Well-being analysis for composite generation and transmission systems
This paper presents a new approach to evaluating the health of composite generation and transmission systems. A well-being framework is used to classify the system states into healthy, marginal and at risk, according to a pre-defined deterministic criterion. In order to combine deterministic and pro...
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| Veröffentlicht in: | IEEE transactions on power systems 2004-11, Vol.19 (4), p.1763-1770 |
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| container_issue | 4 |
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| container_title | IEEE transactions on power systems |
| container_volume | 19 |
| creator | da Silva, A.M.L. de Resende, L.C. da Fonseca Manso, L.A. Billinton, R. |
| description | This paper presents a new approach to evaluating the health of composite generation and transmission systems. A well-being framework is used to classify the system states into healthy, marginal and at risk, according to a pre-defined deterministic criterion. In order to combine deterministic and probabilistic concepts, the proposed methodology uses a nonsequential Monte Carlo simulation, a multilevel nonaggregate Markov load model and new test functions to estimate the well-being indices. These test functions are based on an estimating process, designated as the one-step forward state transition, which is very flexible and efficient. Case studies on the IEEE-RTS (Reliability Test System) and on a modification of this system are presented and discussed. |
| doi_str_mv | 10.1109/TPWRS.2004.835633 |
| format | Article |
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A well-being framework is used to classify the system states into healthy, marginal and at risk, according to a pre-defined deterministic criterion. In order to combine deterministic and probabilistic concepts, the proposed methodology uses a nonsequential Monte Carlo simulation, a multilevel nonaggregate Markov load model and new test functions to estimate the well-being indices. These test functions are based on an estimating process, designated as the one-step forward state transition, which is very flexible and efficient. 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A well-being framework is used to classify the system states into healthy, marginal and at risk, according to a pre-defined deterministic criterion. In order to combine deterministic and probabilistic concepts, the proposed methodology uses a nonsequential Monte Carlo simulation, a multilevel nonaggregate Markov load model and new test functions to estimate the well-being indices. These test functions are based on an estimating process, designated as the one-step forward state transition, which is very flexible and efficient. Case studies on the IEEE-RTS (Reliability Test System) and on a modification of this system are presented and discussed.</description><subject>Classification</subject><subject>Composite reliability</subject><subject>Computer simulation</subject><subject>Criteria</subject><subject>Estimates</subject><subject>health analysis</subject><subject>Load modeling</subject><subject>Markov processes</subject><subject>Mathematical models</subject><subject>Monte Carlo methods</subject><subject>Monte Carlo simulation</subject><subject>Multilevel</subject><subject>Power system analysis computing</subject><subject>Power system measurements</subject><subject>Power system planning</subject><subject>Power system reliability</subject><subject>Power system simulation</subject><subject>Process design</subject><subject>Reliability engineering</subject><subject>State estimation</subject><subject>Testing</subject><subject>well-being analysis</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kE1Lw0AQhhdRsFZ_gHgJHvSUOpv9yO5RSv2AgqKVHsMmuykpabbupIf-e7dGEDx4Gph53mHmIeSSwoRS0HeL1-Xb-yQD4BPFhGTsiIyoECoFmetjMgKlRKq0gFNyhrgGABkHIzJburZNS9d0q8R0pt1jg0ntQ1L5zdZj07tk5ToXTN_4LhI26YPpcNMgHhq4x95t8Jyc1KZFd_FTx-TjYbaYPqXzl8fn6f08rZhgfaptqVkuwWSl4Jxb7lxdGlpXOq_AqswB0FwaZjIw1ipTlRKs5KWSIKzRlo3J7bB3G_znzmFfxEOq-IHpnN9hobSkKud5Fsmbf8lMSc5lLiN4_Qdc-12IJuI2xZSQwHSE6ABVwSMGVxfb0GxM2BcUioP_4tt_cfBfDP5j5mrINM65X54JUDRjXxjagkE</recordid><startdate>20041101</startdate><enddate>20041101</enddate><creator>da Silva, A.M.L.</creator><creator>de Resende, L.C.</creator><creator>da Fonseca Manso, L.A.</creator><creator>Billinton, R.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Classification Composite reliability Computer simulation Criteria Estimates health analysis Load modeling Markov processes Mathematical models Monte Carlo methods Monte Carlo simulation Multilevel Power system analysis computing Power system measurements Power system planning Power system reliability Power system simulation Process design Reliability engineering State estimation Testing well-being analysis |
| title | Well-being analysis for composite generation and transmission systems |
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