Non stationary‐ARMA flicker model for squirrel cage induction generators based wind farms
Abstract Time varying nature of the wind power is studied previously considering different time intervals from seconds to days. However for power quality problems such as flicker, a model which considers the extremely fast variations is essential. Here by using large number of actual records, a time...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-05-01
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| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.12132 |
| _version_ | 1797994908768796672 |
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| author | Haidar Samet Saeedeh Ketabipour Ali Asghar Bagheri |
| author_facet | Haidar Samet Saeedeh Ketabipour Ali Asghar Bagheri |
| author_sort | Haidar Samet |
| collection | DOAJ |
| description | Abstract Time varying nature of the wind power is studied previously considering different time intervals from seconds to days. However for power quality problems such as flicker, a model which considers the extremely fast variations is essential. Here by using large number of actual records, a time‐varying model is proposed which considers the extremely short‐time variations of wind active and reactive powers. The wind farm is modelled as a current source with time varying amplitude and phase which change every 0.01 s. Autoregressive moving‐average (ARMA) models are utilized to model the variations and ARMA coefficients are calculated for every record. Same to the actual behaviour, the proposed model is non‐stationary as the ARMA order and coefficients are different at every run of the model. The proposed model is confirmed through utilizing several applications which need the power time series with extremely short sampling intervals. The following studies are performed by using the actual data and then the proposed model: power spectral density of active and reactive power variations, instantaneous flicker, short term flicker (Pst), estimating the Pst using the maximum value of the instantaneous flicker, estimation of cumulative Pst for multiple wind turbines, and the impact of SVC on flicker mitigation. |
| first_indexed | 2024-04-11T09:52:01Z |
| format | Article |
| id | doaj.art-91c861f8532041b2a38a176c967f381b |
| institution | Directory Open Access Journal |
| issn | 1752-1416 1752-1424 |
| language | English |
| last_indexed | 2024-04-11T09:52:01Z |
| publishDate | 2021-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj.art-91c861f8532041b2a38a176c967f381b2022-12-22T04:30:45ZengWileyIET Renewable Power Generation1752-14161752-14242021-05-011571542156310.1049/rpg2.12132Non stationary‐ARMA flicker model for squirrel cage induction generators based wind farmsHaidar Samet0Saeedeh Ketabipour1Ali Asghar Bagheri2School of Electrical and Computer Engineering Shiraz University Shiraz IranSchool of Electrical and Computer Engineering Shiraz University Shiraz IranDepartment of Electrical Engineering Dashtestan Branch Islamic Azad University Dashtestan IranAbstract Time varying nature of the wind power is studied previously considering different time intervals from seconds to days. However for power quality problems such as flicker, a model which considers the extremely fast variations is essential. Here by using large number of actual records, a time‐varying model is proposed which considers the extremely short‐time variations of wind active and reactive powers. The wind farm is modelled as a current source with time varying amplitude and phase which change every 0.01 s. Autoregressive moving‐average (ARMA) models are utilized to model the variations and ARMA coefficients are calculated for every record. Same to the actual behaviour, the proposed model is non‐stationary as the ARMA order and coefficients are different at every run of the model. The proposed model is confirmed through utilizing several applications which need the power time series with extremely short sampling intervals. The following studies are performed by using the actual data and then the proposed model: power spectral density of active and reactive power variations, instantaneous flicker, short term flicker (Pst), estimating the Pst using the maximum value of the instantaneous flicker, estimation of cumulative Pst for multiple wind turbines, and the impact of SVC on flicker mitigation.https://doi.org/10.1049/rpg2.12132Wind power plantsAsynchronous machinesControl of electric power systemsOther topics in statisticsOther topics in statistics |
| spellingShingle | Haidar Samet Saeedeh Ketabipour Ali Asghar Bagheri Non stationary‐ARMA flicker model for squirrel cage induction generators based wind farms IET Renewable Power Generation Wind power plants Asynchronous machines Control of electric power systems Other topics in statistics Other topics in statistics |
| title | Non stationary‐ARMA flicker model for squirrel cage induction generators based wind farms |
| title_full | Non stationary‐ARMA flicker model for squirrel cage induction generators based wind farms |
| title_fullStr | Non stationary‐ARMA flicker model for squirrel cage induction generators based wind farms |
| title_full_unstemmed | Non stationary‐ARMA flicker model for squirrel cage induction generators based wind farms |
| title_short | Non stationary‐ARMA flicker model for squirrel cage induction generators based wind farms |
| title_sort | non stationary arma flicker model for squirrel cage induction generators based wind farms |
| topic | Wind power plants Asynchronous machines Control of electric power systems Other topics in statistics Other topics in statistics |
| url | https://doi.org/10.1049/rpg2.12132 |
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