Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization
A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS syst...
Uloženo v:
| Vydáno v: | ISA transactions Ročník 56; s. 173 - 187 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
Elsevier Ltd
01.05.2015
|
| Témata: | |
| ISSN: | 0019-0578, 1879-2022, 1879-2022 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions.
•A kind of the nonlinear hydraulic turbine regulating system (HTRS) model is studied.•Multi-objective optimization framework for controller design in HTRS is done.•Hyperbolic tangent function is used to approximate the saturation function in HTRS.•Adaptive grid PSO (AGPSO) is applied to the parameters optimization of the HTRS system.•NSGAII and SPEAII are employed to the parameters tuning in HTRS as a comparison. |
|---|---|
| AbstractList | A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. •A kind of the nonlinear hydraulic turbine regulating system (HTRS) model is studied.•Multi-objective optimization framework for controller design in HTRS is done.•Hyperbolic tangent function is used to approximate the saturation function in HTRS.•Adaptive grid PSO (AGPSO) is applied to the parameters optimization of the HTRS system.•NSGAII and SPEAII are employed to the parameters tuning in HTRS as a comparison. A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions.A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. |
| Author | Huang, Yuehua Yuan, Xiaohui Chen, Zhihuan Yuan, Yanbin Li, Xianshan Li, Wenwu |
| Author_xml | – sequence: 1 givenname: Zhihuan surname: Chen fullname: Chen, Zhihuan organization: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, 430074 Wuhan, China – sequence: 2 givenname: Yanbin surname: Yuan fullname: Yuan, Yanbin organization: School of Resource and Environmental Engineering, Wuhan University of Technology, 430070 Wuhan, China – sequence: 3 givenname: Xiaohui surname: Yuan fullname: Yuan, Xiaohui email: yxh71@163.com organization: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, 430074 Wuhan, China – sequence: 4 givenname: Yuehua surname: Huang fullname: Huang, Yuehua organization: College of Electrical Engineering and New Energy, China Three Gorges University, 443002 Yichang, China – sequence: 5 givenname: Xianshan surname: Li fullname: Li, Xianshan organization: College of Electrical Engineering and New Energy, China Three Gorges University, 443002 Yichang, China – sequence: 6 givenname: Wenwu surname: Li fullname: Li, Wenwu organization: College of Electrical Engineering and New Energy, China Three Gorges University, 443002 Yichang, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25481821$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkc1u3CAUhVGVqJmkfYOqYtmNXcA2truoFKV_kSKli3aNAF9PGWFwASeavk3ftMw42XSRiAVC-s65l3PO0YnzDhB6Q0lJCeXvd6WJMgVZMkLrktKSkOoF2tCu7QtGGDtBG0JoX5Cm7c7QeYw7Qghr-u4lOmNN3dGO0Q36eznP1miZjHfYj3habDKFVzvQydwB1t6l4K2FgJPHfk5mkhanxRm3PfDfrz_hrTQu4tEHLPGv_RDkkh0zE5RxgANsF5v9Mx_3McGEl3h4yEHOxxHbYAY8y5CMtoDjvZzWOebPcatX6HSUNsLrh_sC_fzy-cfVt-Lm9uv11eVNoeuKpaImCkAp0krZ9hTGjjfjqHrF6xGU5LxtBs5BVaSuKsr6mtdcM-D5jAMZKqgu0LvVdw7-9wIxiclEDdZKB36JgvKO9pRUbZXRtw_ooiYYxBxyKmEvHmPNwIcV0MHHGGAU2qTjb3JhxgpKxKFDsRNrh-LQoaBU5A6zuP5P_Oj_jOzjKoMc0p2BIKI24DQMJuQyxeDN0wb_AJkDvLU |
| CitedBy_id | crossref_primary_10_1111_exsy_12366 crossref_primary_10_1016_j_isatra_2018_12_007 crossref_primary_10_1016_j_ijepes_2019_04_033 crossref_primary_10_1016_j_renene_2018_10_061 crossref_primary_10_3233_JIFS_232155 crossref_primary_10_1080_21642583_2016_1275990 crossref_primary_10_1109_ACCESS_2018_2847641 crossref_primary_10_1016_j_isatra_2017_07_022 crossref_primary_10_1016_j_asoc_2020_106342 crossref_primary_10_3390_en11051141 crossref_primary_10_53759_acims_978_9914_9946_6_7_13 crossref_primary_10_1177_1748006X19869114 crossref_primary_10_3390_fractalfract8030126 crossref_primary_10_1016_j_renene_2018_08_100 crossref_primary_10_1155_2020_6161784 crossref_primary_10_1080_00207721_2025_2506007 crossref_primary_10_1109_ACCESS_2019_2951628 crossref_primary_10_3390_en14248484 crossref_primary_10_1002_asjc_2432 crossref_primary_10_1109_ACCESS_2018_2871069 crossref_primary_10_1111_itor_12720 crossref_primary_10_1016_j_energy_2016_07_164 crossref_primary_10_1016_j_isatra_2017_01_009 crossref_primary_10_1016_j_isatra_2018_03_017 crossref_primary_10_1016_j_mechmachtheory_2018_04_007 crossref_primary_10_1016_j_renene_2023_02_091 crossref_primary_10_1016_j_renene_2023_01_019 crossref_primary_10_1002_ente_202301170 crossref_primary_10_3390_act13080296 crossref_primary_10_1109_ACCESS_2020_2979810 crossref_primary_10_1016_j_enconman_2018_10_004 crossref_primary_10_3390_machines9090181 crossref_primary_10_1016_j_ifacol_2018_06_113 crossref_primary_10_1016_j_ymssp_2018_03_028 crossref_primary_10_1016_j_isatra_2016_04_020 crossref_primary_10_1049_iet_rpg_2019_1186 crossref_primary_10_3390_en12173220 crossref_primary_10_1109_ACCESS_2021_3064584 crossref_primary_10_3390_en15134771 crossref_primary_10_1016_j_energy_2024_130916 crossref_primary_10_1016_j_renene_2020_05_071 crossref_primary_10_1016_j_enconman_2019_04_068 crossref_primary_10_1109_ACCESS_2021_3095057 crossref_primary_10_3390_su151712810 crossref_primary_10_1007_s12665_019_8257_5 crossref_primary_10_1016_j_energy_2017_02_174 crossref_primary_10_3390_en11010035 crossref_primary_10_1016_j_eswa_2025_128120 crossref_primary_10_1016_j_isatra_2020_09_014 crossref_primary_10_1051_meca_2024002 crossref_primary_10_3390_en11010151 crossref_primary_10_1016_j_est_2022_104315 crossref_primary_10_1109_TPWRS_2016_2639293 crossref_primary_10_1016_j_engappai_2021_104453 crossref_primary_10_1016_j_neucom_2016_05_007 crossref_primary_10_1177_1077546317740013 crossref_primary_10_1155_2019_5826873 crossref_primary_10_1177_10775463211062332 crossref_primary_10_1680_jener_23_00067 crossref_primary_10_1016_j_energy_2024_131798 crossref_primary_10_1016_j_jfranklin_2019_08_006 crossref_primary_10_1016_j_renene_2018_12_082 crossref_primary_10_1016_j_asoc_2021_108192 crossref_primary_10_1016_j_asoc_2016_10_035 crossref_primary_10_1016_j_energy_2018_10_028 |
| Cites_doi | 10.1007/978-3-642-23878-9_16 10.1016/j.enconman.2014.04.052 10.1016/j.eswa.2011.02.050 10.1016/j.protcy.2012.05.139 10.1016/j.energy.2008.09.010 10.1016/j.amc.2013.06.074 10.1007/s00500-010-0541-y 10.1016/S0019-0578(07)60102-5 10.1109/TCST.2012.2185698 10.1016/j.enconman.2010.11.005 10.1016/j.eswa.2008.10.047 10.1016/j.isatra.2011.06.006 10.1016/j.isatra.2013.07.002 10.1016/j.ijepes.2014.02.005 10.1016/j.ijepes.2012.06.034 10.1016/j.conengprac.2013.02.015 10.1016/j.isatra.2012.05.004 10.1016/j.ijepes.2009.11.006 10.1016/j.isatra.2012.09.004 10.1016/j.ijepes.2013.09.029 10.1016/j.ins.2014.04.015 10.1016/j.rser.2005.06.003 10.1016/j.asoc.2014.07.009 10.1016/j.isatra.2013.05.003 10.1109/ICNC.2010.5583639 10.1016/j.enconman.2005.07.009 10.1109/MHS.1995.494215 10.12785/amis/080619 10.1016/j.isatra.2013.04.004 10.1016/j.ijepes.2011.06.002 10.1109/TEVC.2004.826067 10.1007/978-3-642-20499-9_6 10.1016/j.enconman.2013.10.060 |
| ContentType | Journal Article |
| Copyright | 2014 ISA Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved. |
| Copyright_xml | – notice: 2014 ISA – notice: Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved. |
| DBID | AAYXX CITATION NPM 7X8 |
| DOI | 10.1016/j.isatra.2014.11.003 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Sciences (General) |
| EISSN | 1879-2022 |
| EndPage | 187 |
| ExternalDocumentID | 25481821 10_1016_j_isatra_2014_11_003 S0019057814002602 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | --- --K --M -~X .DC .~1 0R~ 1B1 1~. 1~5 29J 4.4 457 4G. 53G 5GY 5VS 6P2 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO ABFNM ABFRF ABJNI ABMAC ABNEU ABTAH ABXDB ABYKQ ACDAQ ACFVG ACGFO ACNNM ACRLP ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFDAS AFFNX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIKHN AITUG AIVDX AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 GBLVA HVGLF HZ~ IHE J1W JJJVA KOM LY7 M41 MO0 N9A O-L O9- OAUVE OGIMB OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SDF SDG SES SET SEW SPC SPCBC SPD SSQ SST SSZ T5K T9H TAE TN5 UHS UNMZH WUQ XPP ZMT ZY4 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD BNPGV NPM SSH 7X8 |
| ID | FETCH-LOGICAL-c432t-40beebb07aa791ef865ffb9b64feba6675d66eb304331294646c2e6e6efd0d3e3 |
| ISICitedReferencesCount | 71 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000356128000017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0019-0578 1879-2022 |
| IngestDate | Thu Oct 02 07:03:21 EDT 2025 Thu Apr 03 07:00:33 EDT 2025 Tue Nov 18 21:19:01 EST 2025 Sat Nov 29 02:28:32 EST 2025 Fri Feb 23 02:32:13 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Hydraulic turbine regulating system Non-dominated Sorting Genetic Algorithm II (NSGAII) Strength Pareto Evolutionary Algorithm II (SPEAII) Multi-objective Adaptive grid particle swarm optimization |
| Language | English |
| License | Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c432t-40beebb07aa791ef865ffb9b64feba6675d66eb304331294646c2e6e6efd0d3e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PMID | 25481821 |
| PQID | 1681910373 |
| PQPubID | 23479 |
| PageCount | 15 |
| ParticipantIDs | proquest_miscellaneous_1681910373 pubmed_primary_25481821 crossref_citationtrail_10_1016_j_isatra_2014_11_003 crossref_primary_10_1016_j_isatra_2014_11_003 elsevier_sciencedirect_doi_10_1016_j_isatra_2014_11_003 |
| PublicationCentury | 2000 |
| PublicationDate | 2015-05-01 |
| PublicationDateYYYYMMDD | 2015-05-01 |
| PublicationDate_xml | – month: 05 year: 2015 text: 2015-05-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | ISA transactions |
| PublicationTitleAlternate | ISA Trans |
| PublicationYear | 2015 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Kanasottu AN, Pullabhatla S, Mettu VR. IMC design based optimal tuning of a PID-filter governor controller for hydro power plant. In: Proceedings of the advances in power electronics and instrumentation engineering. Springer 2011. p. 34–42. Zitzler, Laumanns, Thiele (bib35) 2002 Kou P, Zhou J, Li C, He Y, He H. Identification of hydraulic turbine governor system parameters based on bacterial foraging optimization algorithm. In: Proceedings of the 2010 sixth international conference on natural computation (ICNC), IEEE 2010. P. 3339–343. Yuan, Su, Nie, Yuan, Wang (bib29) 2011; 15 Deb, Agrawal, Pratap, Meyarivan (bib33) 1917; 2000 Hušek (bib12) 2014; 55 Yuan, Nie, Su, Wang, Yuan (bib28) 2009; 36 Chen, Yuan, Tian, Ji (bib21) 2014; 78 Reynoso-Meza, Sanchis, Blasco, García-Nieto (bib25) 2014; 24 Kishor, Saini, Singh (bib20) 2007; 11 Shigang, Qian (bib36) 2007; 33 Tavakkoli-Moghaddam, Azarkish, Sadeghnejad-Barkousaraie (bib38) 2011; 38 Modares, Naghibi Sistani, Lewis (bib1) 2013; 52 Naik, Srikanth, Negi (bib11) 2012; 4 Chen, Yuan, Ji, Wang, Tian (bib19) 2014; 84 Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science. New York, NY 1995. p. 39–43. Wang, Wang, Wu (bib31) 2013; 221 Fang, Chen, Shen (bib15) 2011; 52 Zhao, Chai, Wang, Fu (bib6) 2014; 22 Panda, Multi-objective (bib32) 2011; 33 Kang, Wang (bib2) 2013; 52 Jin M, Hu W, Liu F, Mei S, Lu Q. Nonlinear co-ordinated control of excitation and governor for hydraulic power plants. In: Proceedings of the IEE – generation, transmission and distribution. 2005, 152, p. 544–48. García, Quintana, Galván, Isasi (bib37) 2011; 6936 Kuhm, Knittel, Bueno (bib3) 2012; 51 Pan, Das (bib17) 2012; 43 Jiang, Ma, Wang (bib16) 2006; 47 Reynoso-Meza, Garcia-Nieto, Sanchis, Blasco (bib24) 2013; 21 Das, Das, Pan (bib34) 2013; 52 Tuba, Bacanin (bib30) 2014; 8 Herreros, Baeyens, Perán (bib23) 2002; 41 Yuan, Su, Yuan, Nie, Wang (bib27) 2009; 34 Zhang, Jiang, Ren, Hou, Xu (bib4) 2013; 52 Juang, Liu, Lin (bib5) 2011; 50 Coello, Pulido, Lechuga (bib18) 2004; 8 Letcher (bib9) 2008 Khodabakhshian, Hooshmand (bib13) 2010; 32 Chekkal, Lahaçani, Aouzellag, Ghedamsi (bib7) 2014; 59 Castillo, Melin (bib8) 2014; 279 Fang (10.1016/j.isatra.2014.11.003_bib15) 2011; 52 Zitzler (10.1016/j.isatra.2014.11.003_bib35) 2002 Modares (10.1016/j.isatra.2014.11.003_bib1) 2013; 52 Hušek (10.1016/j.isatra.2014.11.003_bib12) 2014; 55 Kuhm (10.1016/j.isatra.2014.11.003_bib3) 2012; 51 Zhang (10.1016/j.isatra.2014.11.003_bib4) 2013; 52 Reynoso-Meza (10.1016/j.isatra.2014.11.003_bib25) 2014; 24 Zhao (10.1016/j.isatra.2014.11.003_bib6) 2014; 22 Tavakkoli-Moghaddam (10.1016/j.isatra.2014.11.003_bib38) 2011; 38 Kang (10.1016/j.isatra.2014.11.003_bib2) 2013; 52 10.1016/j.isatra.2014.11.003_bib22 Deb (10.1016/j.isatra.2014.11.003_bib33) 1917; 2000 Juang (10.1016/j.isatra.2014.11.003_bib5) 2011; 50 10.1016/j.isatra.2014.11.003_bib26 Chekkal (10.1016/j.isatra.2014.11.003_bib7) 2014; 59 Tuba (10.1016/j.isatra.2014.11.003_bib30) 2014; 8 Naik (10.1016/j.isatra.2014.11.003_bib11) 2012; 4 Shigang (10.1016/j.isatra.2014.11.003_bib36) 2007; 33 Letcher (10.1016/j.isatra.2014.11.003_bib9) 2008 10.1016/j.isatra.2014.11.003_bib10 Kishor (10.1016/j.isatra.2014.11.003_bib20) 2007; 11 Wang (10.1016/j.isatra.2014.11.003_bib31) 2013; 221 Yuan (10.1016/j.isatra.2014.11.003_bib29) 2011; 15 Yuan (10.1016/j.isatra.2014.11.003_bib28) 2009; 36 10.1016/j.isatra.2014.11.003_bib14 Panda (10.1016/j.isatra.2014.11.003_bib32) 2011; 33 García (10.1016/j.isatra.2014.11.003_bib37) 2011; 6936 Pan (10.1016/j.isatra.2014.11.003_bib17) 2012; 43 Coello (10.1016/j.isatra.2014.11.003_bib18) 2004; 8 Jiang (10.1016/j.isatra.2014.11.003_bib16) 2006; 47 Chen (10.1016/j.isatra.2014.11.003_bib21) 2014; 78 Khodabakhshian (10.1016/j.isatra.2014.11.003_bib13) 2010; 32 Das (10.1016/j.isatra.2014.11.003_bib34) 2013; 52 Yuan (10.1016/j.isatra.2014.11.003_bib27) 2009; 34 Castillo (10.1016/j.isatra.2014.11.003_bib8) 2014; 279 Chen (10.1016/j.isatra.2014.11.003_bib19) 2014; 84 Reynoso-Meza (10.1016/j.isatra.2014.11.003_bib24) 2013; 21 Herreros (10.1016/j.isatra.2014.11.003_bib23) 2002; 41 |
| References_xml | – volume: 221 start-page: 296 year: 2013 end-page: 305 ident: bib31 article-title: Particle swarm optimization with adaptive mutation for multimodal optimization publication-title: Appl Math Comput – reference: Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science. New York, NY 1995. p. 39–43. – volume: 8 start-page: 2831 year: 2014 end-page: 2844 ident: bib30 article-title: Artificial bee colony algorithm hybridized with firefly algorithm for cardinality constrained mean-variance portfolio selection problem publication-title: Appl Math Inf Sci – volume: 84 start-page: 390 year: 2014 end-page: 404 ident: bib19 article-title: Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II publication-title: Energy Convers Manag – volume: 78 start-page: 306 year: 2014 end-page: 315 ident: bib21 article-title: Improved gravitational search algorithm for parameter identification of water turbine regulation system publication-title: Energy Convers Manag – volume: 38 start-page: 10812 year: 2011 end-page: 10821 ident: bib38 article-title: A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem publication-title: Expert Syst Appl – volume: 55 start-page: 460 year: 2014 end-page: 466 ident: bib12 article-title: controller design for hydraulic turbine based on sensitivity margin specifications publication-title: Int J Electr Power Energy Syst – volume: 2000 start-page: 849 year: 1917 end-page: 858 ident: bib33 article-title: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II publication-title: Lect Notes Comput Sci – volume: 36 start-page: 8049 year: 2009 end-page: 8055 ident: bib28 article-title: An improved binary particle swarm optimization for unit commitment problem publication-title: Expert Syst Appl – volume: 11 start-page: 776 year: 2007 end-page: 796 ident: bib20 article-title: A review on hydropower plant models and control publication-title: Renew Sustain Energy Rev – volume: 8 start-page: 256 year: 2004 end-page: 279 ident: bib18 article-title: Handling multiple objectives with particle swarm optimization publication-title: IEEE Trans Evol Comput – volume: 15 start-page: 139 year: 2011 end-page: 148 ident: bib29 article-title: Unit commitment problem using enhanced particle swarm optimization algorithm publication-title: Soft Comput – volume: 279 start-page: 615 year: 2014 end-page: 631 ident: bib8 article-title: A review on interval type-2 fuzzy logic applications in intelligent control publication-title: Inf Sci – volume: 32 start-page: 375 year: 2010 end-page: 382 ident: bib13 article-title: A new PID controller design for automatic generation control of hydro power systems publication-title: Int J Electr Power Energy Syst – volume: 33 start-page: 1296 year: 2011 end-page: 1308 ident: bib32 article-title: controller tuning for a FACTS-based damping stabilizer using non-dominated sorting genetic algorithm-II publication-title: Int J Electric Power Energy Syst – volume: 22 start-page: 217 year: 2014 end-page: 230 ident: bib6 article-title: Hybrid intelligent control for regrinding process in hematite beneficiation publication-title: Control Eng Pract – volume: 43 start-page: 393 year: 2012 end-page: 407 ident: bib17 article-title: Chaotic multi-objective optimization based design of fractional order controller publication-title: Int J Electr Power Energy Syst – volume: 34 start-page: 67 year: 2009 end-page: 74 ident: bib27 article-title: An improved PSO for dynamic load dispatch of generators with valve-point effects publication-title: Energy – year: 2008 ident: bib9 article-title: Future energy: improved, sustainable and clean options for our planet – reference: Kanasottu AN, Pullabhatla S, Mettu VR. IMC design based optimal tuning of a PID-filter governor controller for hydro power plant. In: Proceedings of the advances in power electronics and instrumentation engineering. Springer 2011. p. 34–42. – volume: 6936 start-page: 127 year: 2011 end-page: 134 ident: bib37 article-title: Portfolio optimization using SPEA2 with resampling. Intelligent data engineering and automated learning-IDEAL 2011 publication-title: Lecture notes in computer science – volume: 52 start-page: 611 year: 2013 end-page: 621 ident: bib1 article-title: A policy iteration approach to online optimal control of continuous-time constrained-input systems publication-title: ISA Trans – volume: 52 start-page: 844 year: 2013 end-page: 852 ident: bib2 article-title: Adaptive control of 5 DOF upper-limb exoskeleton robot with improved safety publication-title: ISA Trans – reference: Jin M, Hu W, Liu F, Mei S, Lu Q. Nonlinear co-ordinated control of excitation and governor for hydraulic power plants. In: Proceedings of the IEE – generation, transmission and distribution. 2005, 152, p. 544–48. – volume: 50 start-page: 609 year: 2011 end-page: 619 ident: bib5 article-title: A hybrid intelligent controller for a twin rotor MIMO system and its hardware implementation publication-title: ISA Trans – volume: 52 start-page: 752 year: 2013 end-page: 758 ident: bib4 article-title: Improved single neuron controller for multivariable stochastic systems with non-Gaussianities and unmodeled dynamics publication-title: ISA Trans – start-page: 95 year: 2002 end-page: 100 ident: bib35 article-title: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. Evolutionary methods for design, optimisation and control with application to industrial problems (EUROGEN 2001) – volume: 24 start-page: 341 year: 2014 end-page: 362 ident: bib25 article-title: Physical programming for preference driven evolutionary multi-objective optimization publication-title: Appl Soft Comput – volume: 41 start-page: 457 year: 2002 end-page: 472 ident: bib23 article-title: Design of PID-type controllers using multiobjective genetic algorithms publication-title: ISA Trans – volume: 21 start-page: 445 year: 2013 end-page: 458 ident: bib24 article-title: Controller tuning by means of multi-objective optimization algorithms: a global tuning framework publication-title: IEEE Trans Control Syst Technol – volume: 47 start-page: 1222 year: 2006 end-page: 1230 ident: bib16 article-title: controller parameters optimization of hydro-turbine governing systems using deterministic-chaotic-mutation evolutionary programming (DCMEP) publication-title: Energy Convers Manag – volume: 33 start-page: 115 year: 2007 end-page: 119 ident: bib36 article-title: Multi-objective reactive power optimization using SPEA2 publication-title: High Volt Eng – volume: 52 start-page: 1763 year: 2011 end-page: 1770 ident: bib15 article-title: Application of an improved PSO algorithm to optimal tuning of PID gains for water turbine governor publication-title: Energy Convers Manag – volume: 59 start-page: 166 year: 2014 end-page: 175 ident: bib7 article-title: Fuzzy logic control strategy of wind generator based on the dual-stator induction generator publication-title: Int J Electr Power Energy Syst – reference: Kou P, Zhou J, Li C, He Y, He H. Identification of hydraulic turbine governor system parameters based on bacterial foraging optimization algorithm. In: Proceedings of the 2010 sixth international conference on natural computation (ICNC), IEEE 2010. P. 3339–343. – volume: 51 start-page: 732 year: 2012 end-page: 742 ident: bib3 article-title: Robust control strategies for an electric motor driven accumulator with elastic webs publication-title: ISA Trans – volume: 4 start-page: 845 year: 2012 end-page: 853 ident: bib11 article-title: IMC tuned PID governor controller for hydro power plant with water hammer effect publication-title: Procedia Technol – volume: 52 start-page: 56 year: 2013 end-page: 77 ident: bib34 article-title: Multi-objective optimization framework for networked predictive controller design publication-title: ISA Trans – volume: 6936 start-page: 127 year: 2011 ident: 10.1016/j.isatra.2014.11.003_bib37 article-title: Portfolio optimization using SPEA2 with resampling. Intelligent data engineering and automated learning-IDEAL 2011 publication-title: Lecture notes in computer science doi: 10.1007/978-3-642-23878-9_16 – ident: 10.1016/j.isatra.2014.11.003_bib14 – volume: 84 start-page: 390 year: 2014 ident: 10.1016/j.isatra.2014.11.003_bib19 article-title: Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2014.04.052 – volume: 2000 start-page: 849 year: 1917 ident: 10.1016/j.isatra.2014.11.003_bib33 article-title: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II publication-title: Lect Notes Comput Sci – volume: 38 start-page: 10812 year: 2011 ident: 10.1016/j.isatra.2014.11.003_bib38 article-title: A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2011.02.050 – volume: 4 start-page: 845 year: 2012 ident: 10.1016/j.isatra.2014.11.003_bib11 article-title: IMC tuned PID governor controller for hydro power plant with water hammer effect publication-title: Procedia Technol doi: 10.1016/j.protcy.2012.05.139 – volume: 34 start-page: 67 year: 2009 ident: 10.1016/j.isatra.2014.11.003_bib27 article-title: An improved PSO for dynamic load dispatch of generators with valve-point effects publication-title: Energy doi: 10.1016/j.energy.2008.09.010 – volume: 221 start-page: 296 year: 2013 ident: 10.1016/j.isatra.2014.11.003_bib31 article-title: Particle swarm optimization with adaptive mutation for multimodal optimization publication-title: Appl Math Comput doi: 10.1016/j.amc.2013.06.074 – volume: 15 start-page: 139 year: 2011 ident: 10.1016/j.isatra.2014.11.003_bib29 article-title: Unit commitment problem using enhanced particle swarm optimization algorithm publication-title: Soft Comput doi: 10.1007/s00500-010-0541-y – volume: 41 start-page: 457 year: 2002 ident: 10.1016/j.isatra.2014.11.003_bib23 article-title: Design of PID-type controllers using multiobjective genetic algorithms publication-title: ISA Trans doi: 10.1016/S0019-0578(07)60102-5 – volume: 21 start-page: 445 year: 2013 ident: 10.1016/j.isatra.2014.11.003_bib24 article-title: Controller tuning by means of multi-objective optimization algorithms: a global tuning framework publication-title: IEEE Trans Control Syst Technol doi: 10.1109/TCST.2012.2185698 – volume: 52 start-page: 1763 year: 2011 ident: 10.1016/j.isatra.2014.11.003_bib15 article-title: Application of an improved PSO algorithm to optimal tuning of PID gains for water turbine governor publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2010.11.005 – volume: 36 start-page: 8049 year: 2009 ident: 10.1016/j.isatra.2014.11.003_bib28 article-title: An improved binary particle swarm optimization for unit commitment problem publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2008.10.047 – volume: 50 start-page: 609 year: 2011 ident: 10.1016/j.isatra.2014.11.003_bib5 article-title: A hybrid intelligent controller for a twin rotor MIMO system and its hardware implementation publication-title: ISA Trans doi: 10.1016/j.isatra.2011.06.006 – volume: 52 start-page: 752 year: 2013 ident: 10.1016/j.isatra.2014.11.003_bib4 article-title: Improved single neuron controller for multivariable stochastic systems with non-Gaussianities and unmodeled dynamics publication-title: ISA Trans doi: 10.1016/j.isatra.2013.07.002 – volume: 59 start-page: 166 year: 2014 ident: 10.1016/j.isatra.2014.11.003_bib7 article-title: Fuzzy logic control strategy of wind generator based on the dual-stator induction generator publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2014.02.005 – volume: 43 start-page: 393 year: 2012 ident: 10.1016/j.isatra.2014.11.003_bib17 article-title: Chaotic multi-objective optimization based design of fractional order controller PIλDμ in AVR system publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2012.06.034 – volume: 22 start-page: 217 year: 2014 ident: 10.1016/j.isatra.2014.11.003_bib6 article-title: Hybrid intelligent control for regrinding process in hematite beneficiation publication-title: Control Eng Pract doi: 10.1016/j.conengprac.2013.02.015 – year: 2008 ident: 10.1016/j.isatra.2014.11.003_bib9 – volume: 33 start-page: 115 year: 2007 ident: 10.1016/j.isatra.2014.11.003_bib36 article-title: Multi-objective reactive power optimization using SPEA2 publication-title: High Volt Eng – volume: 51 start-page: 732 year: 2012 ident: 10.1016/j.isatra.2014.11.003_bib3 article-title: Robust control strategies for an electric motor driven accumulator with elastic webs publication-title: ISA Trans doi: 10.1016/j.isatra.2012.05.004 – volume: 32 start-page: 375 year: 2010 ident: 10.1016/j.isatra.2014.11.003_bib13 article-title: A new PID controller design for automatic generation control of hydro power systems publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2009.11.006 – volume: 52 start-page: 56 year: 2013 ident: 10.1016/j.isatra.2014.11.003_bib34 article-title: Multi-objective optimization framework for networked predictive controller design publication-title: ISA Trans doi: 10.1016/j.isatra.2012.09.004 – volume: 55 start-page: 460 year: 2014 ident: 10.1016/j.isatra.2014.11.003_bib12 article-title: controller design for hydraulic turbine based on sensitivity margin specifications publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2013.09.029 – volume: 279 start-page: 615 year: 2014 ident: 10.1016/j.isatra.2014.11.003_bib8 article-title: A review on interval type-2 fuzzy logic applications in intelligent control publication-title: Inf Sci doi: 10.1016/j.ins.2014.04.015 – volume: 11 start-page: 776 year: 2007 ident: 10.1016/j.isatra.2014.11.003_bib20 article-title: A review on hydropower plant models and control publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2005.06.003 – volume: 24 start-page: 341 year: 2014 ident: 10.1016/j.isatra.2014.11.003_bib25 article-title: Physical programming for preference driven evolutionary multi-objective optimization publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2014.07.009 – volume: 52 start-page: 844 year: 2013 ident: 10.1016/j.isatra.2014.11.003_bib2 article-title: Adaptive control of 5 DOF upper-limb exoskeleton robot with improved safety publication-title: ISA Trans doi: 10.1016/j.isatra.2013.05.003 – ident: 10.1016/j.isatra.2014.11.003_bib22 doi: 10.1109/ICNC.2010.5583639 – volume: 47 start-page: 1222 year: 2006 ident: 10.1016/j.isatra.2014.11.003_bib16 article-title: controller parameters optimization of hydro-turbine governing systems using deterministic-chaotic-mutation evolutionary programming (DCMEP) publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2005.07.009 – ident: 10.1016/j.isatra.2014.11.003_bib26 doi: 10.1109/MHS.1995.494215 – start-page: 95 year: 2002 ident: 10.1016/j.isatra.2014.11.003_bib35 – volume: 8 start-page: 2831 year: 2014 ident: 10.1016/j.isatra.2014.11.003_bib30 article-title: Artificial bee colony algorithm hybridized with firefly algorithm for cardinality constrained mean-variance portfolio selection problem publication-title: Appl Math Inf Sci doi: 10.12785/amis/080619 – volume: 52 start-page: 611 year: 2013 ident: 10.1016/j.isatra.2014.11.003_bib1 article-title: A policy iteration approach to online optimal control of continuous-time constrained-input systems publication-title: ISA Trans doi: 10.1016/j.isatra.2013.04.004 – volume: 33 start-page: 1296 year: 2011 ident: 10.1016/j.isatra.2014.11.003_bib32 article-title: controller tuning for a FACTS-based damping stabilizer using non-dominated sorting genetic algorithm-II publication-title: Int J Electric Power Energy Syst doi: 10.1016/j.ijepes.2011.06.002 – volume: 8 start-page: 256 year: 2004 ident: 10.1016/j.isatra.2014.11.003_bib18 article-title: Handling multiple objectives with particle swarm optimization publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2004.826067 – ident: 10.1016/j.isatra.2014.11.003_bib10 doi: 10.1007/978-3-642-20499-9_6 – volume: 78 start-page: 306 year: 2014 ident: 10.1016/j.isatra.2014.11.003_bib21 article-title: Improved gravitational search algorithm for parameter identification of water turbine regulation system publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2013.10.060 |
| SSID | ssj0002598 |
| Score | 2.3364322 |
| Snippet | A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety,... |
| SourceID | proquest pubmed crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 173 |
| SubjectTerms | Adaptive grid particle swarm optimization Hydraulic turbine regulating system Multi-objective Non-dominated Sorting Genetic Algorithm II (NSGAII) Strength Pareto Evolutionary Algorithm II (SPEAII) |
| Title | Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization |
| URI | https://dx.doi.org/10.1016/j.isatra.2014.11.003 https://www.ncbi.nlm.nih.gov/pubmed/25481821 https://www.proquest.com/docview/1681910373 |
| Volume | 56 |
| WOSCitedRecordID | wos000356128000017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-2022 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002598 issn: 0019-0578 databaseCode: AIEXJ dateStart: 19950301 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1ba9RAFB7W1gd9KLbeWrWM4IOypOQymUkel1pxfSiFVtj1JUySSTfLNlmym1p_jj_Hf-WZSy66lKogC2EJSWbgfDnnOyfngtAbKsA5TnwXXqSUWAT2agWuSKw4CO0sBO_N46kaNsFOT4PJJDwbDH40tTDXC1YUwc1NuPyvooZzIGxZOvsX4m4fCifgPwgdjiB2OP6R4EfdJ2n18VxmDFplPNearclNX4hK0s4SNMaVzDevC5P-fDZ-P7zkMnFG5VcOZ9_SiteyFTYYp1hy0kqPr1eBCNUHelirgANP-VItcVnl6XBptjZcfeVXeh1T8tnnw-PzkZxS0Ywsbwn-sSka-TLLZ3UH4Gmt47VTXsT5xtlJzstZnXdINbHwaS3gIf34huN32YQ66NYU3nRZTkqRy9orX0__ORJadwcsBCS4vyh3v6-dHT01xRh6R1v6DRuiwxlzlU5VydZUDjmSjV5tr7OZbSbjudyJ3Ag4qrI9G7CBbZf5IdiI7dH4ZPKppQXgZxpaoHfe1HGqZMPNtW7jSbf5QYoPXTxCO8aRwSMt5V00EMUeethrb7mHdo3hWOG3prv5u8foew-fuMzwb_jEHT7xusQGn1jjU14P-MQKnxjwiTlu8YkNPnGHT6zxiRU-cYNPLPGJG3xiiU_cx-cT9PnDycXxR8vMCbES4rlri9ixEHFsM85Z6IgsoH6WxWFMSSZiTsElTikFtSN79QG9JZTQxBUUfllqp57wnqKtoizEc4RZnDiZ76QiZUB1QXURRh3qh_DXIyTx95HXSCVKTBN9OctlETXZkvNIyzKSsgT_Wjbf3UdWe9dSN5G543rWCDwyRFgT3Agwesedrxt8RGAn5Mc_XoiyXkUOlaEZ22NwzTMNnHYvrk-At7vOwT-v-wI96N7dl2hrXdXiFbqfXK_zVXWI7rFJcGheiJ-V5fgy |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Application+of+multi-objective+controller+to+optimal+tuning+of+PID+gains+for+a+hydraulic+turbine+regulating+system+using+adaptive+grid+particle+swam+optimization&rft.jtitle=ISA+transactions&rft.au=Chen%2C+Zhihuan&rft.au=Yuan%2C+Yanbin&rft.au=Yuan%2C+Xiaohui&rft.au=Huang%2C+Yuehua&rft.date=2015-05-01&rft.pub=Elsevier+Ltd&rft.issn=0019-0578&rft.eissn=1879-2022&rft.volume=56&rft.spage=173&rft.epage=187&rft_id=info:doi/10.1016%2Fj.isatra.2014.11.003&rft.externalDocID=S0019057814002602 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0019-0578&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0019-0578&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0019-0578&client=summon |