Identification of a synchronous generator parameters using recursive least squares and Kalman filter
The comparison between recursive least squares (RLS) and Kalman filter (KF) is presented in this paper, both methods were adequate to estimate six parameters of a synchronous machine. The work focused on finding the operating conditions which the quality of the identification achieved with Kalman fi...
| Main Authors: | , , , |
|---|---|
| Format: | Online |
| Language: | spa |
| Published: |
Universidad Pedagógica y Tecnológica de Colombia
2021
|
| Subjects: | |
| Online Access: | https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/11779 |
| _version_ | 1801706347909087232 |
|---|---|
| author | Bravo Montenegro, Diego Alberto Rengifo, Carlos Felipe Giron, Cristian Palechor, Jhon |
| author_facet | Bravo Montenegro, Diego Alberto Rengifo, Carlos Felipe Giron, Cristian Palechor, Jhon |
| author_sort | Bravo Montenegro, Diego Alberto |
| collection | OJS |
| description | The comparison between recursive least squares (RLS) and Kalman filter (KF) is presented in this paper, both methods were adequate to estimate six parameters of a synchronous machine. The work focused on finding the operating conditions which the quality of the identification achieved with Kalman filter is better than recursive least squares. A linear model of the machine is used in order to considerate the currents and their derivatives as the system inputs while the three-phase voltage signals are the outputs. Furthermore two experiments with simulated and measured data were carried out, three operating scenarios and two variations of the algorithms respectively were considered. Despite the great similarity and good performance of both methods, it was found that Kalman filter slightly exceeded least squares due to the fact that it presented smaller oscillations in the estimated value of the parameters for any operating condition. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-11779 |
| institution | Revista Ciencia en Desarrollo |
| language | spa |
| publishDate | 2021 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-117792021-12-14T02:35:17Z Identification of a synchronous generator parameters using recursive least squares and Kalman filter Identificación de los parámetros de un generador síncrono mediante mínimos cuadrados recursivos y filtro de Kalman Bravo Montenegro, Diego Alberto Rengifo, Carlos Felipe Giron, Cristian Palechor, Jhon Identificación, Modelo Dinámico, Filtro de Kalman, Mínimos Cuadrados Recursivos. Identification, Dynamic Model, Kalman Filter, Recursive least squares. The comparison between recursive least squares (RLS) and Kalman filter (KF) is presented in this paper, both methods were adequate to estimate six parameters of a synchronous machine. The work focused on finding the operating conditions which the quality of the identification achieved with Kalman filter is better than recursive least squares. A linear model of the machine is used in order to considerate the currents and their derivatives as the system inputs while the three-phase voltage signals are the outputs. Furthermore two experiments with simulated and measured data were carried out, three operating scenarios and two variations of the algorithms respectively were considered. Despite the great similarity and good performance of both methods, it was found that Kalman filter slightly exceeded least squares due to the fact that it presented smaller oscillations in the estimated value of the parameters for any operating condition. En este articulo se presenta la comparación entre mínimos cuadrados recursivos (RLS) y filtro de Kalman (KF), ambos métodos fueron adecuados para estimar seis parámetros de una máquina síncrona. El trabajo se centró en encontrar las condiciones de funcionamiento en las que la calidad de la identificación lograda con el filtro de Kalman es mejor que los mínimos cuadrados recursivos. Se utiliza un modelo lineal de la máquina para considerar las corrientes y sus derivadas como entradas del sistema, mientras que las señales de tensión trifásica son las salidas. Además, se llevaron a cabo dos experimentos con datos simulados y medidos, se consideraron tres escenarios operativos y dos variaciones de los algoritmos respectivamente. A pesar de la gran similitud y buen desempeño de ambos métodos, se encontró que el filtro de Kalman excedía levemente los mínimos cuadrados debido a que presentaba menores oscilaciones en el valor estimado de los parámetros para cualquier condición de operación. Universidad Pedagógica y Tecnológica de Colombia 2021-01-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/11779 10.19053/01217488.v12.n1.2021.11779 Ciencia En Desarrollo; Vol. 12 No. 1 (2021): Vol 12, Núm.1 (2021): Enero-Junio; 13-21 Ciencia en Desarrollo; Vol. 12 Núm. 1 (2021): Vol 12, Núm.1 (2021): Enero-Junio; 13-21 2462-7658 0121-7488 spa https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/11779/10690 Derechos de autor 2021 CIENCIA EN DESARROLLO |
| spellingShingle | Identificación, Modelo Dinámico, Filtro de Kalman, Mínimos Cuadrados Recursivos. Identification, Dynamic Model, Kalman Filter, Recursive least squares. Bravo Montenegro, Diego Alberto Rengifo, Carlos Felipe Giron, Cristian Palechor, Jhon Identification of a synchronous generator parameters using recursive least squares and Kalman filter |
| title | Identification of a synchronous generator parameters using recursive least squares and Kalman filter |
| title_alt | Identificación de los parámetros de un generador síncrono mediante mínimos cuadrados recursivos y filtro de Kalman |
| title_full | Identification of a synchronous generator parameters using recursive least squares and Kalman filter |
| title_fullStr | Identification of a synchronous generator parameters using recursive least squares and Kalman filter |
| title_full_unstemmed | Identification of a synchronous generator parameters using recursive least squares and Kalman filter |
| title_short | Identification of a synchronous generator parameters using recursive least squares and Kalman filter |
| title_sort | identification of a synchronous generator parameters using recursive least squares and kalman filter |
| topic | Identificación, Modelo Dinámico, Filtro de Kalman, Mínimos Cuadrados Recursivos. Identification, Dynamic Model, Kalman Filter, Recursive least squares. |
| topic_facet | Identificación, Modelo Dinámico, Filtro de Kalman, Mínimos Cuadrados Recursivos. Identification, Dynamic Model, Kalman Filter, Recursive least squares. |
| url | https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/11779 |
| work_keys_str_mv | AT bravomontenegrodiegoalberto identificationofasynchronousgeneratorparametersusingrecursiveleastsquaresandkalmanfilter AT rengifocarlosfelipe identificationofasynchronousgeneratorparametersusingrecursiveleastsquaresandkalmanfilter AT gironcristian identificationofasynchronousgeneratorparametersusingrecursiveleastsquaresandkalmanfilter AT palechorjhon identificationofasynchronousgeneratorparametersusingrecursiveleastsquaresandkalmanfilter AT bravomontenegrodiegoalberto identificaciondelosparametrosdeungeneradorsincronomedianteminimoscuadradosrecursivosyfiltrodekalman AT rengifocarlosfelipe identificaciondelosparametrosdeungeneradorsincronomedianteminimoscuadradosrecursivosyfiltrodekalman AT gironcristian identificaciondelosparametrosdeungeneradorsincronomedianteminimoscuadradosrecursivosyfiltrodekalman AT palechorjhon identificaciondelosparametrosdeungeneradorsincronomedianteminimoscuadradosrecursivosyfiltrodekalman |
