Abstract
Solar Photovoltaic (PV) systems are playing a major role in the present electrical energy systems. The solar PV gives nonlinear I–V and P–V characteristics. As a result, it is difficult to extract the maximum power of the solar PV. Under Partial Shading Conditions (PSCs), the solar PV characteristics consist of multiple local Maximum Power Points (MPPs) and one global MPP. The classical Maximum Power Point Tracking (MPPT) techniques cannot track the global MPP under PSCs. Accordingly, this work aims to study the performance of five soft computing MPPT techniques. The studied five soft computing MPPT techniques are Modified Variable Step Size-Radial Basis Functional Network (MVSS-RBFN), Modified Hill-Climb with Fuzzy Logic Controller (MHC-FLC), Artificial Neuro-Fuzzy Inference System (ANFIS), Perturb and Observe with Practical Swarm Optimization (P&O-PSO), and Adaptive Cuckoo Search (ACS). The comparative performance analysis of five soft computing techniques has been carried out against the Variable Step Size-Incremental Resistance (VSS-INR), and Variable Step Size-Feedback Controller (VSS-FC)-based MPPT techniques. The performance analysis of seven MPPT techniques has been done by considering the parameters are steady-state settling time, MPP tracking speed, algorithm complexity, PV array dependency, handling of partial shading, and efficiency.
















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- P MPP :
-
Maximum Peak Power of solar PV, 249.3 W
- V MPP :
-
A peak-Peak voltage of PV, 30 V
- I MPP :
-
Peak-Peak current of PV, 8.31 A
- N pp :
-
Strings connected in parallel, 1
- N ss :
-
Each string series-connected modules, 3
- N s :
-
Each module cells, 60
- r s :
-
Series resistance of PV cell, 0.2914 Ω
- r p :
-
Parallel resistance, 314.76 Ω
- V oc :
-
Open-circuit voltage of PV, 36.8 V
- I sc -n :
-
Short-circuit current of PV, 8.83 A
- I 0 - n :
-
Saturation current of the diode, 1.013*exp10−10A
- T n :
-
Standard temperature, 25 °C
- G n :
-
Nominal irradiation, 1000 W/m2
- Kv :
-
Temperature coefficient of voltage, − 0.33%/°C
- K i :
-
Temperature coefficient of current, 0.063%/°C
- a1, a2 :
-
Diode ideality factors, 0.984, 1
- T :
-
Operating Temperature of PV module, 45 °C
References
Inglesi-Lotz, R.: The impact of renewable energy consumption to economic growth: A panel data application. Energy Econ. 53, 58–63 (2016)
Evans, A., Vladimir, S., Tim, J.E.: Assessment of sustainability indicators for renewable energy technologies. Renew. Sustain. Energy Rev. 13(5), 1082–1088 (2009)
Güney, T.: Renewable energy, non-renewable energy and sustainable development. Int. J. Sustain. Develop. World Ecol. 26(5), 389–397 (2019)
Fathabadi, H.: Utilization of electric vehicles and renewable energy sources used as distributed generators for improving characteristics of electric power distribution systems. Energy. 90, 1100–1110 (2015)
Iqbal, M., et al.: Optimization classification, algorithms and tools for renewable energy: A review. Renew. Sustain. Energy Rev. 39, 640–654 (2014)
Brouwer, A.S., et al.: Impacts of large-scale Intermittent Renewable Energy Sources on electricity systems, and how these can be modeled. Renew. Sustain. Energy Rev. 33, 443–466 (2014)
Wang, Z., et al.: Applications of solar water heating system with phase change material. Renew. Sustain. Energy Rev. 52, 645–652 (2015)
Crisostomo, F., et al.: Experimental testing of SiNx/SiO2 thin film filters for a concentrating solar hybrid PV/T collector. Renew. Energy 72, 79–87 (2014)
Chikate, B.V.: The factors affecting the performance of solar cell. Int. J. Computer Appl. 11, 0975–8887 (2015)
Nguyen, X.H., Minh, P.N.: Mathematical modeling of photovoltaic cell/module/arrays with tags in Matlab/Simulink. Environ. Syst. Res. 4(1), 24 (2015)
Tamrakar, V., Gupta, S.C., Sawle, Y.: Study of characteristics of single and double diode electrical equivalent circuit models of solar PV module. Published In 2015 International Conference on Energy Systems and Applications (2015). https://doi.org/https://doi.org/10.1109/ICESA.2015.7503362.
Selmi, T., Mohammed, A.-N., Mamoon, A.: Analysis and Investigation of two-diode solar cells using MATLAB/Simulink. Int. J. Renew. Energy Res. (IJRER). 4(1), 99–102 (2014)
Hejri, M., et al.: On the parameter extraction of a five-parameter double-diode model of photovoltaic cells and modules. IEEE J. Photovoltaics. 4(3), 915–923 (2014)
Basha, X., Hussaian, C.H., et al: Mathematical Design and Analysis of Photovoltaic Cell Using MATLAB/Simulink Soft Computing for Problem Solving, Springer, Singapore. pp. 711–726 (2020)
Chandel, T.A., Mohd, Y.Y., Arifuddin, M.: Modeling and simulation of photovoltaic cell using single diode solar cell and double diode solar cell model. Int. J. Innovative Technol. Explor. Eng. (IJITEE). 8.10, (2019)
Podder, A.K., Naruttam, K.R., Hemanshu, R.P.: MPPT methods for solar PV systems: a critical review based on tracking nature. IET Renew. Power Gener. 13(10), 1615–1632 (2019)
Faria, J., et al.: Power management control strategy based on artificial neural networks for standalone PV applications with a hybrid energy storage system. Energies. 12(5), 902 (2019)
Ali, A., et al.: Investigation of MPPT Techniques under Uniform and Non-Uniform Solar Irradiation Condition–A Retrospection. IEEE Access. 8, 127368–127392 (2020)
Ali, A., et al.: Review of online and soft computing maximum power point tracking techniques under non-uniform solar irradiation conditions. Energies. 13(12), 3256 (2020)
Pathak, P.K., Anil, K.Y., Alvi, P.A.: Advanced solar MPPT techniques under uniform and non-uniform irradiance: a comprehensive review. J. Solar Energy Eng. 142, 4 (2020)
Sujith, S., Kathiravan, N.: Comparison of fuzzy logic based MPPT with P & O for solar PV pumping system. In: 2016 IEEE international conference on emerging technological trends (ICETT) (2016). https://doi.org/https://doi.org/10.1109/ICETT.2016.7873755
Althomali, R., Mohammed, A.: Improved MPPT controllers for wind generation system based on hill climbing technique. Published In 2017 Intl Conf on Advanced Control Circuits Systems (ACCS) Systems & 2017 Intl Conf on New Paradigms in Electronics & Information Technology (PEIT) (2017). https://doi.org/https://doi.org/10.1109/ACCS-PEIT.2017.8303032
Loukriz, A., Mourad, H., Sabir, M.: Simulation and experimental design of a new advanced variable step size Incremental Conductance MPPT algorithm for PV systems. ISA Trans. 62, 30–38 (2016)
Rezk, H.: Performance of incremental resistance MPPT based proton exchange membrane fuel cell power system. In: 2016 Eighteenth International Middle East Power Systems Conference (MEPCON) (2016). https://doi.org/https://doi.org/10.1109/MEPCON.2016.7836891
John, R., Sheik Mohammed, S., Zachariah, R.: Variable step size Perturb and observe MPPT algorithm for standalone solar photovoltaic system. In: 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE (2017). https://doi.org/https://doi.org/10.1109/ITCOSP.2017.8303163
Gopal, Y., Mahendra, L., Dinesh, B.: Genetic algorithm based cascaded H-bridge multilevel inverters for PV system with MPPT technique. In: 2017 International conference on information, communication, instrumentation and control (ICICIC). IEEE (2017). https://doi.org/https://doi.org/10.1109/ICOMICON.2017.8279127
Taborda, D.M.G., Zdravkovic, L.: Application of a Hill-Climbing technique to the formulation of a new cyclic nonlinear elastic constitutive model. Comput. Geotech. 43, 80–91 (2012)
Li, C., et al.: A high-performance adaptive incremental conductance MPPT algorithm for photovoltaic systems. Energies. 9(4), 288 (2016)
Karami, N., Nazih, M., Rachid, O.: General review and classification of different MPPT Techniques. Renew. Sustain. Energy Rev. 68, 1–18 (2017)
de Carvalho, J.T., Andres, O.S., Alberto, S.L.: One cycle control based maximum power point tracker applied in photovoltaic systems. IEEE Latin Am. Trans. 14(2), 602–609 (2016)
Lasheen, M., et al.: Adaptive reference voltage-based MPPT technique for PV applications. IET Renew. Power Gener. 11(5), 715–722 (2017)
Labidi, Z.R., Horst, S., Abdelkader, M.: A systematic controller design for a photovoltaic generator with boost converter using integral state feedback control. Eng. Technol. Appl. Sci. Res. 9(2), 4030–4036 (2019)
Abdel-Rahim, O., Haoyu, W.: A new high gain DC-DC converter with model-predictive-control based MPPT technique for photovoltaic systems. CPSS Trans. Power Elect. Appl. 5(2), 191–200 (2020)
Mumtaz, S., et al.: Adaptive feedback linearization based neurofuzzy maximum power point tracking for a photovoltaic system. Energies. 11(3), 606 (2018)
Singh, B., Nishant, K., Bijaya, K.P.: Steepest descent Laplacian regression based neural network approach for optimal operation of grid supportive solar PV generation. Express Briefs, IEEE Transactions on Circuits and Systems II (2020)
Rezk, H., Ahmed, F., Almoataz, Y.A.: A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions. Renew. Sustain. Energy Rev. 74, 377–386 (2017)
Verma, D., et al.: Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems. Renew. Sustain. Energy Rev. 54, 1018–1034 (2016)
Ishaque, K., Zainal, S.: A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition. Renew. Sustain. Energy Rev. 19, 475–488 (2013)
Muthuramalingam, M., Manoharan, P.S.: Comparative analysis of distributed MPPT controllers for partially shaded standalone photovoltaic systems. Energy Convers. Manage. 86, 286–299 (2014)
Punitha, K., Devaraj, D., Sakthivel, S.: Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions. Energy. 62, 330–340 (2013)
M'Sirdi, N.K., et al.: The best mppt algorithms by vsas approach for renewable energy sources (res). In: 2014 3rd International Symposium on Environmental Friendly Energies and Applications (2014). https://doi.org/https://doi.org/10.1109/EFEA.2014.7059994
Shaiek, Y., et al.: Comparison between conventional methods and GA approach for maximum power point tracking of shaded solar PV generators. Sol. Energy 90, 107–122 (2013)
Soufyane, B., et al.: Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions. Appl. Soft Computing. 32, 38–48 (2015)
Salam, Z., et al.: The application of soft computing methods for MPPT of PV system: a technological and status review. Appl. Energy 107, 135–148 (2013)
Soufi, Y., Mohcene, B., Sami, K.: Fuzzy-PSO controller design for maximum power point tracking in photovoltaic system. Int. J. Hydrogen Energy 42(13), 8680–8688 (2017)
Cheng, P.-C., et al.: Optimization of a fuzzy-logic-control-based MPPT algorithm using the particle swarm optimization technique. Energies. 8(6), 5338–5360 (2015)
Rad, M.R., et al.: Using ANFIS, PSO, FCN in cooperation with fuzzy controller for MPPT of photovoltaic arrays. Adv Electr Eng Syst. 1(1), 1–9 (2012)
Amara, K., et al.: Improved performance of a PV solar panel with adaptive neuro fuzzy inference system ANFIS based MPPT. In: 2018 IEE 7th International Conference on Renewable Energy Research and Applications (2018). https://doi.org/https://doi.org/10.1109/ICRERA.2018.8566818
Reddy, K.J., Sudhakar, N.: ANFIS-MPPT control algorithm for a PEMFC system used in electric vehicle applications. Int. J. Hydrogen Energy 44(29), 15355–15369 (2019)
Farayola, A.M., et al.: Distributive MPPT approach using ANFIS and perturb&observe techniques under uniform and partial shading conditions. In: Artificial Intelligence and Evolutionary Computations in Engineering Systems. pp. 27–37. Springer, Singapore (2018)
Mahmod, M., Altwallbah, N., et al.: An enhanced adaptive perturb and observe technique for efficient maximum power point tracking under partial shading conditions. Appl. Sci. 10(11), 3912 (2020)
Batarseh, M.G., Muhy, E.Z.: Hybrid maximum power point tracking techniques: a comparative survey, suggested classification and uninvestigated combinations. Sol. Energy 169, 535–555 (2018)
Bollipo, R.B., et al.: Critical review on PV MPPT techniques: classical, intelligent and optimization. IET Renew. Power Gener. 14(9), 1433–1452 (2020)
Dolara, A., et al.: An evolutionary-based MPPT algorithm for photovoltaic systems under dynamic partial shading. Appl. Sci. 8(4), 558 (2018)
Guichi, A., et al.: A new method for intermediate power point tracking for PV generator under partially shaded conditions in hybrid system. Sol. Energy 170, 974–987 (2018)
Youssef, A., et al.: Reconfigurable generic FPGA implementation of fuzzy logic controller for MPPT of PV systems. Renew. Sustain. Energy Rev. 82, 1313–1319 (2018)
Arsalan, M., et al.: MPPT for photovoltaic system using nonlinear back stepping controller with integral action. Sol. Energy 170, 192–200 (2018)
Duman, A.C., Önder, G.: Techno-economic analysis of off-grid PV/wind/fuel cell hybrid system combinations with a comparison of regularly and seasonally occupied households. Sustain. Cities Soc. 42, 107–126 (2018)
Fathabadi, H.: Novel standalone hybrid solar/wind/fuel cell power generation system for remote areas. Sol. Energy 146, 30–43 (2017)
Majidi, M., et al.: A multi-objective model for optimal operation of a battery/PV/fuel cell/grid hybrid energy system using weighted sum technique and fuzzy satisfying approach considering responsible load management. Sol. Energy 144, 79–89 (2017)
Cavalcanti, M.C., et al.: Hybrid maximum power point tracking technique for PV modules based on a double-diode model. IEEE Trans. Ind. Elect. 12, 98 (2020)
Lasheen, M., et al.: Maximum power point tracking using Hill Climbing and ANFIS techniques for PV applications: a review and a novel hybrid approach. Energy Convers. Manage. 171, 1002–1019 (2018)
Joisher, M., et al.: A hybrid evolutionary-based MPPT for photovoltaic systems under partial shading conditions. IEEE Access. 8, 38481–38492 (2020)
Kamarzarrin, M., Mohammad, H.R.: Intelligent sliding mode adaptive controller design for wind turbine pitch control system using PSO-SVM in presence of disturbance. J. Control Autom. Elect. Syst. 3, 1–14 (2020)
Atia, D.M.: Global maximum power point tracking-based computational intelligence techniques. In: Modern maximum power point tracking techniques for photovoltaic energy systems. pp. 131–163. Springer, Cham (2020)
Pathy, S., et al.: Nature-inspired MPPT algorithms for partially shaded PV systems: a comparative study. Energies. 12(8), 1451 (2019)
Farzaneh, J., Reza, K., Ali, K.: A novel fast maximum power point tracking for a PV system using hybrid PSO-ANFIS algorithm under partial shading conditions. Int. J. Ind. Elect. Control Optim. 2(1), 47–58 (2019)
Mansoor, M., et al.: Novel Grass Hopper optimization based MPPT of PV systems for complex partial shading conditions. Sol. Energy 198, 499–518 (2020)
Bataineh, K.: Improved hybrid algorithms-based MPPT algorithm for PV system operating under severe weather conditions. In: IET Power Electronics. (2018)
Yin, X., Zhansi, J., Li, P.: Recurrent neural network based adaptive integral sliding mode power maximization control for wind power systems. Renew. Energy 145, 1149–1157 (2020)
Tobón, A., et al.: MPPT of a photovoltaic panels array with partial shading using the IPSM with implementation both in simulation as in hardware. Energies. 13(4), 815 (2020)
Eltamaly, A.M., Hassan, M.F.: Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC. Sol. Energy 177, 306–316 (2019)
Eltamaly, A.M., et al.: A novel bat algorithm strategy for maximum power point tracker of photovoltaic energy systems under dynamic partial shading. IEEE Access. 8, 10048–10060 (2020)
Abdalla, O., et al.: Wind driven optimization algorithm based global MPPT for PV system under non-uniform solar irradiance. Sol. Energy 180, 429–444 (2019)
Fathy, A., Hegazy, R., Dalia, Y.: A robust global MPPT to mitigate partial shading of triple-junction solar cell-based system using manta ray foraging optimization algorithm. Sol. Energy 207, 305–316 (2020)
Kumar, N., et al.: Maximum power peak detection of partially shaded PV panel by using intelligent monkey king evolution algorithm. IEEE Trans. Ind. Appl. 53(6), 5734–5743 (2017)
Chawla, R., et al.: Design and analysis of multi junction solar photovoltaic cell with graphene as an intermediate layer. J Nano Sci. Nanotechnol. 20(6), 3693–3702 (2020)
Yousri, D., et al.: Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models’ parameters. Energy. 195, 116979 (2020)
Messaoud, R.B.: Extraction of uncertain parameters of single and double diode model of a photovoltaic panel using Salp Swarm algorithm. Measurement 154, 107446 (2020)
Winston, D.P., et al.: Performance improvement of solar PV array topologies during various partial shading conditions. Sol. Energy 196, 228–242 (2020)
Jotham, J., et al.: Non-isolated conventional DC-DC converter comparison for a photovoltaic system: a review. J. Renew. Sustain. Energy. 12(1), 013502 (2020)
Raghavendra, K., Venkat, G., et al.: A comprehensive review of DC–DC converter topologies and modulation strategies with recent advances in solar photovoltaic systems. Electronics. 9(1), 31 (2020)
Chandra, S., Prerna, G., Diwaker, P.: Radial basis function neural network based maximum power point tracking for photovoltaic brushless DC motor connected water pumping system. Comput. Electr. Eng. 86, 106730 (2020)
Boukenoui, R, et al. Comparative analysis of P&O, modified hill climbing-FLC, and adaptive P&O-FLC MPPTs for microgrid standalone PV system. In: 2015 IEEE international conference on renewable energy research and applications (2015). https://doi.org/https://doi.org/10.1109/ICRERA.2015.7418579
Naci, G.E.N.C., Dilovan, H.: Dynamic Behavior Analysis of ANFIS Based MPPT Controller for Standalone Photovoltaic Systems. Int. J. Renew. Energy Res. (IJRER). 10(1), 101–108 (2020)
Manickam, C., et al.: A hybrid algorithm for tracking of GMPP based on P&O and PSO with reduced power oscillation in string inverters. IEEE Trans. Industr. Electron. 63(10), 6097–6106 (2016)
Inci, M., Abdullah, C.: Performance enhancement of energy extraction capability for fuel cell implementations with improved Cuckoo search algorithm. Int. J. Hydrogen Energy 3, q12 (2020)
Acknowledgement
The authors would like to thank the University Grants Commission, Govt. of India (F1-17.1/2017-18/MANF-2017-18-AND-76098 / (SA-III/Website)) for funding our research program and they especially thank VIT University management for providing all the facilities to carry out our research work.
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Hussaian Basha, C.H., Rani, C. Performance Analysis of MPPT Techniques for Dynamic Irradiation Condition of Solar PV. Int. J. Fuzzy Syst. 22, 2577–2598 (2020). https://doi.org/10.1007/s40815-020-00974-y
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DOI: https://doi.org/10.1007/s40815-020-00974-y