Advanced Reactive Power Compensation of Wind Power Plant Using PMU Data
This paper introduces a new model to improve the wind power plant performance by modeling its reactive power demand. It develops a probabilistic model based on prediction interval to help better modeling of the reactive power demands of wind unit which needs to be compensated by the static VAr compe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9416666/ |
| _version_ | 1818672265626124288 |
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| author | Zhen Wang Baohua Zhang Mohammadamin Mobtahej Aliasghar Baziar Baseem Khan |
| author_facet | Zhen Wang Baohua Zhang Mohammadamin Mobtahej Aliasghar Baziar Baseem Khan |
| author_sort | Zhen Wang |
| collection | DOAJ |
| description | This paper introduces a new model to improve the wind power plant performance by modeling its reactive power demand. It develops a probabilistic model based on prediction interval to help better modeling of the reactive power demands of wind unit which needs to be compensated by the static VAr compensator (SVC). This is made possible by the use of a non-parametric neural network (NN) based model using the lower and upper bound estimation (LUBE) method. To avoid the instability arising due to the nonlinear and complex nature of NN, the idea of combined prediction intervals is used here. Due to the highly nonlinear and non-stationary characteristics of the reactive power pattern consumed in the wind power plant, a new optimization algorithm based on <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>-symbiotic organisms search (<inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>-SOS) is proposed to train the LUBE model parameters in the polar coordinates. In addition, a two-phase modification method is developed to enhance the local search ability of SOS and avoid premature convergence issue. The performance of the proposed model on the experimental Phasor Measurement Unit (PMU) data of a wind unit shows that the model can help to improve the performance of the wind SVC, effectively. |
| first_indexed | 2024-12-17T07:37:09Z |
| format | Article |
| id | doaj.art-9d1713a69415426482aab3ade01251c2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T07:37:09Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9d1713a69415426482aab3ade01251c22022-12-21T21:58:17ZengIEEEIEEE Access2169-35362021-01-019670066701410.1109/ACCESS.2021.30759669416666Advanced Reactive Power Compensation of Wind Power Plant Using PMU DataZhen Wang0https://orcid.org/0000-0002-9163-1654Baohua Zhang1Mohammadamin Mobtahej2Aliasghar Baziar3Baseem Khan4https://orcid.org/0000-0002-0562-0933Xiamen Great Power Geo Information Technology Company Ltd., Xiamen, ChinaXiamen Great Power Geo Information Technology Company Ltd., Xiamen, ChinaSchool of Electrical Engineering, Islamic Azad University, Kazerun, IranSchool of Electrical Engineering, Islamic Azad University Sarvestan, Sarvestan, IranDepartment of Electrical Engineering, Hawassa University, Awasa, EthiopiaThis paper introduces a new model to improve the wind power plant performance by modeling its reactive power demand. It develops a probabilistic model based on prediction interval to help better modeling of the reactive power demands of wind unit which needs to be compensated by the static VAr compensator (SVC). This is made possible by the use of a non-parametric neural network (NN) based model using the lower and upper bound estimation (LUBE) method. To avoid the instability arising due to the nonlinear and complex nature of NN, the idea of combined prediction intervals is used here. Due to the highly nonlinear and non-stationary characteristics of the reactive power pattern consumed in the wind power plant, a new optimization algorithm based on <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>-symbiotic organisms search (<inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>-SOS) is proposed to train the LUBE model parameters in the polar coordinates. In addition, a two-phase modification method is developed to enhance the local search ability of SOS and avoid premature convergence issue. The performance of the proposed model on the experimental Phasor Measurement Unit (PMU) data of a wind unit shows that the model can help to improve the performance of the wind SVC, effectively.https://ieeexplore.ieee.org/document/9416666/Wind unitoptimization<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">θ</italic>-symbiotic organisms searchprediction |
| spellingShingle | Zhen Wang Baohua Zhang Mohammadamin Mobtahej Aliasghar Baziar Baseem Khan Advanced Reactive Power Compensation of Wind Power Plant Using PMU Data IEEE Access Wind unit optimization <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">θ</italic>-symbiotic organisms search prediction |
| title | Advanced Reactive Power Compensation of Wind Power Plant Using PMU Data |
| title_full | Advanced Reactive Power Compensation of Wind Power Plant Using PMU Data |
| title_fullStr | Advanced Reactive Power Compensation of Wind Power Plant Using PMU Data |
| title_full_unstemmed | Advanced Reactive Power Compensation of Wind Power Plant Using PMU Data |
| title_short | Advanced Reactive Power Compensation of Wind Power Plant Using PMU Data |
| title_sort | advanced reactive power compensation of wind power plant using pmu data |
| topic | Wind unit optimization <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">θ</italic>-symbiotic organisms search prediction |
| url | https://ieeexplore.ieee.org/document/9416666/ |
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