An improved Rao algorithm for frequency stability enhancement of nonlinear power system interconnected by AC/DC links with high renewables penetration
In this paper, an improved optimization algorithm is proposed to overcome the original Rao algorithm limitations (i.e., different characteristics in exploration and exploitation) and enhance the performance of the original Rao algorithm. In the improved algorithm, the self-adaptive multi-population...
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Veröffentlicht in: | Neural computing & applications 2022-02, Vol.34 (4), p.2883-2911 |
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creator | Khamies, Mohamed Magdy, Gaber Selim, Ali Kamel, Salah |
description | In this paper, an improved optimization algorithm is proposed to overcome the original Rao algorithm limitations (i.e., different characteristics in exploration and exploitation) and enhance the performance of the original Rao algorithm. In the improved algorithm, the self-adaptive multi-population and Levy flight methods are utilized in the original Rao algorithm. The improved algorithm is called I_Rao_3. The improved algorithm’s efficiency is confirmed by comparing it to the original Rao algorithm utilizing various standard benchmark test functions. Moreover, the proposed I_Rao_3 algorithm is utilized to improve the frequency response in a hybrid renewable power grid by fine-tuning the proportional-integral-derivative (PID) controller parameters. The targeted system used for this study is a hybrid power grid, which encompasses conventional generating stations (i.e., thermal power plants), renewable power stations (i.e., PV and wind power stations) for the analysis of the load frequency control (LFC) issue. Unlike other previously published works, this study considers the impact of DC links in parallel to AC links to interconnect the two-hybrid renewable power system area. In addition, the nonlinearities effects (i.e., generation rate constraint and a governor dead band) are applied to each area in order to achieve a more realistic study. The superiority of the proposed PID controller-based I_Rao_3 algorithm is endorsed by comparing its performance with many other optimization algorithms. |
doi_str_mv | 10.1007/s00521-021-06545-y |
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In the improved algorithm, the self-adaptive multi-population and Levy flight methods are utilized in the original Rao algorithm. The improved algorithm is called I_Rao_3. The improved algorithm’s efficiency is confirmed by comparing it to the original Rao algorithm utilizing various standard benchmark test functions. Moreover, the proposed I_Rao_3 algorithm is utilized to improve the frequency response in a hybrid renewable power grid by fine-tuning the proportional-integral-derivative (PID) controller parameters. The targeted system used for this study is a hybrid power grid, which encompasses conventional generating stations (i.e., thermal power plants), renewable power stations (i.e., PV and wind power stations) for the analysis of the load frequency control (LFC) issue. Unlike other previously published works, this study considers the impact of DC links in parallel to AC links to interconnect the two-hybrid renewable power system area. In addition, the nonlinearities effects (i.e., generation rate constraint and a governor dead band) are applied to each area in order to achieve a more realistic study. The superiority of the proposed PID controller-based I_Rao_3 algorithm is endorsed by comparing its performance with many other optimization algorithms.</description><identifier>ISSN: 0941-0643</identifier><identifier>EISSN: 1433-3058</identifier><identifier>DOI: 10.1007/s00521-021-06545-y</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Adaptive algorithms ; Algorithms ; Alternative energy sources ; Artificial Intelligence ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Controllers ; Data Mining and Knowledge Discovery ; Electric power systems ; Frequency control ; Frequency response ; Frequency stability ; Hybrid systems ; Image Processing and Computer Vision ; Links ; Nonlinearity ; Optimization ; Original Article ; Power plants ; Probability and Statistics in Computer Science ; Proportional integral derivative ; Thermal power plants ; Wind power</subject><ispartof>Neural computing & applications, 2022-02, Vol.34 (4), p.2883-2911</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-df03fae16e96e6d871d95bef018a02698a1c49a910a34ec046c405413d8ad8f73</citedby><cites>FETCH-LOGICAL-c319t-df03fae16e96e6d871d95bef018a02698a1c49a910a34ec046c405413d8ad8f73</cites><orcidid>0000-0001-6894-695X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00521-021-06545-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00521-021-06545-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27902,27903,41466,42535,51296</link.rule.ids></links><search><creatorcontrib>Khamies, Mohamed</creatorcontrib><creatorcontrib>Magdy, Gaber</creatorcontrib><creatorcontrib>Selim, Ali</creatorcontrib><creatorcontrib>Kamel, Salah</creatorcontrib><title>An improved Rao algorithm for frequency stability enhancement of nonlinear power system interconnected by AC/DC links with high renewables penetration</title><title>Neural computing & applications</title><addtitle>Neural Comput & Applic</addtitle><description>In this paper, an improved optimization algorithm is proposed to overcome the original Rao algorithm limitations (i.e., different characteristics in exploration and exploitation) and enhance the performance of the original Rao algorithm. In the improved algorithm, the self-adaptive multi-population and Levy flight methods are utilized in the original Rao algorithm. The improved algorithm is called I_Rao_3. The improved algorithm’s efficiency is confirmed by comparing it to the original Rao algorithm utilizing various standard benchmark test functions. Moreover, the proposed I_Rao_3 algorithm is utilized to improve the frequency response in a hybrid renewable power grid by fine-tuning the proportional-integral-derivative (PID) controller parameters. The targeted system used for this study is a hybrid power grid, which encompasses conventional generating stations (i.e., thermal power plants), renewable power stations (i.e., PV and wind power stations) for the analysis of the load frequency control (LFC) issue. Unlike other previously published works, this study considers the impact of DC links in parallel to AC links to interconnect the two-hybrid renewable power system area. In addition, the nonlinearities effects (i.e., generation rate constraint and a governor dead band) are applied to each area in order to achieve a more realistic study. The superiority of the proposed PID controller-based I_Rao_3 algorithm is endorsed by comparing its performance with many other optimization algorithms.</description><subject>Adaptive algorithms</subject><subject>Algorithms</subject><subject>Alternative energy sources</subject><subject>Artificial Intelligence</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computational Science and Engineering</subject><subject>Computer Science</subject><subject>Controllers</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Electric power systems</subject><subject>Frequency control</subject><subject>Frequency response</subject><subject>Frequency stability</subject><subject>Hybrid systems</subject><subject>Image Processing and Computer Vision</subject><subject>Links</subject><subject>Nonlinearity</subject><subject>Optimization</subject><subject>Original Article</subject><subject>Power plants</subject><subject>Probability and Statistics in Computer Science</subject><subject>Proportional integral derivative</subject><subject>Thermal power plants</subject><subject>Wind power</subject><issn>0941-0643</issn><issn>1433-3058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kM9q4zAQxsXSQrNtX6CngZ69GVmyYx9D9k8LhULZPRtFHiXK2lIqqRv8In3elcnC3noYZmB-3zfMx9gdxy8ccbWMiFXJC5yrrmRVTJ_YgkshCoFVc8EW2Mp5JcUV-xzjARFl3VQL9r52YMdj8H-ohxflQQ07H2zaj2B8ABPo9Y2cniAmtbWDTROQ2yunaSSXwBtw3g3WkQpw9CcKEKeYaATrEgXtnSOdsvV2gvVm-XUDmf0d4ZQvwN7u9hDI0UltB4pwzGMKKlnvbtilUUOk23_9mv36_u3n5qF4ev7xuFk_FVrwNhW9QWEU8Zramuq-WfG-rbZkkDcKy7ptFNeyVS1HJSTp_LOWWEku-kb1jVmJa3Z_9s0J5Edj6g7-Lbh8sivrsuY84zNVnikdfIyBTHcMdlRh6jh2c_7dOf8O55rz76YsEmdRzLDbUfhv_YHqL_AkjFE</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Khamies, Mohamed</creator><creator>Magdy, Gaber</creator><creator>Selim, Ali</creator><creator>Kamel, Salah</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0001-6894-695X</orcidid></search><sort><creationdate>20220201</creationdate><title>An improved Rao algorithm for frequency stability enhancement of nonlinear power system interconnected by AC/DC links with high renewables penetration</title><author>Khamies, Mohamed ; Magdy, Gaber ; Selim, Ali ; Kamel, Salah</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-df03fae16e96e6d871d95bef018a02698a1c49a910a34ec046c405413d8ad8f73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adaptive algorithms</topic><topic>Algorithms</topic><topic>Alternative energy sources</topic><topic>Artificial Intelligence</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computational Science and Engineering</topic><topic>Computer Science</topic><topic>Controllers</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Electric power systems</topic><topic>Frequency control</topic><topic>Frequency response</topic><topic>Frequency stability</topic><topic>Hybrid systems</topic><topic>Image Processing and Computer Vision</topic><topic>Links</topic><topic>Nonlinearity</topic><topic>Optimization</topic><topic>Original Article</topic><topic>Power plants</topic><topic>Probability and Statistics in Computer Science</topic><topic>Proportional integral derivative</topic><topic>Thermal power plants</topic><topic>Wind power</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khamies, Mohamed</creatorcontrib><creatorcontrib>Magdy, Gaber</creatorcontrib><creatorcontrib>Selim, Ali</creatorcontrib><creatorcontrib>Kamel, Salah</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Neural computing & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Khamies, Mohamed</au><au>Magdy, Gaber</au><au>Selim, Ali</au><au>Kamel, Salah</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An improved Rao algorithm for frequency stability enhancement of nonlinear power system interconnected by AC/DC links with high renewables penetration</atitle><jtitle>Neural computing & applications</jtitle><stitle>Neural Comput & Applic</stitle><date>2022-02-01</date><risdate>2022</risdate><volume>34</volume><issue>4</issue><spage>2883</spage><epage>2911</epage><pages>2883-2911</pages><issn>0941-0643</issn><eissn>1433-3058</eissn><abstract>In this paper, an improved optimization algorithm is proposed to overcome the original Rao algorithm limitations (i.e., different characteristics in exploration and exploitation) and enhance the performance of the original Rao algorithm. In the improved algorithm, the self-adaptive multi-population and Levy flight methods are utilized in the original Rao algorithm. The improved algorithm is called I_Rao_3. The improved algorithm’s efficiency is confirmed by comparing it to the original Rao algorithm utilizing various standard benchmark test functions. Moreover, the proposed I_Rao_3 algorithm is utilized to improve the frequency response in a hybrid renewable power grid by fine-tuning the proportional-integral-derivative (PID) controller parameters. The targeted system used for this study is a hybrid power grid, which encompasses conventional generating stations (i.e., thermal power plants), renewable power stations (i.e., PV and wind power stations) for the analysis of the load frequency control (LFC) issue. Unlike other previously published works, this study considers the impact of DC links in parallel to AC links to interconnect the two-hybrid renewable power system area. In addition, the nonlinearities effects (i.e., generation rate constraint and a governor dead band) are applied to each area in order to achieve a more realistic study. The superiority of the proposed PID controller-based I_Rao_3 algorithm is endorsed by comparing its performance with many other optimization algorithms.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00521-021-06545-y</doi><tpages>29</tpages><orcidid>https://orcid.org/0000-0001-6894-695X</orcidid></addata></record> |
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subjects | Adaptive algorithms Algorithms Alternative energy sources Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Controllers Data Mining and Knowledge Discovery Electric power systems Frequency control Frequency response Frequency stability Hybrid systems Image Processing and Computer Vision Links Nonlinearity Optimization Original Article Power plants Probability and Statistics in Computer Science Proportional integral derivative Thermal power plants Wind power |
title | An improved Rao algorithm for frequency stability enhancement of nonlinear power system interconnected by AC/DC links with high renewables penetration |
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