Das |
| Qualification: | Master of Engineering Sciences (Electrical and Electronics) |
| Date Deposited: | 31 Oct 2024 05:54 |
| Last Modified: | 31 Oct 2024 06:09 |
| URI: | https://sear.unisq.edu.au/id/eprint/52085 |
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