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EnglishThe problem of electrical parameters identification in complex systems, and in particular in electric railway traction systems, is considered. Parameters are determined by an indirect approach: only the terminal variables (voltages and currents and, impedance and admittance, which can be readily calculated) are measured and the per-unit-length electrical parameters are determined using a multiconductor transmission line model of the track section under test. It will be shown that some parameters cannot be measured directly, that they are not constant with frequency and that they may depend on other external conditions. An indirect method for parameters identification is proposed through an adaptive algorithm (AA), so that the calculated terminal variables match the measured ones. The AA is based on a genetic algorithm (GA) approach, with details on pattern generation (through mutation and recombination) and determination of the degree of fitness and, as a consequence, of matching and stop criteria. There is a general accordance with model previsions, within common variations of the most critical properties of the test track.
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| Titolo: | Genetic Algorithm approach for the determination of the electrical parameters of railway traction lines |
| Autori: | |
| Data di pubblicazione: | 2008 |
| Rivista: | |
| Abstract: | The problem of electrical parameters identification in complex systems, and in particular in electric railway traction systems, is considered. Parameters are determined by an indirect approach: only the terminal variables (voltages and currents and, impedance and admittance, which can be readily calculated) are measured and the per-unit-length electrical parameters are determined using a multiconductor transmission line model of the track section under test. It will be shown that some parameters cannot be measured directly, that they are not constant with frequency and that they may depend on other external conditions. An indirect method for parameters identification is proposed through an adaptive algorithm (AA), so that the calculated terminal variables match the measured ones. The AA is based on a genetic algorithm (GA) approach, with details on pattern generation (through mutation and recombination) and determination of the degree of fitness and, as a consequence, of matching and stop criteria. There is a general accordance with model previsions, within common variations of the most critical properties of the test track. |
| Handle: | http://hdl.handle.net/11567/230850 |
| Appare nelle tipologie: | 01.01 - Articolo su rivista |
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