Please wait a minute...

Frontiers in Energy

Front. Energy    2019, Vol. 13 Issue (1) : 131-148     https://doi.org/10.1007/s11708-017-0446-x
RESEARCH ARTICLE
Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and RBFNSM for wind turbine in the grid connected mode
Alireza REZVANI1(), Ali ESMAEILY2, Hasan ETAATI3, Mohammad MOHAMMADINODOUSHAN4
1. Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh 3919715179, Iran Water and Power Resources Development Company (IWPCO), Iran
2. Department of Electrical Engineering, Karaj Branch, Islamic Azad University, Karaj 3148635731, Iran
3. Iran Water and Power Resources Development Company (IWPCO), Iran
4. Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
Download: PDF(5532 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is intermittent because of depending on weather conditions. Therefore, the wind power can be considered to assist for a stable and reliable output from the PV generation system for loads and improve the dynamic performance of the whole generation system in the grid connected mode. In this paper, a novel topology of an intelligent hybrid generation system with PV and wind turbine is presented. In order to capture the maximum power, a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. The average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison with the conventional methods. The pitch angle of the wind turbine is controlled by radial basis function network-sliding mode (RBFNSM). Different conditions are represented in simulation results that compare the real power values with those of the presented methods. The obtained results verify the effectiveness and superiority of the proposed method which has the advantages of robustness, fast response and good performance. Detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink.

Keywords photovoltaic      wind turbine      hybrid system      fuzzy logic controller      genetic algorithm      RBFNSM     
Corresponding Authors: Alireza REZVANI   
Online First Date: 24 February 2017    Issue Date: 20 March 2019
 Cite this article:   
Alireza REZVANI,Ali ESMAEILY,Hasan ETAATI, et al. Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and RBFNSM for wind turbine in the grid connected mode[J]. Front. Energy, 2019, 13(1): 131-148.
 URL:  
http://journal.hep.com.cn/fie/EN/10.1007/s11708-017-0446-x
http://journal.hep.com.cn/fie/EN/Y2019/V13/I1/131
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Alireza REZVANI
Ali ESMAEILY
Hasan ETAATI
Mohammad MOHAMMADINODOUSHAN
Fig.1  Equivalent circuit of one PV array
IMP ( Rated current)/AVMP( Rated voltage)/VPmax(Rated power)/WVoc ( Open circuit voltage)Isc ( Short circuit current)Np (number of parallel cells)Ns (number of series cells)
4.9418.659022.325.24136
Tab.1  Red sun 90 W module under STC
Number of design variablePopulation sizeCrossover constant/%Mutation rate/%Maximum generations
120801020
Tab.2  Genetic algorithm parameters
Fig.2  Diagram of the discussed method
Fig.3  Structure of fuzzy-neural hybrid method
Fig.4  The membership function of fuzzy logic
Rule number?Ppv?Vpv?ref
1PPP
2PNN
3NPN
4NNP
1PPP
Tab.3  Fuzzy rules
Fig.5  
Fig.6  
Fig.7  Training data of ANN controller
Fig.8  Validation data of ANN controller
Fig.9  Testing data of ANN controller
Fig.10  Simulated results of variations of irradiance) in case 1
AlgorithmTracking efficiency (avg)Response time (avg)/sOscillation around MPP (avg)/W
Hybrid fuzzy-neural99.120.102.52
Fuzzy logic97.350.177.31
P&O95.140.2829.12
Tab.4  Tracking efficiency and response time comparison for different MPPT techniques under irradiance variation
Time/sReal value/ WHybrid fuzzy-neural/WFuzzy logic/WP&O/W
0–41600159815551533
4–83530352734703455
8–111600159815551533
11–1444004398434844331
Tab.5  Output power values of solar array (watt) in various irradiation conditions
Fig.11  Simulated results for wind system in case1
Fig.12  Simulated results of variations of irradiance) in case 2
AlgorithmTracking efficiency ( avg)Response time (avg)/sOscillation around MPP (avg)/W
Hybrid fuzzy-neural99.450.142.12
Fuzzy logic97.620.197.21
P&O94.840.2726.12
Tab.6  Tracking efficiency and response time comparison for different MPPT techniques under temperature variation
Time/sReal value/WHybrid fuzzy-neural/WFuzzy logic/WP&O/W
0–44298429742614246
8–141952194919041887
Tab.7  Output power values of solar array (watt) in various temperature conditions
Fig.13  Simulated results for wind system in case 2
Controller typeWind speed/(m·s–1)Power coefficient (Cp)Pitch angle/(°)Average power/kW
RBFNSM120.475–0.0959.2
PI120.461–0.6658.1
Tab.8  Performance comparison of RBFNSM and PI Controller
1 ARezvani, MGandomkar, MIzadbakhsh, AAhmadi. Environmental/economic scheduling of a micro-grid with renewable energy resources. Journal of Cleaner Production, 2015, 87: 216–226
https://doi.org/10.1016/j.jclepro.2014.09.088
2 MIzadbakhsh, MGandomkar, ARezvani, AAhmadi. Short-term resource scheduling of a renewable energy based micro grid. Renewable Energy, 2015, 75: 598–606
https://doi.org/10.1016/j.renene.2014.10.043
3 ARezvani, MIzadbakhsh, and MGandomkar. Microgrid dynamic responses enhancement using artificial neural network-genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds. International Journal of Numerical Modeling, Electronic Networks, Devices and Fields, 2015, 40(40): C7-415–C7-416
4 ARezvani, MIzadbakhsh, MGandomkar. Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine. Energy Systems, 2015, 6(4): 551–584
https://doi.org/10.1007/s12667-015-0156-6
5 SVafaei, MGandomkar, ARezvani, MIzadbakhsh. Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances. Frontiers in energy, 2015, 9(3): 322–334
6 SMorimoto, HNakayama, MSanada, YTakeda. Sensorless output maximization control for variable-speed wind generation system using IPMSG. IEEE Transactions on Industry Applications, 2005, 41(1): 60–67
https://doi.org/10.1109/TIA.2004.841159
7 C MMing, C HChen. Intelligent control of agrid-connected wind-photovoltaic hybrid power systems. Electrical Power and Energy Systems, 2014, 55(2): 554–561
8 M MAlgazar, HAl-Monier, H AEL-halim, M E E KSalem. Maximum power point tracking using fuzzy logic control. International Journal of Electrical Power & Energy Systems, 2012, 39(1): 21–28
https://doi.org/10.1016/j.ijepes.2011.12.006
9 CLiu, BWu, RCheung. Advanced algorithm for MPPT control of photovoltaic systems. In: Proceeding of the Canadian Solar Buildings Conference. Montreal, Canada, 2004
10 A KRai, N DKaushika, BSingh, NAgarwal. Simulation model of ANN based maximum power point tracking controller for solar PV system. Solar Energy Materials and Solar Cells, 2011, 95(2): 773–778
https://doi.org/10.1016/j.solmat.2010.10.022
11 AChaouachi, R MKamel, KNagasaka. A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system. Solar Energy, 2010, 84(12): 2219–2229
https://doi.org/10.1016/j.solener.2010.08.004
12 R KKharb, S LShimi, SChatterji, M FAnsari. Modeling of solar PV module and maximum power point tracking using ANFIS. Renewable and Sustainable Energy, 2014, 33(5): 602–612
https://doi.org/10.1016/j.rser.2014.02.014
13 AAfsin, AKulaksiz. Training data optimization for ANNs using genetic algorithms to enhance MPPT efficiency of a stand-alone PV system. Turkish Journal of Electrical Engineering and Computer Sciences, 2012, 20(2): 241–254
14 CBen Salah, MOuali. Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems. Electric Power Systems Research, 2011, 81(1): 43–50
https://doi.org/10.1016/j.epsr.2010.07.005
15 ARezvani, MIzadbakhsh, MGandomkar. Enhancement of hybrid dynamic performance using ANFIS for fast varying solar radiation and fuzzy logic controller in high speeds wind. Journal of Electrical Systems, 2015, 11(1): 11–26
16 R MVincheh, AKargar, G AMarkadeh. A hybrid control method for maximum power point tracking (MPPT) in photovoltaic systems. Arabian Journal for Science and Engineering, 2014, 39(6): 4715–4725
https://doi.org/10.1007/s13369-014-1056-0
17 MIzadbakhsh, ARezvani, MGandomkar. Improvement of microgrid dynamic performance under fault circumstances using ANFIS for fast varying solar radiation and fuzzy logic controller for wind system. Archives of Electrical Engineering, 2014, 63(4): 551–578
https://doi.org/10.2478/aee-2014-0038
18 SHadji, FKrim, J PGaubert. Development of an algorithm of maximum power point tracking for photovoltaic systems using genetic algorithms. In: 7th International Workshop on Systems, Signal Processing and their Applications (WOSSPA). 2011, 43–46
19 VBakić, MPezo, ŽanaStevanović, M Živković, BGrubor. Dynamical simulation of PV/Wind hybrid energy conversion system. Energy, 2012, 45(1): 324–328
20 SSamia, GAhmed. Modeling and simulation of hybrid systems PV/Wind/Battery connected to the grid. International Conference of Automatic Control, Nantou, Taiwan, China, 2013
21 N HSamrat, N BAhmad, I AChoudhury, Z BTaha. Modeling, control, and simulation of battery storage photovoltaic-wave energy hybrid renewable power generation systems for island electrification in Malaysia. Scientific World Journal, 2014, 2014(38): 278–279
22 BBhandari, S RPoudel, K TLee, S HAhn. Mathematical modeling of hybrid renewable energy system: a review on small hydro-solar-wind power generation. International Journal of Precision Engineering and Manufacturing-green Technology, 2014, 1(2): 157–173
https://doi.org/10.1007/s40684-014-0021-4
23 JAhmed, ZSalam. An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency. Applied Energy, 2015, 150: 97–108
https://doi.org/10.1016/j.apenergy.2015.04.006
24 P CChen, P YChen, YLiu, J HChen, Y FLuo. A comparative study on maximum power point tracking techniques for photovoltaic generation systems operating under fast changing environments. Solar Energy, 2015, 119: 261–276
https://doi.org/10.1016/j.solener.2015.07.006
25 W MLin, C MHong. A new Elman neural network-based control algorithm for adjustable-pitch variable speed wind energy conversion systems. IEEE Transactions on Power Electronics, 2011, 26(2): 473–481
https://doi.org/10.1109/TPEL.2010.2085454
26 S CLin, Y YChen. RBF network based sliding mode control. IEEE International Conference on Systems, 1994, 2(2): 1957–1961
27 FBlaabjerg, RTeodorescu, MLiserre, A VTimbus. Overview of control and grid synchronization for distributed power generation systems. IEEE Transactions on Industrial Electronics, 2006, 53(5): 1398–1409
https://doi.org/10.1109/TIE.2006.881997
Related articles from Frontiers Journals
[1] Maurizio FACCIO, Mauro GAMBERI, Marco BORTOLINI, Mojtaba NEDAEI. State-of-art review of the optimization methods to design the configuration of hybrid renewable energy systems (HRESs)[J]. Front. Energy, 2018, 12(4): 591-622.
[2] Xiaojing LV, Yu WENG, Xiaoyi DING, Shilie WENG, Yiwu WENG. Technological development of multi-energy complementary system based on solar PVs and MGT[J]. Front. Energy, 2018, 12(4): 509-517.
[3] Qing NI, Hassan ALSHEHRI, Yue YANG, Hong YE, Liping WANG. Plasmonic light trapping for enhanced light absorption in film-coupled ultrathin metamaterial thermophotovoltaic cells[J]. Front. Energy, 2018, 12(1): 185-194.
[4] Eric TERVO, Elham BAGHERISERESHKI, Zhuomin ZHANG. Near-field radiative thermoelectric energy converters: a review[J]. Front. Energy, 2018, 12(1): 5-21.
[5] Przemyslaw JANIK, Grzegorz KOSOBUDZKI, Harald SCHWARZ. Influence of increasing numbers of RE-inverters on the power quality in the distribution grids: A PQ case study of a representative wind turbine and photovoltaic system[J]. Front. Energy, 2017, 11(2): 155-167.
[6] Nitesh Ganesh BHAT, B. Rajanarayan PRUSTY, Debashisha JENA. Cumulant-based correlated probabilistic load flow considering photovoltaic generation and electric vehicle charging demand[J]. Front. Energy, 2017, 11(2): 184-196.
[7] Kinattingal SUNDARESWARAN,Kevin Ark KUMAR,Payyalore Raman VENKATESWARAN,Sankaran PALANI. Solar photovoltaic fed dual input LED lighting system with constant illumination control[J]. Front. Energy, 2016, 10(4): 473-478.
[8] T A BINSHAD,K VIJAYAKUMAR,M KALEESWARI. PV based water pumping system for agricultural irrigation[J]. Front. Energy, 2016, 10(3): 319-328.
[9] Amir AHADI,Seyed Mohsen MIRYOUSEFI AVAL,Hosein HAYATI. Generating capacity adequacy evaluation of large-scale, grid-connected photovoltaic systems[J]. Front. Energy, 2016, 10(3): 308-318.
[10] Himani,Ratna DAHIYA. Condition monitoring of a wind turbine generator using a standalone wind turbine emulator[J]. Front. Energy, 2016, 10(3): 286-297.
[11] Amir AHADI,Hosein HAYATI,Joydeep MITRA,Reza ABBASI-ASL,Kehinde AWODELE. A new method for estimating the longevity and degradation of photovoltaic systems considering weather states[J]. Front. Energy, 2016, 10(3): 277-285.
[12] S. SURENDER REDDY,Jae Young PARK,Chan Mook JUNG. Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm[J]. Front. Energy, 2016, 10(3): 355-362.
[13] Namani RAKESH,T. Venkata MADHAVARAM. Performance enhancement of partially shaded solar PV array using novel shade dispersion technique[J]. Front. Energy, 2016, 10(2): 227-239.
[14] Abdelhak DIDA,Djilani BENATTOUS. A complete modeling and simulation of DFIG based wind turbine system using fuzzy logic control[J]. Front. Energy, 2016, 10(2): 143-154.
[15] Louar FATEH,Ouari AHMED,Omeiri AMAR,Djellad ABDELHAK,Bouras LAKHDAR. Modeling and control of a permanent magnet synchronous generator dedicated to standalone wind energy conversion system[J]. Front. Energy, 2016, 10(2): 155-163.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed