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| Title: | Impedance identification of integrated power system components using recurrent neural networks. | |
| Author (s): | Xiao, Peng Venayagamoorthy, Ganesh K. Corzine, Keith | |
| Department/Lab Affiliations: | Electrical and Computer Engineering Energy Research and Development Center Power Systems Laboratory Real-Time Power and Intelligent Systems Laboratory | |
| Keywords: | naval engineering computing power electronics power engineering computing recurrent neural nets ships | |
| Issue Date: | 2007-05 | |
| Publisher: | Institute of Electrical and Electronics Engineers IEEE | |
| Citation: | Xiao, Peng, Venayagamoorthy GK, Corzine KA. "Impedance identification of integrated power system components using recurrent neural networks." 2007 IEEE Electric Ship Technologies Symposium. May 2007: 48-52. | |
| Abstract: | Impedance characteristics of shipboard power systems provide important information for studies on system stability and integration. Existing injection based impedance measurement techniques require multiple tests on the system to obtain characteristics over wide frequency ranges. In this paper, recurrent neural networks (RNNs) are used to model the small signal dynamics of power electronic systems based on a single test in which randomized signals are injected into the system. The trained RNN is then used to extract the small-signal impedances/admittances of the system. A number of tests have been carried out in simulation to verify the effectiveness of the proposed method. | |
| Type: | Article - Conference proceedings text | |
| In Title: | Electric Ship Technologies Symposium | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. can upload final version FULL COPYRIGHT INFORMATION: | |
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| title | Impedance identification of integrated power system components using recurrent neural networks. | |
| contributor.author | Xiao, Peng | |
| contributor.author | Venayagamoorthy, Ganesh K. | |
| contributor.author | Corzine, Keith | |
| contributor.deptlab | Electrical and Computer Engineering | |
| contributor.deptlab | Energy Research and Development Center | |
| contributor.deptlab | Power Systems Laboratory | |
| contributor.deptlab | Real-Time Power and Intelligent Systems Laboratory | |
| subject | naval engineering computing | |
| subject | power electronics | |
| subject | power engineering computing | |
| subject | recurrent neural nets | |
| subject | ships | |
| date.issued | 2007-05 | |
| publisher | Institute of Electrical and Electronics Engineers IEEE | |
| identifier.citation | Xiao, Peng, Venayagamoorthy GK, Corzine KA. "Impedance identification of integrated power system components using recurrent neural networks." 2007 IEEE Electric Ship Technologies Symposium. May 2007: 48-52. | |
| identifier.pub.URI | ||
| description.abstract | Impedance characteristics of shipboard power systems provide important information for studies on system stability and integration. Existing injection based impedance measurement techniques require multiple tests on the system to obtain characteristics over wide frequency ranges. In this paper, recurrent neural networks (RNNs) are used to model the small signal dynamics of power electronic systems based on a single test in which randomized signals are injected into the system. The trained RNN is then used to extract the small-signal impedances/admittances of the system. A number of tests have been carried out in simulation to verify the effectiveness of the proposed method. | |
| type | Article - Conference proceedings | |
| type.DCMIType | text | |
| type.status | Final version | |
| relation.isPartOf | Electric Ship Technologies Symposium | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights | can upload final version | |
| rights.URI | ||
| rights.URI | ||
| rights.URI | ||
| date.accessioned | 2009-01-21T20:39:18Z | |
| date.available | 2009-01-23T21:51:36Z | |
| identifier.persist.URI | ||
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