An ANN based switching network for optimally selected photovoltaic array with battery and supercapacitor to mitigate the effect of intermittent solar irradiance

References

• Ahmed, J., and Z. Salam. July 1 2015. A critical evaluation on maximum power point tracking methods for partial shading in PV systems. Renewable and Sustainable Energy Reviews 47:933–53. doi:10.1016/j.rser.2015.03.080.
   Web of Science ®Google Scholar
• Ajmal, A. M., T. S. Babu, V. K. Ramachandaramurthy, D. Yousri, and J. B. Ekanayake. August 1 2020. Static and dynamic reconfiguration approaches for mitigation of partial shading influence in photovoltaic arrays. Sustainable Energy Technologies and Assessments 40:100738. doi:10.1016/j.seta.2020.100738.
   Web of Science ®Google Scholar
• Aliyu, A., N. Tekbiyik-Ersoy, and A. Novel. January 2019. Framework for cost optimization of renewable energy installations: A case study of Nigeria. Energies 12 (22):4311. doi: 10.3390/en12224311.
   Web of Science ®Google Scholar
• Almashakbeh, A. S., A. A. Arfoa, and E. S. Hrayshat. 2019. Techno-economic evaluation of an off-grid hybrid PV-wind-diesel-battery system with various scenarios of system’s renewable energy fraction. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 1–24. doi:10.1080/15567036.2019.1673515.
   Web of Science ®Google Scholar
• Aygül, K., M. Cikan, T. Demirdelen, and M. Tumay October 2019. Butterfly optimization algorithm based maximum power point tracking of photovoltaic systems under partial shading condition. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 14:1–9. doi: 10.1080/15567036.2019.1677818.
   Google Scholar
• Babu, T. S., J. P. Ram, T. Dragičević, M. Miyatake, F. Blaabjerg, and N. Rajasekar. 2017 June 13. Particle swarm optimization based solar PV array reconfiguration of the maximum power extraction under partial shading conditions. IEEE Transactions on Sustainable Energy 9(1):74–85.
   Web of Science ®Google Scholar
• Babu, T. S., J. P. Ram, T. Dragičević, M. Miyatake, F. Blaabjerg, and N. Rajasekar. January 2018. Particle swarm optimization based solar PV array reconfiguration of the maximum power extraction under partial shading conditions. IEEE Transactions on Sustainable Energy 9 (1):74–85. doi: 10.1109/TSTE.2017.2714905.
   Web of Science ®Google Scholar
• Behabtu, H. A., M. Messagie, T. Coosemans, M. Berecibar, K. Anlay Fante, A. A. Kebede, and J. V. Mierlo. 2020. A review of energy storage technologies’ application potentials in renewable energy sources grid integration. Sustainability 12 (24):10511. doi:10.3390/su122410511.
   Web of Science ®Google Scholar
• Chang, G. W., and H. J. Lu. 2018. Integrating gray data preprocessor and deep belief network for day-ahead pv power output forecast. IEEE Transactions on Sustainable Energy 11 (1):185–94. doi:10.1109/TSTE.2018.2888548.
   Web of Science ®Google Scholar
• Deboucha, H., I. Shams, S. L. Belaid, and S. Mekhilef. April8 2021. A fast GMPPT scheme based on collaborative swarm algorithm for partially shaded photovoltaic system. IEEE Journal of Emerging and Selected Topics in Power Electronics 9 (5):5571–80. doi: 10.1109/JESTPE.2021.3071732
   Web of Science ®Google Scholar
• Dhanalakshmi, B., and N. Rajasekar. 2018. Dominance square based array reconfiguration scheme for power loss reduction in solar photovoltaic (pv) systems. Energy Conversion and Management 156:84–102. doi:10.1016/j.enconman.2017.10.080.
   Web of Science ®Google Scholar
• Dheeban, S. S., Muthu Selvan, N. B., Subramaniam, U. 2021. Artificial neural network based solar energy integrated unified power quality conditioner. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 43 (1): 1–25.
   Google Scholar
• Gao, L., R. A. Dougal, S. Liu, and A. Jotova. 2007. Portable solar systems using a step-up power converter with a fast-speed MPPT and a parallel-configured solar panel to address rapidly changing illumination. In APEC 07-Twenty-Second Annual IEEE Applied Power Electronics Conference and Exposition. Anaheim, CA, USA, (pp. 520–23). IEEE, March
   Google Scholar
• Ghofrani, M., and N. N. Hosseini. 2016. Optimizing hybrid renewable energy systems: A review, sustainable energy-technological issues, applications and case studies. 161–76.
   Google Scholar
• Guo, F., B. Xu, W. A. Zhang, C. Wen, D. Zhang, and L. Yu. February 10 2021. Training deep neural network for optimal power allocation in Islanded microgrid systems: A distributed learning-based approach. IEEE Transactions on Neural Networks and Learning Systems 33 (5): 2057–2069 .
   Web of Science ®Google Scholar
• Han, S., S. Han, and H. Aki. 2014. A practical battery wear model for electric vehicle charging applications. Applied Energy 113:1100–08. doi:10.1016/j.apenergy.2013.08.062.
   Web of Science ®Google Scholar
• Han, Y., K. Zhang, H. Li, E. A. A. Coelho, and J. M. Guerrero. 2017. Mas-based distributed coordinated control and optimization in microgrid and microgrid clusters: A comprehensive overview. IEEE Transactions on Power Electronics 33 (8):6488–508. doi:10.1109/TPEL.2017.2761438.
   Web of Science ®Google Scholar
• Jedou, E., M. Ndongo, M. M. Ali, K. Yetilmezsoy, B. Bilal, C. C. Ebeya, C. M. Kébé, P. A. Ndiaye, E. Kıyan, and M. Bahramian. October 1 2021. A cartographic approach coupled with optimized sizing and management of an on-grid hybrid PV-solar-battery-group based on the state of the sky: An African case study. Solar Energy 227:101–15. doi:10.1016/j.solener.2021.08.079.
   Web of Science ®Google Scholar
• Jha, V., and U. S. Triar. 2019. A detailed comparative analysis of different photovoltaic array configurations under partial shading conditions. Int Trans Electr Energ Syst 29 (6):e12020. doi:10.1002/2050-7038.12020.
   Web of Science ®Google Scholar
• Kumar, K. P., and B. Saravanan. 2017. Recent techniques to model uncertainties in power generation from renewable energy sources and loads in microgrids–a review. Renewable and Sustainable Energy Reviews 71:348–58. doi:10.1016/j.rser.2016.12.063.
   Web of Science ®Google Scholar
• Makhloufi, S., and S. Mekhilef. April 14 2021. Logarithmic PSO based global/local maximum power point tracker for partially shaded photovoltaic systems. IEEE Journal of Emerging and Selected Topics in Power Electronics 10 (1): 375–386.
   Web of Science ®Google Scholar
• Malathy, S., and R. Ramaprabha. 2018. Reconfiguration strategies to extract maximum power from photovoltaic array under partially shaded conditions. Renewable and Sustainable Energy Reviews 81:2922–34. doi:10.1016/j.rser.2017.06.100.
   Web of Science ®Google Scholar
• Martínez-Moreno, F., J. Muñoz, and E. Lorenzo. 2010. Experimental model to estimate shading losses on pv arrays. Solar Energy Materials and Solar Cells 94 (12):2298–303. doi:10.1016/j.solmat.2010.07.029.
   Web of Science ®Google Scholar
• McCandless, T. C., S. E. Haupt, and G. S. Young. April 1 2016. A regime-dependent artificial neural network technique for short-range solar irradiance forecasting. Renewable Energy 89:351–59. doi:10.1016/j.renene.2015.12.030.
   Web of Science ®Google Scholar
• Moumouni, Y., and R. J. 2015. Baker RJ Buffer sizing of concentrated photovoltaic batteries:An economic analysis. in: 2015. IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS) IEEE. Fort Collins, CO, USA, pp. 1–4.
   Google Scholar
• Moutis, P., A. Vassilakis, A. Sampani, and N. D. Hatziargyriou. July24 2015. DC switch driven active power output control of photovoltaic inverters for the provision of frequency regulation. IEEE Transactions on Sustainable Energy 6 (4):1485–93. doi: 10.1109/TSTE.2015.2450539
   Web of Science ®Google Scholar
• Niazi, K. A. K., Y. Yang, M. Nasir, and D. Sera. 2019. Evaluation of interconnection configuration schemes for pv modules with switched-inductor converters under partial shading conditions. Energies 12 (14):2802. doi:10.3390/en12142802.
   Web of Science ®Google Scholar
• O’Shaughnessy, E., D. Cutler, K. Ardani, and R. Margolis. 2018. Solar plus: Optimization of distributed solar pv through battery storage and dispatchable load in residential buildings. Applied Energy 213:11–21. doi:10.1016/j.apenergy.2017.12.118.
   Web of Science ®Google Scholar
• Patel, H., and V. Agarwal. 2008 February 15. MATLAB-based modeling to study the effects of partial shading on PV array characteristics. IEEE Transactions on Energy Conversion 23 (1):302–10. doi:10.1109/TEC.2007.914308.
   Web of Science ®Google Scholar
• Pervez, I., I. Shams, S. Mekhilef, A. Sarwar, M. Tariq, and B. Alamri. March29 2021. Most valuable player algorithm based maximum power point tracking for a partially shaded PV generation system. IEEE Transactions on Sustainable Energy 12 (4):1876–90. doi: 10.1109/TSTE.2021.3069262
   Web of Science ®Google Scholar
• Potnuru, S. R., D. Pattabiraman, S. I. Ganesan, and N. Chilakapati. 2015 June 1. Positioning of PV panels for reduction in line losses and mismatch losses in PV array. Renewable Energy. 78:264–75. 10.1016/j.renene.2014.12.055
   Web of Science ®Google Scholar
• Rani, B. I., G. S. Ilango, and C. Nagamani. January29 2013. Enhanced power generation from PV array under partial shading conditions by shade dispersion using Su Do Ku configuration. IEEE Transactions on Sustainable Energy 4 (3):594–601. doi: 10.1109/TSTE.2012.2230033
   Web of Science ®Google Scholar
• Rosiek, S., J. Alonso-Montesinos, and F. J. Batlles. 2018. Online 3-h forecasting of the power output from a BIPV system using satellite observations and ANN. International Journal of Electrical Power & Energy Systems 99:261–72. doi:10.1016/j.ijepes.2018.01.025.
   Web of Science ®Google Scholar
• Samsatli, S., and N. J. Samsatli. 2018. A general mixed integer linear programming model for the design and operation of integrated urban energy systems. Journal of Cleaner Production 191:458–79. doi:10.1016/j.jclepro.2018.04.198.
   Web of Science ®Google Scholar
• Satpathy, P. R., R. Sharma, and S. Jena. 2017. A shade dispersion interconnection scheme for partially shaded modules in a solar pv array network. Energy 139:350–65. doi:10.1016/j.energy.2017.07.161.
   Web of Science ®Google Scholar
• Schaltz, E., A. Khaligh, and P. O. Rasmussen. 2009. Influence of battery/ultracapacitor energy-storage sizing on battery lifetime in a fuel cell hybrid electric vehicle. IEEE Transactions on Vehicular Technology 58 (8):3882–91. doi:10.1109/TVT.2009.2027909.
   Web of Science ®Google Scholar
• Shams, I., S. Mekhilef, and K. S. Tey. October8 2020. Maximum power point tracking using modified butterfly optimization algorithm for partial shading, uniform shading, and fast varying load conditions. IEEE Transactions on Power Electronics 36 (5):5569–81. doi: 10.1109/TPEL.2020.3029607
   Web of Science ®Google Scholar
• Shidhani, T. A., A. Ioannou, and G. Falcone. 2020. Multi-objective optimisation for power system planning integrating sustainability indicators. Energies 13 (9):2199. doi:10.3390/en13092199.
   Web of Science ®Google Scholar
• Subramani, G., V. K. Ramachandaramurthy, P. Sanjeevikumar, J. B. Holm-Nielsen, F. Blaabjerg, L. Zbigniew, and P. Kostyla. 2019. Techno-economic optimization of grid-connected photovoltaic (pv) and battery systems based on maximum demand reduction (mdred) modelling in Malaysia. Energies 12 (18):3531. doi:10.3390/en12183531.
   Web of Science ®Google Scholar
• Suresh Kumar, L. V., and G. V. Nagesh Kumar. 2017. Power conversion in renewable energy systems: A review advances in wind and PV system. International Journal of Energy Research 41 (2):182–97. doi:10.1002/er.3601.
   Web of Science ®Google Scholar
• Tahmasbi-Fard, M., M. Tarafdar-Hagh, S. Pourpayam, and A. A. Haghrah. 2018. A voltage equalizer circuit to reduce partial shading effect in photovoltaic string. IEEE Journal of Photovoltaics 8 (4):1102–09. doi:10.1109/JPHOTOV.2018.2823984.
   Web of Science ®Google Scholar
• Venkadesan, A., S. Himavathi, K. Sedhuraman, and A. Muthuramalingam. 2017. Design and FPGA implementation of cascade neural network based flux estimator for speed estimation in IM drives. Neural Comput. Appl 11 (1):1–32.
   Google Scholar
• Wang, Y. J., and R. L. Sheu. 2015. Probabilistic modeling of partial shading of photovoltaic arrays. International Journal of Photoenergy 2015:1–10. doi:10.1155/2015/863637.
   Web of Science ®Google Scholar
• Wang, H., M. Muñoz-Garca, G. Moreda, and M. Alonso-Garca. 2018. Optimum inverter sizing of grid-connected photovoltaic systems based on energetic and economic considerations. Renewable Energy 118:709–17. doi:10.1016/j.renene.2017.11.063.
   Web of Science ®Google Scholar
• Winston, D. P., S. Kumaravel, B. P. Kumar, and S. Devakirubakaranand . 2020. Performance improvement of solar PV array topologies during various partial shading conditions. Solar Energy 196:228–42. doi:10.1016/j.solener.2019.12.007.
   Web of Science ®Google Scholar
• Yang, H., Z. Gong, Y. Ma, L. Wang, and B. Dong. 2020. Optimal two-stage dispatch method of household PV-BESS integrated generation system under time-of-use electricity price. International Journal of Electrical Power & Energy Systems 123:106244. pp.0142-0615. doi:10.1016/j.ijepes.2020.106244.
   Web of Science ®Google Scholar
• Yuhao, N., A. S. Zamzam, and A. Brandt. 2021. Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks. Solar Energy 224:341–54. doi:10.1016/j.solener.2021.05.095.
   Web of Science ®Google Scholar
• Zador, A. M. 2019. A critique of pure learning and what artificial neural networks can learn from animal brains. Nature Communications 10 (1):1–7. doi:10.1038/s41467-019-11786-6.
   PubMedGoogle Scholar
• Zeynali, S., N. Rostami, A. Ahmadian, and A. Elkamel. June 1 2020. Two-stage stochastic home energy management strategy considering electric vehicle and battery energy storage system: An ANN-based scenario generation methodology. Sustainable Energy Technologies and Assessments 39:100722. doi:10.1016/j.seta.2020.100722.
   Web of Science ®Google Scholar
• Zhang, W., A. Maleki, M. A. Rosen, and J. Liu. 2018. Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage. Energy 163:191–207. doi:10.1016/j.energy.2018.08.112.
   Web of Science ®Google Scholar
• Zhou, C., K. Qian, M. Allan, and W. Zhou. 2011. Modeling of the cost of ev battery wear due to v2g application in power systems. IEEE Transactions on Energy Conversion 26 (4):1041–50. doi:10.1109/TEC.2011.2159977.
   Web of Science ®Google Scholar