PI controller design using ABC algorithm for MPPT of PV system supplying DC motor pump load
Maximum power point tracking (MPPT) is used in photovoltaic (PV) systems to maximize its output power. A new MPPT system has been suggested for PV–DC motor pump system by designing two PI controllers. The first one is used to reach MPPT by monitoring the voltage and current of the PV array and adjus...
Saved in:
| Published in | Neural computing & applications Vol. 28; no. 2; pp. 353 - 364 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.02.2017
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0941-0643 1433-3058 |
| DOI | 10.1007/s00521-015-2067-9 |
Cover
| Abstract | Maximum power point tracking (MPPT) is used in photovoltaic (PV) systems to maximize its output power. A new MPPT system has been suggested for PV–DC motor pump system by designing two PI controllers. The first one is used to reach MPPT by monitoring the voltage and current of the PV array and adjusting the duty cycle of the DC/DC converter. The second PI controller is designed for speed control of DC series motor by setting the voltage fed to the DC series motor through another DC/DC converter. The suggested design problem of MPPT and speed controller is formulated as an optimization task which is solved by artificial bee colony (ABC) to search for optimal parameters of PI controllers. Simulation results have shown the validity of the developed technique in delivering MPPT to DC series motor pump system under atmospheric conditions and tracking the reference speed of motor. Moreover, the performance of the ABC algorithm is compared with genetic algorithm for various disturbances to prove its robustness. |
|---|---|
| AbstractList | Maximum power point tracking (MPPT) is used in photovoltaic (PV) systems to maximize its output power.A new MPPT system has been suggested for PV–DC motor pump system by designing two PI controllers. The first one is used to reach MPPT by monitoring the voltage and current of the PV array and adjusting the duty cycle of the DC/DC converter. The second PI controller is designed for speed control of DC series motor by setting the voltage fed to the DC series motor through another DC/DC converter. The suggested design problem of MPPT and speed controller is formulated as an optimization task which is solved by artificial bee colony (ABC) to search for optimal parameters of PI controllers. Simulation results have shown the validity of the developed technique in delivering MPPT to DC series motor pump system under atmospheric conditions and tracking the reference speed of motor. Moreover, the performance of the ABC algorithm is compared with genetic algorithm for various disturbances to prove its robustness. |
| Author | Oshaba, A. S. Abd Elazim, S. M. Ali, E. S. |
| Author_xml | – sequence: 1 givenname: A. S. surname: Oshaba fullname: Oshaba, A. S. organization: Electronics Research Institute, Power Electronics and Energy Conversions – sequence: 2 givenname: E. S. surname: Ali fullname: Ali, E. S. organization: Electric Power and Machine Department, Faculty of Engineering, Zagazig University – sequence: 3 givenname: S. M. surname: Abd Elazim fullname: Abd Elazim, S. M. email: sahareldeep@yahoo.com organization: Electric Power and Machine Department, Faculty of Engineering, Zagazig University |
| BookMark | eNp9kD1PwzAQhi1UJNrCD2CzxBw4f8SJx1K-KhWRobAwWKlrl1RJHOxk6L_HVRkQEixn6fw8vvM7QaPWtQahSwLXBCC7CQApJQmQNKEgskSeoDHhjCUM0nyExiB5vBWcnaFJCDsA4CJPx-i9WGDt2t67ujYeb0yoti0eQtVu8ex2jst663zVfzTYOo-fi2KFncXFGw770JsGh6Hr6v2BvpvjxvUR6oamw7UrN-fo1JZ1MBff5xS9Ptyv5k_J8uVxMZ8tE82I6BOaC2NTqq0xGuxaGmZtmTItpRaEai5sbMRC842gPJcmW695KdmaUyolS9kUXR3f7bz7HEzo1c4Nvo0jFclzyCQHAZHKjpT2LgRvrNJVX_bV4fNlVSsC6pCkOiapYpLqkKSS0SS_zM5XTen3_zr06ITItlvjf-z0p_QFMk6G2A |
| CitedBy_id | crossref_primary_10_1016_j_conengprac_2018_10_013 crossref_primary_10_1016_j_solener_2017_11_040 crossref_primary_10_3390_app121910056 crossref_primary_10_1016_j_solener_2018_12_008 crossref_primary_10_1080_03772063_2018_1497552 crossref_primary_10_1007_s00521_016_2668_y crossref_primary_10_1016_j_seta_2020_100859 crossref_primary_10_1111_exsy_12488 crossref_primary_10_1038_s41598_024_58852_8 crossref_primary_10_1007_s00521_016_2210_2 crossref_primary_10_1007_s00521_017_2860_8 crossref_primary_10_1016_j_seta_2021_101824 crossref_primary_10_1007_s00521_019_04301_x crossref_primary_10_1016_j_renene_2024_121801 crossref_primary_10_1007_s12652_021_03173_1 crossref_primary_10_1007_s00500_022_07139_z crossref_primary_10_1007_s13198_024_02362_3 crossref_primary_10_1002_2050_7038_12984 crossref_primary_10_1016_j_jksues_2021_06_008 crossref_primary_10_1016_j_seta_2022_102675 crossref_primary_10_1016_j_seta_2022_102156 crossref_primary_10_1080_00207217_2023_2210299 crossref_primary_10_1016_j_isatra_2021_08_021 crossref_primary_10_1016_j_rser_2021_111467 crossref_primary_10_1007_s00521_019_04260_3 crossref_primary_10_1016_j_matpr_2022_03_414 crossref_primary_10_1155_2019_1378783 crossref_primary_10_25092_baunfbed_885152 crossref_primary_10_1016_j_iswa_2023_200261 crossref_primary_10_1109_ACCESS_2019_2931547 crossref_primary_10_3233_JIFS_179069 crossref_primary_10_1016_j_asej_2020_08_016 crossref_primary_10_1016_j_atech_2023_100237 crossref_primary_10_1109_MSMC_2021_3066149 crossref_primary_10_1007_s00521_018_3429_x crossref_primary_10_46300_9106_2023_17_10 crossref_primary_10_3390_en15176172 crossref_primary_10_1109_ACCESS_2021_3066281 crossref_primary_10_1016_j_asej_2023_102329 crossref_primary_10_3390_jmse11010222 crossref_primary_10_1007_s00521_021_06128_x crossref_primary_10_1016_j_enconman_2017_08_088 crossref_primary_10_1016_j_solener_2022_07_012 crossref_primary_10_1016_j_solener_2019_03_038 crossref_primary_10_1016_j_ijepes_2020_106440 crossref_primary_10_1016_j_conengprac_2021_104809 crossref_primary_10_1007_s00521_018_3772_y crossref_primary_10_1049_pel2_12369 crossref_primary_10_1007_s13369_023_07861_2 crossref_primary_10_1016_j_jestch_2018_07_007 crossref_primary_10_1016_j_isatra_2023_05_025 crossref_primary_10_1007_s42835_022_01097_0 |
| Cites_doi | 10.1007/978-3-642-25541-0_11 10.1007/s00521-014-1682-1 10.1016/j.renene.2004.09.011 10.1109/ISIE.1998.707746 10.1109/PESC.1999.785575 10.1016/j.rser.2013.05.022 10.1109/TPEL.2012.2185713 10.1016/j.apenergy.2013.02.008 10.1109/PVSC.2000.916230 10.1007/s00202-012-0242-x 10.1049/ip-epa:20010656 10.1109/EEEIC.2011.5874849 10.1007/s13369-012-0423-y 10.1016/j.asoc.2015.03.047 10.1016/j.segan.2015.04.002 10.1016/j.ijepes.2012.04.047 10.3844/ajassp.2012.1464.1471 10.1016/j.solener.2011.04.015 10.1002/0471668826 10.1049/iet-rpg.2009.0006 10.1007/s00521-014-1685-y 10.19026/rjaset.5.4377 10.1016/j.ijepes.2010.11.002 10.1109/ICEES.2011.5725340 10.1007/s10898-007-9149-x 10.1016/j.energy.2014.05.011 10.1007/b100747 10.19026/rjaset.5.4380 10.1007/978-3-642-15853-7_28 10.1016/j.solener.2013.09.025 10.1016/j.solener.2011.06.025 10.1016/j.ijepes.2012.10.047 10.1080/00908319808970042 |
| ContentType | Journal Article |
| Copyright | The Natural Computing Applications Forum 2015 Copyright Springer Science & Business Media 2017 |
| Copyright_xml | – notice: The Natural Computing Applications Forum 2015 – notice: Copyright Springer Science & Business Media 2017 |
| DBID | AAYXX CITATION |
| DOI | 10.1007/s00521-015-2067-9 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1433-3058 |
| EndPage | 364 |
| ExternalDocumentID | 10_1007_s00521_015_2067_9 |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB PUEGO |
| ID | FETCH-LOGICAL-c316t-286ef52cfeec0fb9e3ffa53c99c612c46f3ff6f328d62489e7bb4a93b42299353 |
| IEDL.DBID | AGYKE |
| ISSN | 0941-0643 |
| IngestDate | Fri Jul 25 04:22:47 EDT 2025 Thu Apr 24 22:59:37 EDT 2025 Wed Oct 01 02:25:42 EDT 2025 Fri Feb 21 02:34:22 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | Optimization algorithm Maximum power point tracking DC series motor pump system PI controller Photovoltaic system |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c316t-286ef52cfeec0fb9e3ffa53c99c612c46f3ff6f328d62489e7bb4a93b42299353 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 1880794060 |
| PQPubID | 2043988 |
| PageCount | 12 |
| ParticipantIDs | proquest_journals_1880794060 crossref_citationtrail_10_1007_s00521_015_2067_9 crossref_primary_10_1007_s00521_015_2067_9 springer_journals_10_1007_s00521_015_2067_9 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2017-02-01 |
| PublicationDateYYYYMMDD | 2017-02-01 |
| PublicationDate_xml | – month: 02 year: 2017 text: 2017-02-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: Heidelberg |
| PublicationTitle | Neural computing & applications |
| PublicationTitleAbbrev | Neural Comput & Applic |
| PublicationYear | 2017 |
| Publisher | Springer London Springer Nature B.V |
| Publisher_xml | – name: Springer London – name: Springer Nature B.V |
| References | PatelMRWind and solar power systems: design, analysis, and operation20062USACRC Press, Taylor & Francis Group Brambilla A, Gambarara M, Garutti A, Ronchi F (1999) New approach to photovoltaic arrays maximum power point tracking. In: 30th annual IEEE power electronics specialists conference 1999 (PESC 99), vol 2, pp 632–637 AI-Amoudi A, Zhang L (2000) Application of radial basis function networks for solar-array modeling and maximum power-point prediction. In: IEE proceeding-generation, transmission and distribution, vol 147, no. 5, pp 310–316 IshaqueKSalamZAmjadMMekhilefSAn improved particle swarm optimization (PSO)-based MPPT for PV with reduced steady-state oscillationIEEE Trans Power Electron20122783627363810.1109/TPEL.2012.2185713 OshabaASAliESAbd-ElazimSMMPPT control design of PV system supplied SRM using BAT search algorithmSustain Energy Grids Netw20152C516010.1016/j.segan.2015.04.002 GitizadehMKhalilnezhadHHedayatzadehRTCSC allocation in power systems considering switching loss using MOABC algorithmElectr Eng2013952738510.1007/s00202-012-0242-x OshiroMTanakaKSenjyuTTomaSAtsushiYSaberAFunabashiTKimCOptimal voltage control in distribution systems using PV generatorsInt J Electr Power Energy Syst201133348549210.1016/j.ijepes.2010.11.002 TiacharoenSChatchanayuenyongTDesign and development of an intelligent control by using bee colony optimization techniqueAm J Appl Sci2012991464147110.3844/ajassp.2012.1464.1471 MastersGMRenewable and efficient electric power systems2004HobokenWiley10.1002/0471668826 OuadaMMeridjetMSaoudMTalbiNIncrease efficiency of photovoltaic pumping system based BLDC motor using fuzzy logic MPPT controlWSEAS Trans Power Syst201383104113 BenyoucefASChouderAKaraKSilvestreSSahedOAArtificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditionsAppl Soft Comput201532384810.1016/j.asoc.2015.03.047 MehtaRMehtaVKPrinciples of electrical machines20132New Delhi, IndiaS. Chand Publishing YeadonWHYeadonAWHandbook of small electric motors2001New YorkMcGraw-Hill0272.46043 CheikhMSALarbesCKebirGFTZerguerrasAMaximum power point tracking using fuzzy logic control schemeRev Energ Renouv2007103387395 AwanSMAslamMKhanZASaeedHAn efficient model based on artificial bee colony optimization algorithm with neural networks for electric load forecastingNeural Comput Appl2014257–81967197810.1007/s00521-014-1685-y Swiegers W, Enslin J (1998) An integrated maximum power point tracker for photovoltaic panels. In: Proceedings IEEE international symposium on industrial electronics, vol 1, pp 40–44 VeeracharyMSeniyuTUezatoKMaximum power point tracking control of IDB converter supplied PV systemIEE Proc Electron Power Appl2001148649450210.1049/ip-epa:20010656 YuGJJungYSChoiJYKimGSA novel two-mode MPPT control algorithm based on comparative study of existing algorithmsSol Energy200476444546310.1016/j.solener.2003.08.038 Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. In: Technical report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department Hohm DP, Ropp ME (2000) Comparative study of maximum power point tracking algorithm using an experimental programmable, maximum power point tracking test bed. In: Proceedings of 28th IEEE photovoltaic specialist conference, pp 1699–1702 Armstrong S, Hurley W (2004) Self regulating maximum power point tracking for solar energy systems. In: 39th international universities power engineering conference, UPEC, Bristol, UK, pp 604–609 AbediniaONaslianMDBekraviMA new stochastic search algorithm bundled honeybee mating for solving optimization problemsNeural Comput Appl2014257–81921193910.1007/s00521-014-1682-1 Baek J, Ko J, Choi J, Kang S, Chung D (2010) Maximum power point tracking control of photovoltaic system using neural network. In: International of conference on electrical machines and systems (ICEMS), pp 638–643 Ramaprabha R, Gothandaraman V, Kanimozhi K, Divya R, Mathur BL (2011) Maximum power point tracking using GA optimized artificial neural network for solar PV system. In: IEEE international conference on electrical energy systems, pp 264–268 Osheba DS (2011) Photovoltaic system fed DC motor controlled by converters. In: M.Sc. Thesis, March 2011, Menoufiya University, Egypt PoojaBDubSSSinghJBLehanaPSolar power optimization using BFO algorithmInt J Adv Res Comput Sci Softw Eng2013312238241 GokmenNKaratepeEUgranliFSilvestreSVoltage band based global MPPT controller for photovoltaic systemsSol Energy201398332233410.1016/j.solener.2013.09.025 KarabogaDBasturkBA powerful and efficient algorithm for numerical function optimization: artificial bee colony algorithmJ Glob Optim2007393459471234617810.1007/s10898-007-9149-x1149.90186 AhinAEModeling and optimization of renewable energy systems2012IndiaInTech BabarBCrăciunescuAComparison of artificial bee colony Algorithm with other algorithms used for tracking of maximum power point of photovoltaic arraysRenew Energy Power Q J20141214 Abedinia O, Wyns B, Ghasemi A (2011) Robust fuzzy PSS design using ABC. In: 10th environment and electrical energy international conference (EEEIC) Rome, Italy, pp 100–103 NafehAAFahmyFHMahgoubOAEl-ZahabEMDeveloped algorithm of maximum power tracking for stand-alone photovoltaic systemEnergy Sour199820455310.1080/00908319808970042 PiegariLRizzoRAdaptive perturb and observe algorithm for photovoltaic maximum power point trackingIET Renew Power Gener20104431732810.1049/iet-rpg.2009.0006 IshaqueKSalamZAn improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE)Sol Energy2011852349235910.1016/j.solener.2011.06.025 Abd-ElazimSMAliESSynergy of particle swarm optimization and bacterial foraging for TCSC damping controller designInt J WSEAS Trans Power Syst2013827484 EricksonRWMaksimovicDFundamentals of power electronics20012New YorkSpringer10.1007/b100747 Amrouche B, Belhamel M, Guessoum A (2007) Artificial intelligence based P&O MPPT method for photovoltaic systems. Revue des Energies Renouvelables ICRESD-07 Tlemcen 11–16 OshabaASAliESSwarming speed control for DC permanent magnet motor drive via pulse width modulation technique and DC/DC converterRes J Appl Sci Eng Technol201351845764583 AliESAbd-ElazimSMPower system stability enhancement via new coordinated design of PSSs and SVCInt J WSEAS Trans Power Syst20149428438 Hui J, Sun X (2010) MPPT strategy of PV system based on adaptive fuzzy PID algorithm. In: International conference on life system modeling and intelligent computing, vol 97, pp 220–228 AliESAbd-ElazimSMPower system stability enhancement via bacteria foraging optimization algorithmInt Arab J Sci Eng (AJSE)201338359961110.1007/s13369-012-0423-y MostofiFSafaviMApplication of ABC algorithm for grid-independent hybrid hydro/photovoltaic/wind/fuel cell power generation system considering cost and reliabilityInt J Renew Energy Res201334928940 EltawilMAZhaoZMPPT techniques for photovoltaic applicationsRenew Sustain Energy Rev20132579381310.1016/j.rser.2013.05.022 YouesfAOshabaAEfficient fuzzy logic speed control for various types of DC motors supplied by photovoltaic system under maximum power point trackingJ Eng Sci Assiut Univ201240514551474 Abd-ElazimSMAliESA hybrid particle swarm optimization and bacterial foraging for optimal power system stabilizers designInt J Electr Power Energy Syst20134633434110.1016/j.ijepes.2012.10.047 IshaqueKSalamZTaheriHShamsudinAA critical evaluation of EA computational methods for photovoltaic cell parameter extraction Based on two diode modelSol Energy2011851768177910.1016/j.solener.2011.04.015 OlivaDCuevasEPajaresGParameter identification of solar cells using artificial bee colony optimizationEnergy20147219310210.1016/j.energy.2014.05.011 KassemAMMPPT control design and performance improvements of a PV generator powered DC motor-pump system based on artificial neural networksInt J Electr Power Energy Syst201243909810.1016/j.ijepes.2012.04.047 YounisMAKhatibTNajeebMAriffinAMAn improved maximum power point tracking controller for PV systems using artificial neural networkPrz Elektrotech2012883b116121 ZhaoYZhaoXZhangYMPPT for photovoltaic system using multi-objective improved particle swarm optimization algorithmTeklanika Indones J Electr Eng2014121261268 SalamZAhmedJMeruguBSThe application of soft computing methods for MPPT of PV system: a technological and status reviewAppl Energy201310713514810.1016/j.apenergy.2013.02.008 BahgatABGHelwaNHAhmadGEEl ShenawyETMaximum power point tracking controller for PV systems using neural networksRenew Energy20053081257126810.1016/j.renene.2004.09.011 ZhangHChengSA new MPPT algorithm based on ANN in solar PV systemsAdv Comput Commun Control Autom LNEE2011121778410.1007/978-3-642-25541-0_11 MohanNUndelandTMRobbinsWPPower electronics converters, applications, and design20033AmsterdamWiley Hua C, Shen C (1997) Control of DC/DC converters for solar energy system with maximum power tracking. In: 23rd international conference on industrial electronics, control and instrumentation. IECON’97, vol 2, pp 827–832 OshabaASAliESSpeed control of induction motor fed from wind turbine via particle swarm optimization based PI controllerRes J Appl Sci Eng Technol201351845944606 Liu X, Lopes LAC (2004) An improvement perturbation and observation maximum power point tracking algorithm for PV arrays. In: Power electronics specialists conference, PESC’04, vol 3, pp 2005–2010 OshabaASAliESBacteria foraging: A new technique for speed control of DC series motor supplied by photovoltaic systemInt J WSEAS Trans Power Syst20149185195 GJ Yu (2067_CR56) 2004; 76 MSA Cheikh (2067_CR57) 2007; 10 K Ishaque (2067_CR22) 2011; 85 2067_CR37 B Babar (2067_CR44) 2014; 12 K Ishaque (2067_CR24) 2012; 27 2067_CR35 MA Eltawil (2067_CR7) 2013; 25 M Ouada (2067_CR14) 2013; 8 2067_CR9 AM Kassem (2067_CR19) 2012; 43 L Piegari (2067_CR6) 2010; 4 2067_CR8 2067_CR5 AS Benyoucef (2067_CR45) 2015; 32 N Gokmen (2067_CR26) 2013; 98 D Karaboga (2067_CR36) 2007; 39 M Veerachary (2067_CR4) 2001; 148 ABG Bahgat (2067_CR16) 2005; 30 MA Younis (2067_CR20) 2012; 88 AE Ahin (2067_CR2) 2012 S Tiacharoen (2067_CR39) 2012; 9 O Abedinia (2067_CR41) 2014; 25 F Mostofi (2067_CR42) 2013; 3 K Ishaque (2067_CR21) 2011; 85 R Mehta (2067_CR47) 2013 AA Nafeh (2067_CR55) 1998; 20 Y Zhao (2067_CR25) 2014; 12 B Pooja (2067_CR29) 2013; 3 SM Abd-Elazim (2067_CR31) 2013; 8 SM Awan (2067_CR40) 2014; 25 WH Yeadon (2067_CR46) 2001 AS Oshaba (2067_CR33) 2014; 9 H Zhang (2067_CR17) 2011; 121 2067_CR15 2067_CR58 2067_CR13 2067_CR18 2067_CR50 2067_CR10 2067_CR54 2067_CR53 Z Salam (2067_CR51) 2013; 107 D Oliva (2067_CR43) 2014; 72 M Gitizadeh (2067_CR38) 2013; 95 ES Ali (2067_CR34) 2014; 9 2067_CR23 RW Erickson (2067_CR48) 2001 N Mohan (2067_CR49) 2003 AS Oshaba (2067_CR27) 2013; 5 MR Patel (2067_CR3) 2006 A Youesf (2067_CR12) 2012; 40 GM Masters (2067_CR1) 2004 ES Ali (2067_CR30) 2013; 38 AS Oshaba (2067_CR28) 2013; 5 M Oshiro (2067_CR11) 2011; 33 AS Oshaba (2067_CR52) 2015; 2C SM Abd-Elazim (2067_CR32) 2013; 46 |
| References_xml | – reference: Baek J, Ko J, Choi J, Kang S, Chung D (2010) Maximum power point tracking control of photovoltaic system using neural network. In: International of conference on electrical machines and systems (ICEMS), pp 638–643 – reference: BabarBCrăciunescuAComparison of artificial bee colony Algorithm with other algorithms used for tracking of maximum power point of photovoltaic arraysRenew Energy Power Q J20141214 – reference: OlivaDCuevasEPajaresGParameter identification of solar cells using artificial bee colony optimizationEnergy20147219310210.1016/j.energy.2014.05.011 – reference: PoojaBDubSSSinghJBLehanaPSolar power optimization using BFO algorithmInt J Adv Res Comput Sci Softw Eng2013312238241 – reference: TiacharoenSChatchanayuenyongTDesign and development of an intelligent control by using bee colony optimization techniqueAm J Appl Sci2012991464147110.3844/ajassp.2012.1464.1471 – reference: YeadonWHYeadonAWHandbook of small electric motors2001New YorkMcGraw-Hill0272.46043 – reference: VeeracharyMSeniyuTUezatoKMaximum power point tracking control of IDB converter supplied PV systemIEE Proc Electron Power Appl2001148649450210.1049/ip-epa:20010656 – reference: EltawilMAZhaoZMPPT techniques for photovoltaic applicationsRenew Sustain Energy Rev20132579381310.1016/j.rser.2013.05.022 – reference: Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. In: Technical report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department – reference: AliESAbd-ElazimSMPower system stability enhancement via new coordinated design of PSSs and SVCInt J WSEAS Trans Power Syst20149428438 – reference: OuadaMMeridjetMSaoudMTalbiNIncrease efficiency of photovoltaic pumping system based BLDC motor using fuzzy logic MPPT controlWSEAS Trans Power Syst201383104113 – reference: AI-Amoudi A, Zhang L (2000) Application of radial basis function networks for solar-array modeling and maximum power-point prediction. In: IEE proceeding-generation, transmission and distribution, vol 147, no. 5, pp 310–316 – reference: MostofiFSafaviMApplication of ABC algorithm for grid-independent hybrid hydro/photovoltaic/wind/fuel cell power generation system considering cost and reliabilityInt J Renew Energy Res201334928940 – reference: Abd-ElazimSMAliESSynergy of particle swarm optimization and bacterial foraging for TCSC damping controller designInt J WSEAS Trans Power Syst2013827484 – reference: OshiroMTanakaKSenjyuTTomaSAtsushiYSaberAFunabashiTKimCOptimal voltage control in distribution systems using PV generatorsInt J Electr Power Energy Syst201133348549210.1016/j.ijepes.2010.11.002 – reference: ZhangHChengSA new MPPT algorithm based on ANN in solar PV systemsAdv Comput Commun Control Autom LNEE2011121778410.1007/978-3-642-25541-0_11 – reference: AwanSMAslamMKhanZASaeedHAn efficient model based on artificial bee colony optimization algorithm with neural networks for electric load forecastingNeural Comput Appl2014257–81967197810.1007/s00521-014-1685-y – reference: Amrouche B, Belhamel M, Guessoum A (2007) Artificial intelligence based P&O MPPT method for photovoltaic systems. Revue des Energies Renouvelables ICRESD-07 Tlemcen 11–16 – reference: AbediniaONaslianMDBekraviMA new stochastic search algorithm bundled honeybee mating for solving optimization problemsNeural Comput Appl2014257–81921193910.1007/s00521-014-1682-1 – reference: Hui J, Sun X (2010) MPPT strategy of PV system based on adaptive fuzzy PID algorithm. In: International conference on life system modeling and intelligent computing, vol 97, pp 220–228 – reference: IshaqueKSalamZAmjadMMekhilefSAn improved particle swarm optimization (PSO)-based MPPT for PV with reduced steady-state oscillationIEEE Trans Power Electron20122783627363810.1109/TPEL.2012.2185713 – reference: YuGJJungYSChoiJYKimGSA novel two-mode MPPT control algorithm based on comparative study of existing algorithmsSol Energy200476444546310.1016/j.solener.2003.08.038 – reference: BenyoucefASChouderAKaraKSilvestreSSahedOAArtificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditionsAppl Soft Comput201532384810.1016/j.asoc.2015.03.047 – reference: NafehAAFahmyFHMahgoubOAEl-ZahabEMDeveloped algorithm of maximum power tracking for stand-alone photovoltaic systemEnergy Sour199820455310.1080/00908319808970042 – reference: CheikhMSALarbesCKebirGFTZerguerrasAMaximum power point tracking using fuzzy logic control schemeRev Energ Renouv2007103387395 – reference: Swiegers W, Enslin J (1998) An integrated maximum power point tracker for photovoltaic panels. In: Proceedings IEEE international symposium on industrial electronics, vol 1, pp 40–44 – reference: IshaqueKSalamZTaheriHShamsudinAA critical evaluation of EA computational methods for photovoltaic cell parameter extraction Based on two diode modelSol Energy2011851768177910.1016/j.solener.2011.04.015 – reference: ZhaoYZhaoXZhangYMPPT for photovoltaic system using multi-objective improved particle swarm optimization algorithmTeklanika Indones J Electr Eng2014121261268 – reference: PiegariLRizzoRAdaptive perturb and observe algorithm for photovoltaic maximum power point trackingIET Renew Power Gener20104431732810.1049/iet-rpg.2009.0006 – reference: OshabaASAliESAbd-ElazimSMMPPT control design of PV system supplied SRM using BAT search algorithmSustain Energy Grids Netw20152C516010.1016/j.segan.2015.04.002 – reference: YouesfAOshabaAEfficient fuzzy logic speed control for various types of DC motors supplied by photovoltaic system under maximum power point trackingJ Eng Sci Assiut Univ201240514551474 – reference: OshabaASAliESSwarming speed control for DC permanent magnet motor drive via pulse width modulation technique and DC/DC converterRes J Appl Sci Eng Technol201351845764583 – reference: OshabaASAliESSpeed control of induction motor fed from wind turbine via particle swarm optimization based PI controllerRes J Appl Sci Eng Technol201351845944606 – reference: SalamZAhmedJMeruguBSThe application of soft computing methods for MPPT of PV system: a technological and status reviewAppl Energy201310713514810.1016/j.apenergy.2013.02.008 – reference: BahgatABGHelwaNHAhmadGEEl ShenawyETMaximum power point tracking controller for PV systems using neural networksRenew Energy20053081257126810.1016/j.renene.2004.09.011 – reference: MehtaRMehtaVKPrinciples of electrical machines20132New Delhi, IndiaS. Chand Publishing – reference: KassemAMMPPT control design and performance improvements of a PV generator powered DC motor-pump system based on artificial neural networksInt J Electr Power Energy Syst201243909810.1016/j.ijepes.2012.04.047 – reference: PatelMRWind and solar power systems: design, analysis, and operation20062USACRC Press, Taylor & Francis Group – reference: KarabogaDBasturkBA powerful and efficient algorithm for numerical function optimization: artificial bee colony algorithmJ Glob Optim2007393459471234617810.1007/s10898-007-9149-x1149.90186 – reference: Abd-ElazimSMAliESA hybrid particle swarm optimization and bacterial foraging for optimal power system stabilizers designInt J Electr Power Energy Syst20134633434110.1016/j.ijepes.2012.10.047 – reference: Hohm DP, Ropp ME (2000) Comparative study of maximum power point tracking algorithm using an experimental programmable, maximum power point tracking test bed. In: Proceedings of 28th IEEE photovoltaic specialist conference, pp 1699–1702 – reference: AliESAbd-ElazimSMPower system stability enhancement via bacteria foraging optimization algorithmInt Arab J Sci Eng (AJSE)201338359961110.1007/s13369-012-0423-y – reference: EricksonRWMaksimovicDFundamentals of power electronics20012New YorkSpringer10.1007/b100747 – reference: Abedinia O, Wyns B, Ghasemi A (2011) Robust fuzzy PSS design using ABC. In: 10th environment and electrical energy international conference (EEEIC) Rome, Italy, pp 100–103 – reference: GitizadehMKhalilnezhadHHedayatzadehRTCSC allocation in power systems considering switching loss using MOABC algorithmElectr Eng2013952738510.1007/s00202-012-0242-x – reference: YounisMAKhatibTNajeebMAriffinAMAn improved maximum power point tracking controller for PV systems using artificial neural networkPrz Elektrotech2012883b116121 – reference: Ramaprabha R, Gothandaraman V, Kanimozhi K, Divya R, Mathur BL (2011) Maximum power point tracking using GA optimized artificial neural network for solar PV system. In: IEEE international conference on electrical energy systems, pp 264–268 – reference: IshaqueKSalamZAn improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE)Sol Energy2011852349235910.1016/j.solener.2011.06.025 – reference: OshabaASAliESBacteria foraging: A new technique for speed control of DC series motor supplied by photovoltaic systemInt J WSEAS Trans Power Syst20149185195 – reference: Brambilla A, Gambarara M, Garutti A, Ronchi F (1999) New approach to photovoltaic arrays maximum power point tracking. In: 30th annual IEEE power electronics specialists conference 1999 (PESC 99), vol 2, pp 632–637 – reference: GokmenNKaratepeEUgranliFSilvestreSVoltage band based global MPPT controller for photovoltaic systemsSol Energy201398332233410.1016/j.solener.2013.09.025 – reference: Hua C, Shen C (1997) Control of DC/DC converters for solar energy system with maximum power tracking. In: 23rd international conference on industrial electronics, control and instrumentation. IECON’97, vol 2, pp 827–832 – reference: Armstrong S, Hurley W (2004) Self regulating maximum power point tracking for solar energy systems. In: 39th international universities power engineering conference, UPEC, Bristol, UK, pp 604–609 – reference: Osheba DS (2011) Photovoltaic system fed DC motor controlled by converters. In: M.Sc. Thesis, March 2011, Menoufiya University, Egypt – reference: MastersGMRenewable and efficient electric power systems2004HobokenWiley10.1002/0471668826 – reference: AhinAEModeling and optimization of renewable energy systems2012IndiaInTech – reference: MohanNUndelandTMRobbinsWPPower electronics converters, applications, and design20033AmsterdamWiley – reference: Liu X, Lopes LAC (2004) An improvement perturbation and observation maximum power point tracking algorithm for PV arrays. In: Power electronics specialists conference, PESC’04, vol 3, pp 2005–2010 – volume: 88 start-page: 116 issue: 3b year: 2012 ident: 2067_CR20 publication-title: Prz Elektrotech – volume: 12 start-page: 261 issue: 1 year: 2014 ident: 2067_CR25 publication-title: Teklanika Indones J Electr Eng – volume: 121 start-page: 77 year: 2011 ident: 2067_CR17 publication-title: Adv Comput Commun Control Autom LNEE doi: 10.1007/978-3-642-25541-0_11 – volume: 8 start-page: 74 issue: 2 year: 2013 ident: 2067_CR31 publication-title: Int J WSEAS Trans Power Syst – volume-title: Principles of electrical machines year: 2013 ident: 2067_CR47 – volume: 25 start-page: 1921 issue: 7–8 year: 2014 ident: 2067_CR41 publication-title: Neural Comput Appl doi: 10.1007/s00521-014-1682-1 – volume: 30 start-page: 1257 issue: 8 year: 2005 ident: 2067_CR16 publication-title: Renew Energy doi: 10.1016/j.renene.2004.09.011 – ident: 2067_CR10 doi: 10.1109/ISIE.1998.707746 – ident: 2067_CR50 – volume-title: Power electronics converters, applications, and design year: 2003 ident: 2067_CR49 – ident: 2067_CR8 doi: 10.1109/PESC.1999.785575 – volume: 25 start-page: 793 year: 2013 ident: 2067_CR7 publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2013.05.022 – volume: 27 start-page: 3627 issue: 8 year: 2012 ident: 2067_CR24 publication-title: IEEE Trans Power Electron doi: 10.1109/TPEL.2012.2185713 – volume: 107 start-page: 135 year: 2013 ident: 2067_CR51 publication-title: Appl Energy doi: 10.1016/j.apenergy.2013.02.008 – volume: 76 start-page: 445 issue: 4 year: 2004 ident: 2067_CR56 publication-title: Sol Energy – ident: 2067_CR9 doi: 10.1109/PVSC.2000.916230 – volume: 95 start-page: 73 issue: 2 year: 2013 ident: 2067_CR38 publication-title: Electr Eng doi: 10.1007/s00202-012-0242-x – ident: 2067_CR54 – volume: 148 start-page: 494 issue: 6 year: 2001 ident: 2067_CR4 publication-title: IEE Proc Electron Power Appl doi: 10.1049/ip-epa:20010656 – ident: 2067_CR58 – ident: 2067_CR37 doi: 10.1109/EEEIC.2011.5874849 – volume: 40 start-page: 1455 issue: 5 year: 2012 ident: 2067_CR12 publication-title: J Eng Sci Assiut Univ – volume-title: Handbook of small electric motors year: 2001 ident: 2067_CR46 – volume: 3 start-page: 928 issue: 4 year: 2013 ident: 2067_CR42 publication-title: Int J Renew Energy Res – ident: 2067_CR53 – volume: 38 start-page: 599 issue: 3 year: 2013 ident: 2067_CR30 publication-title: Int Arab J Sci Eng (AJSE) doi: 10.1007/s13369-012-0423-y – ident: 2067_CR18 – volume: 12 start-page: 1 year: 2014 ident: 2067_CR44 publication-title: Renew Energy Power Q J – volume: 10 start-page: 387 issue: 3 year: 2007 ident: 2067_CR57 publication-title: Rev Energ Renouv – volume: 32 start-page: 38 year: 2015 ident: 2067_CR45 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2015.03.047 – volume: 2C start-page: 51 year: 2015 ident: 2067_CR52 publication-title: Sustain Energy Grids Netw doi: 10.1016/j.segan.2015.04.002 – volume: 8 start-page: 104 issue: 3 year: 2013 ident: 2067_CR14 publication-title: WSEAS Trans Power Syst – volume: 9 start-page: 185 year: 2014 ident: 2067_CR33 publication-title: Int J WSEAS Trans Power Syst – volume: 43 start-page: 90 year: 2012 ident: 2067_CR19 publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2012.04.047 – volume: 9 start-page: 1464 issue: 9 year: 2012 ident: 2067_CR39 publication-title: Am J Appl Sci doi: 10.3844/ajassp.2012.1464.1471 – volume: 85 start-page: 1768 year: 2011 ident: 2067_CR22 publication-title: Sol Energy doi: 10.1016/j.solener.2011.04.015 – ident: 2067_CR15 – volume-title: Modeling and optimization of renewable energy systems year: 2012 ident: 2067_CR2 – volume-title: Renewable and efficient electric power systems year: 2004 ident: 2067_CR1 doi: 10.1002/0471668826 – volume: 4 start-page: 317 issue: 4 year: 2010 ident: 2067_CR6 publication-title: IET Renew Power Gener doi: 10.1049/iet-rpg.2009.0006 – volume: 25 start-page: 1967 issue: 7–8 year: 2014 ident: 2067_CR40 publication-title: Neural Comput Appl doi: 10.1007/s00521-014-1685-y – volume: 5 start-page: 4576 issue: 18 year: 2013 ident: 2067_CR28 publication-title: Res J Appl Sci Eng Technol doi: 10.19026/rjaset.5.4377 – volume-title: Wind and solar power systems: design, analysis, and operation year: 2006 ident: 2067_CR3 – volume: 3 start-page: 238 issue: 12 year: 2013 ident: 2067_CR29 publication-title: Int J Adv Res Comput Sci Softw Eng – volume: 33 start-page: 485 issue: 3 year: 2011 ident: 2067_CR11 publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2010.11.002 – ident: 2067_CR23 doi: 10.1109/ICEES.2011.5725340 – volume: 39 start-page: 459 issue: 3 year: 2007 ident: 2067_CR36 publication-title: J Glob Optim doi: 10.1007/s10898-007-9149-x – volume: 72 start-page: 93 issue: 1 year: 2014 ident: 2067_CR43 publication-title: Energy doi: 10.1016/j.energy.2014.05.011 – ident: 2067_CR5 – volume: 9 start-page: 428 year: 2014 ident: 2067_CR34 publication-title: Int J WSEAS Trans Power Syst – volume-title: Fundamentals of power electronics year: 2001 ident: 2067_CR48 doi: 10.1007/b100747 – volume: 5 start-page: 4594 issue: 18 year: 2013 ident: 2067_CR27 publication-title: Res J Appl Sci Eng Technol doi: 10.19026/rjaset.5.4380 – ident: 2067_CR35 – ident: 2067_CR13 doi: 10.1007/978-3-642-15853-7_28 – volume: 98 start-page: 322 issue: 3 year: 2013 ident: 2067_CR26 publication-title: Sol Energy doi: 10.1016/j.solener.2013.09.025 – volume: 85 start-page: 2349 year: 2011 ident: 2067_CR21 publication-title: Sol Energy doi: 10.1016/j.solener.2011.06.025 – volume: 46 start-page: 334 year: 2013 ident: 2067_CR32 publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2012.10.047 – volume: 20 start-page: 45 year: 1998 ident: 2067_CR55 publication-title: Energy Sour doi: 10.1080/00908319808970042 |
| SSID | ssj0004685 |
| Score | 2.3654556 |
| Snippet | Maximum power point tracking (MPPT) is used in photovoltaic (PV) systems to maximize its output power. A new MPPT system has been suggested for PV–DC motor... Maximum power point tracking (MPPT) is used in photovoltaic (PV) systems to maximize its output power.A new MPPT system has been suggested for PV–DC motor pump... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 353 |
| SubjectTerms | Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Control systems design Controllers D C motors Data Mining and Knowledge Discovery Electric converters Electric motors Electric potential Genetic algorithms Image Processing and Computer Vision Maximum power tracking Optimization Original Article Photovoltaic cells Probability and Statistics in Computer Science Speed control Swarm intelligence Voltage Voltage converters (DC to DC) |
| Title | PI controller design using ABC algorithm for MPPT of PV system supplying DC motor pump load |
| URI | https://link.springer.com/article/10.1007/s00521-015-2067-9 https://www.proquest.com/docview/1880794060 |
| Volume | 28 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: Academic Search Ultimate - eBooks customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1433-3058 dateEnd: 20241105 omitProxy: true ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: ABDBF dateStart: 19990101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1433-3058 dateEnd: 20241105 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: ADMLS dateStart: 19930301 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVLSH databaseName: SpringerLink Journals customDbUrl: mediaType: online eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: AFBBN dateStart: 19970301 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1433-3058 dateEnd: 20241105 omitProxy: true ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: BENPR dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK - Czech Republic Consortium customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: AGYKE dateStart: 19970101 isFulltext: true titleUrlDefault: http://link.springer.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink Journals (ICM) customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: U2A dateStart: 19970101 isFulltext: true titleUrlDefault: http://www.springerlink.com/journals/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3JTsMwEB1BuXBhR5Sl8oETKCi1Eyc-llL2oghaBOIQxY5dEKVFNFz4esZpUhYBEpdEcpwoscee5_jNG4DtgHITJlo4gbYpzJQrnQQ9taPqigVBKFIvl9hoX_Djrnd6498Ucdyjku1ebknmM_Uk2M3-wbRLX9-xkuOOmIaZXG6rAjONo9uz1qdwyDwTJy5cLKnHY-Vm5k8P-eqOPjDmt23R3NsczkOnfM8xyeRx7zWTe-rtm4TjPz9kAeYK9EkaY3NZhCk9WIL5MrMDKQb6MtxFJ6QgsfexPM1pHsRy5Huksd8kSb83fHnI7p8IYl7SjqIOGRoSXZOxMDQZ2VyhNoCKHDQJGgNWeka7If1hkq5A97DVaR47RRoGR7E6zxwacm18qozWyjVSaGZM4jMlhEJ4pDxusAAPNEw59UKhAym9RDDpUfR1zGerUBkMB3oNCGUmTQJWZ57PEKcZwSVXKa-nKtSJDGUV3LI3YlVolNtUGf14oq6cN16MjRfbxotFFXYmtzyPBTr-qrxZdnFcjNVRbBXpcFZyuVuF3bLHPl3-7WHr_6q9AbPUIoKc8L0JlezlVW8hnslkDe33oH1-VSvsGM_7rYvosgbTXdp4BxUz7YA |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwELWgDLDwjSgU8MAEipTEiROPpVC10FYZWoTEENmOXYbQVLT8f85uUgoCJJYMzsXDne17zt29Q-gy8qmOuWJOpEwLM-kKh4OndqQnSRTFLAssxUZ_QDuj4P4pfCrruGdVtnsVkrQn9bLYzfzBNFff0DGU4w5bRxuGv8oQ5o_85koxpO3DCdcWk9ITkCqU-dMUX53RJ8L8FhS1vqa9i7ZLkIibC6vuoTU12Uc7VQMGXO7HA_ScdHGZa57DeGazMbBJZR_j5k0L83xcwOX_5RUDNMX9JBniQuPkES_4m_HMtPQ0dU74toXBZiA0BfPivODZIRq174atjlN2S3Ak8ejc8WOqdOhLrZR0tWCKaM1DIhmTgGJkQDUMwMOPM-oHMVOREAFnRIASAaSE5AjVJsVEHSPsE53xiHgkCAnAKc2ooDKjXiZjxUUs6sit1JbKkkrcdLTI0yUJstV0CppOjaZTVkdXy0-mCx6Nv4QblS3SckvNUkMcB4eHS906uq7ss_L6t8lO_iV9gTY7w34v7XUHD6doyzdO3OZoN1Bt_vauzgCCzMW5XXIfDfnPiw |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELagSIiFN6JQwAMTKGpqO048lpaKAq0ytKgSQxQ7dkEKSdWG_4-dRykIkFgyOBcPd7bvnLv7PgAuXUSVF0pmudJQmAmbW6H21JZoCey6HotIDrExGNK7MbmfOJOS53RRVbtXKcmip8GgNCVZcxap5rLxzfzNNNdgxzLw4xZbBxvE4CToBT1G7ZXGyJyTU19hTHkPwVVa86cpvjqmz2jzW4I09zu9XbBdBoywXVh4D6zJZB_sVGQMsNybB-DZ78Oy7jzW41FemQFNWfsUtm86MIyn6fw1e3mDOkyFA98fwVRB_wkWWM5wYeg9Tc8T7Hagtp8WmmlTwzgNo0Mw7t2OOndWyZxgCdyimYU8KpWDhJJS2IoziZUKHSwYEzqiEYQqPaAfyIsoIh6TLuckZJgTpN0TdvARqCVpIo8BRFhFoYtbmDhYh1aKUU5FRFuR8GTIPV4HdqW2QJSw4obdIg6WgMi5pgOt6cBoOmB1cLX8ZFZgavwl3KhsEZTbaxEYEDl9kNjUroPryj4rr3-b7ORf0hdg0-_2gsf-8OEUbCHjz_Ny7QaoZfN3eaajkYyf5yvuA1LL08c |
| 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=PI+controller+design+using+ABC+algorithm+for+MPPT+of+PV+system+supplying+DC+motor+pump+load&rft.jtitle=Neural+computing+%26+applications&rft.au=Oshaba%2C+A+S&rft.au=Ali%2C+E+S&rft.au=Abd+Elazim%2C+S+M&rft.date=2017-02-01&rft.pub=Springer+Nature+B.V&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=28&rft.issue=2&rft.spage=353&rft.epage=364&rft_id=info:doi/10.1007%2Fs00521-015-2067-9&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon |