Current Search: Electric power distribution (x)
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- Title
- Electromagnetic field emissions from underwater power cables.
- Creator
- DiBiasio, Christopher, Dhanak, Manhar R., Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
- Abstract/Description
-
This study is performed as a partial aid to a larger study that aims to determine if electromagnetic fields produced by underwater power cables have any effect on marine species. In this study, a new numerical method for calculating magnetic fields around subsea power cables is presented and tested. The numerical method is derived from electromagnetic theory, and the program, Matlab, is implemented in order to run the simulations. The Matlab code is validated by performing a series of tests...
Show moreThis study is performed as a partial aid to a larger study that aims to determine if electromagnetic fields produced by underwater power cables have any effect on marine species. In this study, a new numerical method for calculating magnetic fields around subsea power cables is presented and tested. The numerical method is derived from electromagnetic theory, and the program, Matlab, is implemented in order to run the simulations. The Matlab code is validated by performing a series of tests in which the theoretical code is compared with other previously validated magnetic field solvers. Three main tests are carried out; two of these tests are physical and involve the use of a magnetometer, and the third is numerical and compares the code with another numerical model known as Ansys. The data produced by the Matlab code remains consistent with the measured values from both the magnetometer and the Ansys program; thus, the code is considered valid. The validated Matlab code can then be implemented into other parts of the study in order to plot the magnetic field around a specific power cable.
Show less - Date Issued
- 2014
- PURL
- http://purl.flvc.org/fau/fd/FA00004277, http://purl.flvc.org/fau/fd/FA00004277
- Subject Headings
- DIstributed generation of electric power, Electromagnetic interference, Electromagnetic theory, Ocean energy resources -- Environmental aspects
- Format
- Document (PDF)
- Title
- Electric Power Distribution Systems: Optimal Forecasting of Supply-Demand Performance and Assessment of Technoeconomic Tariff Profile.
- Creator
- Melendez, Roxana, De Groff, Dolores, Neelakanta, Perambur, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
This study is concerned with the analyses of modern electric power-grids designed to support large supply-demand considerations in metro areas of large cities. Hence proposed are methods to determine optimal performance of the associated distribution networks vis-á-vis power availability from multiple resources (such as hydroelectric, thermal, wind-mill, solar-cell etc.) and varying load-demands posed by distinct set of consumers of domestic, industrial and commercial sectors. Hence,...
Show moreThis study is concerned with the analyses of modern electric power-grids designed to support large supply-demand considerations in metro areas of large cities. Hence proposed are methods to determine optimal performance of the associated distribution networks vis-á-vis power availability from multiple resources (such as hydroelectric, thermal, wind-mill, solar-cell etc.) and varying load-demands posed by distinct set of consumers of domestic, industrial and commercial sectors. Hence, developing the analytics on optimal power-distribution across pertinent power-grids are verified with the models proposed. Forecast algorithms and computational outcomes on supply-demand performance are indicated and illustratively explained using real-world data sets. This study on electric utility takes duly into considerations of both deterministic (technological factors) as well as stochastic variables associated with the available resource-capacity and demand-profile details. Thus, towards forecasting exercise as above, a representative load-curve (RLC) is defined; and, it is optimally determined using an Artificial Neural Network (ANN) method using the data availed on supply-demand characteristics of a practical power-grid. This RLC is subsequently considered as an input parametric profile on tariff policies associated with electric power product-cost. This research further focuses on developing an optimal/suboptimal electric-power distribution scheme across power-grids deployed between multiple resources and different sets of user demands. Again, the optimal/suboptimal decisions are enabled using ANN-based simulations performed on load sharing details. The underlying supply-demand forecasting on distribution service profile is essential to support predictive designs on the amount of power required (or to be generated from single and/or multiple resources) versus distributable shares to different consumers demanding distinct loads. Another topic addressed refers to a business model on a cost reflective tariff levied in an electric power service in terms of the associated hedonic heuristics of customers versus service products offered by the utility operators. This model is based on hedonic considerations and technoeconomic heuristics of incumbent systems In the ANN simulations as above, bootstrapping technique is adopted to generate pseudo-replicates of the available data set and they are used to train the ANN net towards convergence. A traditional, multilayer ANN architecture (implemented with feed-forward and backpropagation techniques) is designed and modified to support a fast convergence algorithm, used for forecasting and in load-sharing computations. Underlying simulations are carried out using case-study details on electric utility gathered from the literature. In all, ANN-based prediction of a representative load-curve to assess power-consumption and tariff details in electrical power systems supporting a smart-grid, analysis of load-sharing and distribution of electric power on smart grids using an ANN and evaluation of electric power system infrastructure in terms of tariff worthiness deduced via hedonic heuristics, constitute the major thematic efforts addressed in this research study.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013242
- Subject Headings
- Electric power distribution, Supply and demand--Forecasting, Artificial neural networks, Tariff
- Format
- Document (PDF)