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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Alabduljabbar, Ayman A. Smiai, M. Varnham, A. |
| Copyright Year | 2010 |
| Description | Author affiliation: Energy Research Institute at King Abdulaziz City for Science and Technology. P.O.Box 6086, Riyadh 11442, Saudi Arabia (Alabduljabbar, Ayman A.; Smiai, M.; Varnham, A.) |
| Abstract | The negative impact of low power factors on power networks is significant and profound particularly at the distribution level. Inadequate voltage profiles, increased network losses, and the overheating of machines and equipment are some of the well-known side effects of low power factors. Regulations, financial penalties and incentives, and technical solutions have all been used to improve low power factor levels. In this paper, a multiplier-based approach for low power factor penalties is proposed by means of genetic algorithm optimization and payback periods. Inherent to the proposed method are the financial and the technical concerns of both the electricity service provider and the customer. Moreover, the study helps with underling the factors that have to be considered when designing the penalties for low power factor in order to eliminate the wide difference in the adopted power factor regulations around the world. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 454686 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424465491 |
| ISSN | 19449925 |
| e-ISBN | 9781424465514 |
| e-ISBN | 9781424483570 |
| DOI | 10.1109/PES.2010.5589811 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-07-25 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Reactive power Industrial plants Electricity Loading Economics Energy consumption Capacitors Power Factor Multipliers Low Power Factor Penalties Genetic Algorithm Payback Period Power Factor Regulations |
| Content Type | Text |
| Resource Type | Article |
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