Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis
There are mainly two most essential problems in power networks, load frequency control and power flow management, which are grown recently because of growth in dimension/complication of grids. Present work suggests a controller based on fuzzy systems in which controller design is performed in a supe...
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Veröffentlicht in: | Computational intelligence 2020-02, Vol.36 (1), p.225-258 |
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description | There are mainly two most essential problems in power networks, load frequency control and power flow management, which are grown recently because of growth in dimension/complication of grids. Present work suggests a controller based on fuzzy systems in which controller design is performed in a supervisory manner over a multiagent system aiming to control the frequency variation as well as generation cost minimization in the entire grid. The designing processes for low‐frequency controller (LFC) and management are mostly performed separately, which results in the disruption of both outputs. This challenge is tackled in this paper by the integration of them in the designing process. Additionally, stability guarantee is in high importance in the power systems, which is neglected in most of the related works. The Gaussian particle swarm optimization (GPSO) algorithm is applied for determining the optimal values of the decision variables, which can also guarantee the stability of the system by adopting a chaotic map by Gaussian function to balance the seeking abilities of particles that promotes the computation effectiveness without affecting the efficiency of the fuzzy controller. Then, the stability situationof the fuzzy + GPSO method is derived that guarantees a suitable global exploration and rapid convergence, with no require to gradients. |
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Present work suggests a controller based on fuzzy systems in which controller design is performed in a supervisory manner over a multiagent system aiming to control the frequency variation as well as generation cost minimization in the entire grid. The designing processes for low‐frequency controller (LFC) and management are mostly performed separately, which results in the disruption of both outputs. This challenge is tackled in this paper by the integration of them in the designing process. Additionally, stability guarantee is in high importance in the power systems, which is neglected in most of the related works. The Gaussian particle swarm optimization (GPSO) algorithm is applied for determining the optimal values of the decision variables, which can also guarantee the stability of the system by adopting a chaotic map by Gaussian function to balance the seeking abilities of particles that promotes the computation effectiveness without affecting the efficiency of the fuzzy controller. 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Present work suggests a controller based on fuzzy systems in which controller design is performed in a supervisory manner over a multiagent system aiming to control the frequency variation as well as generation cost minimization in the entire grid. The designing processes for low‐frequency controller (LFC) and management are mostly performed separately, which results in the disruption of both outputs. This challenge is tackled in this paper by the integration of them in the designing process. Additionally, stability guarantee is in high importance in the power systems, which is neglected in most of the related works. The Gaussian particle swarm optimization (GPSO) algorithm is applied for determining the optimal values of the decision variables, which can also guarantee the stability of the system by adopting a chaotic map by Gaussian function to balance the seeking abilities of particles that promotes the computation effectiveness without affecting the efficiency of the fuzzy controller. Then, the stability situationof the fuzzy + GPSO method is derived that guarantees a suitable global exploration and rapid convergence, with no require to gradients.</description><subject>Algorithms</subject><subject>Chaos theory</subject><subject>Control stability</subject><subject>Control systems design</subject><subject>Controllers</subject><subject>Disruption</subject><subject>Frequency control</subject><subject>Frequency variation</subject><subject>Fuzzy control</subject><subject>Fuzzy systems</subject><subject>GPSO</subject><subject>load frequency control</subject><subject>MIMO controller</subject><subject>multiagent system</subject><subject>Multiagent systems</subject><subject>Particle swarm optimization</subject><subject>Power flow</subject><subject>power flow management</subject><subject>Stability analysis</subject><issn>0824-7935</issn><issn>1467-8640</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kc9qGzEQxkVpIK7TS55A0FtgU2kleXePxTiJwY0Dbc-LVpbsMdrVVtJi1qc8Qh6lz9QnqVz7nDnMH_h9MwMfQreU3NMUX5WD7p7muSg-oAnlsyIrZ5x8RBNS5jwrKiau0acQ9oQQyng5QX8WbW_dCN0WS_wohxBAdvhF-gjKavzjIH2L132EFo4ygutwq-PObbBxHsehOwm_DzYCXnb9EC_9eohp-Pv6ZobjccRhDFG3WAacdkMX9dbLqDdYuS56Z6322Jl0vwXlXVJtPWzwAeIOhygbsBDHpJR2DBBu0JWRNujPlzpFvx4WP-dP2Wr9uJx_W2WKEVpktKBF2VQlEcY0XGmiFDU0Zc1E1cy4Kg1lSoqGNLQ0heaNYIznXKhZXvFcsyn6ct7be_d70CHWezf49ESocybySpCCskTdnan0eAhem7r30Eo_1pTUJ0fqkyP1f0cSTM_wAawe3yHr-Xr5fNb8A19plIA</recordid><startdate>202002</startdate><enddate>202002</enddate><creator>Mir, Mahdi</creator><creator>Dayyani, Mohammad</creator><creator>Sutikno, Tole</creator><creator>Mohammadi Zanjireh, Morteza</creator><creator>Razmjooy, Navid</creator><general>John Wiley & Sons, Inc</general><general>Blackwell Publishing Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-9321-8943</orcidid></search><sort><creationdate>202002</creationdate><title>Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis</title><author>Mir, Mahdi ; Dayyani, Mohammad ; Sutikno, Tole ; Mohammadi Zanjireh, Morteza ; Razmjooy, Navid</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3017-17178b9805ffb4ce0cc1f10cce359b64c8f13ca5b0b18f7e4b5334245c62942e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Chaos theory</topic><topic>Control stability</topic><topic>Control systems design</topic><topic>Controllers</topic><topic>Disruption</topic><topic>Frequency control</topic><topic>Frequency variation</topic><topic>Fuzzy control</topic><topic>Fuzzy systems</topic><topic>GPSO</topic><topic>load frequency control</topic><topic>MIMO controller</topic><topic>multiagent system</topic><topic>Multiagent systems</topic><topic>Particle swarm optimization</topic><topic>Power flow</topic><topic>power flow management</topic><topic>Stability analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mir, Mahdi</creatorcontrib><creatorcontrib>Dayyani, Mohammad</creatorcontrib><creatorcontrib>Sutikno, Tole</creatorcontrib><creatorcontrib>Mohammadi Zanjireh, Morteza</creatorcontrib><creatorcontrib>Razmjooy, Navid</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computational intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mir, Mahdi</au><au>Dayyani, Mohammad</au><au>Sutikno, Tole</au><au>Mohammadi Zanjireh, Morteza</au><au>Razmjooy, Navid</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis</atitle><jtitle>Computational intelligence</jtitle><date>2020-02</date><risdate>2020</risdate><volume>36</volume><issue>1</issue><spage>225</spage><epage>258</epage><pages>225-258</pages><issn>0824-7935</issn><eissn>1467-8640</eissn><abstract>There are mainly two most essential problems in power networks, load frequency control and power flow management, which are grown recently because of growth in dimension/complication of grids. 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The Gaussian particle swarm optimization (GPSO) algorithm is applied for determining the optimal values of the decision variables, which can also guarantee the stability of the system by adopting a chaotic map by Gaussian function to balance the seeking abilities of particles that promotes the computation effectiveness without affecting the efficiency of the fuzzy controller. Then, the stability situationof the fuzzy + GPSO method is derived that guarantees a suitable global exploration and rapid convergence, with no require to gradients.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1111/coin.12257</doi><tpages>34</tpages><orcidid>https://orcid.org/0000-0002-9321-8943</orcidid></addata></record> |
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subjects | Algorithms Chaos theory Control stability Control systems design Controllers Disruption Frequency control Frequency variation Fuzzy control Fuzzy systems GPSO load frequency control MIMO controller multiagent system Multiagent systems Particle swarm optimization Power flow power flow management Stability analysis |
title | Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis |
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