Use of adaptive linear algorithms for very short-term prediction of wind turbine power output
The paper proposes an efficient method for very short-term prediction of wind turbine power output. The method, which models the turbine as a Hammerstein system, exploits an adaptive linear filtering algorithm. The performance of the proposed method is examined by implementation of two linear adapti...
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| creator | Tohidian, M. Esmaili, A. Naghizadeh, R-A Sadeghi, S. H. H. Nasiri, A. Reza, A. M. |
| description | The paper proposes an efficient method for very short-term prediction of wind turbine power output. The method, which models the turbine as a Hammerstein system, exploits an adaptive linear filtering algorithm. The performance of the proposed method is examined by implementation of two linear adaptive algorithms, namely, least mean squares (LMS) and recursive least squares (RLS) filters. Using synthetic generation of turbine power output, it is shown that the RLS algorithm gives more accurate results with moderate computational burden as compared to the LMS algorithm and rival artificial neural networks. |
| doi_str_mv | 10.1109/IECON.2012.6388608 |
| format | Conference Proceeding |
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Using synthetic generation of turbine power output, it is shown that the RLS algorithm gives more accurate results with moderate computational burden as compared to the LMS algorithm and rival artificial neural networks.</description><subject>Adaptation models</subject><subject>Artificial neural networks</subject><subject>Least squares approximation</subject><subject>Turbines</subject><subject>Wind power generation</subject><issn>1553-572X</issn><isbn>9781467324199</isbn><isbn>1467324191</isbn><isbn>1467324213</isbn><isbn>9781467324205</isbn><isbn>9781467324212</isbn><isbn>1467324205</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkM9Kw0AYxFdUsNa8gF72BRJ3v_2T5Cil1kKxFwtepGyyX-xK2g2bTUvf3oo5DQPzG5gh5JGzjHNWPi_ns_V7BoxDpkVRaFZckXsudS5AAhfXJCnzYvS8LG_IhCslUpXD5x1J-v6HMcY5SKHZhHxteqS-ocaaLroj0tYd0ARq2m8fXNzte9r4QI8YzrTf-RDTiGFPu4DW1dH5wx98cgdL4xCqC0s7f8JA_RC7IT6Q28a0PSajTsnmdf4xe0tX68Vy9rJKHc9VTG3R1JxpUCVIU1elBQZNA5IJY7WspABjtCmUqi4RaRG0uqzTFtHkqmZaTMnTf69DxG0X3N6E83Z8R_wCXuVXyA</recordid><startdate>201210</startdate><enddate>201210</enddate><creator>Tohidian, M.</creator><creator>Esmaili, A.</creator><creator>Naghizadeh, R-A</creator><creator>Sadeghi, S. 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M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tohidian, M.</au><au>Esmaili, A.</au><au>Naghizadeh, R-A</au><au>Sadeghi, S. H. H.</au><au>Nasiri, A.</au><au>Reza, A. 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| ispartof | IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, 2012, p.1162-1165 |
| issn | 1553-572X |
| language | eng |
| recordid | cdi_ieee_primary_6388608 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Adaptation models Artificial neural networks Least squares approximation Turbines Wind power generation |
| title | Use of adaptive linear algorithms for very short-term prediction of wind turbine power output |
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