Systematic photovoltaic system power losses calculation and modeling using computational intelligence techniques

Applied Energy - Tập 284 - Trang 116396 - 2021
Behzad Hashemi1, Shamsodin Taheri1, Ana-Maria Cretu1, Edris Pouresmaeil2
1Department of Computer Science and Engineering, Université du Québec en Outaouais, Gatineau, QC, Canada
2Department of Electrical Engineering and Automation, Aalto University, 00076 Aalto, Espoo, Finland

Tài liệu tham khảo

Adaramola, 2015, Preliminary assessment of a small-scale rooftop PV-grid tied in Norwegian climatic conditions, Energy Convers Manag, 90, 458, 10.1016/j.enconman.2014.11.028 Ingenhoven, 2019, Analysis of photovoltaic performance loss rates of six module types in five geographical locations, IEEE J Photovolt, 9, 1091, 10.1109/JPHOTOV.2019.2913342 Burduhos, 2018, Analysis of the conversion efficiency of five types of photovoltaic modules during high relative humidity time periods, IEEE J Photovolt, 8, 1716, 10.1109/JPHOTOV.2018.2861720 Ayompe, 2011, Measured performance of a 1.72kW rooftop grid connected photovoltaic system in Ireland, Energy Convers Manag, 52, 816, 10.1016/j.enconman.2010.08.007 Daher, 2018, Impact of tropical desert maritime climate on the performance of a PV grid-connected power plant, Renew Energy, 125, 729, 10.1016/j.renene.2018.03.013 Sharma, 2017, Performance analysis of a 11.2 kWp roof top grid-connected PV system in Eastern India, Energy Rep, 3, 76, 10.1016/j.egyr.2017.05.001 Pietruszko, 2003, Performance of a grid connected small PV system in Poland, Appl Energy, 74, 177, 10.1016/S0306-2619(02)00144-7 Ma, 2017, Long term performance analysis of a standalone photovoltaic system under real conditions, Appl Energy, 201, 320, 10.1016/j.apenergy.2016.08.126 Okello, 2015, Analysis of measured and simulated performance data of a 3.2kWp grid-connected PV system in Port Elizabeth, South Africa, Energy Convers Manag, 100, 10, 10.1016/j.enconman.2015.04.064 Kumar, 2019, Performance, energy loss, and degradation prediction of roof-integrated crystalline solar PV system installed in Northern India, Case Stud Therm Eng, 13, March, 10.1016/j.csite.2019.100409 Quesada, 2011, Experimental results and simulation with TRNSYS of a 7.2kWp grid-connected photovoltaic system, Appl Energy, 88, 1772, 10.1016/j.apenergy.2010.12.011 Malvoni, 2017, Long term performance, losses and efficiency analysis of a 960kWP photovoltaic system in the Mediterranean climate, Energy Convers Manag, 145, 169, 10.1016/j.enconman.2017.04.075 Goel, 2020, Analysis of measured and simulated performance of a grid-connected PV system in eastern India, Environ Dev Sustain, 1 Zapata, 2015, Design of a cleaning program for a PV plant based on analysis of energy losses, IEEE J Photovolt, 5, 1748, 10.1109/JPHOTOV.2015.2478069 Coello, 2019, Simple model for predicting time series soiling of photovoltaic panels, IEEE J Photovolt, 9, 1382, 10.1109/JPHOTOV.2019.2919628 Liu, 2019, A method of calculating the daily output power reduction of PV modules due to dust deposition on its surface, IEEE J Photovolt, 9, 881, 10.1109/JPHOTOV.2019.2903086 Deceglie, 2018, Quantifying soiling loss directly from PV yield, IEEE J Photovolt, 8, 547, 10.1109/JPHOTOV.2017.2784682 Malamaki, 2014, Analytical calculation of the electrical energy losses on fixed-mounted PV plants, IEEE Trans Sustain Energy, 5, 1080, 10.1109/TSTE.2014.2323694 Oozeki, 2003, An evaluation method of PV systems, Sol Energy Mater Sol Cells, 75, 687, 10.1016/S0927-0248(02)00143-5 Ueda, 2009, Performance analysis of various system configurations on grid-connected residential PV systems, Sol Energy Mater Sol Cells, 93, 945, 10.1016/j.solmat.2008.11.021 Micheli, 2017, An investigation of the key parameters for predicting PV soiling losses, Prog Photovolt: Res Appl, 25, 291, 10.1002/pip.2860 Marion, 2013, Measured and modeled photovoltaic system energy losses from snow for Colorado and Wisconsin locations, Sol Energy, 97, 112, 10.1016/j.solener.2013.07.029 Abenante, 2020, Non-linear continuous analytical model for performance degradation of photovoltaic module arrays as a function of exposure time, Appl Energy, 275, 10.1016/j.apenergy.2020.115363 Meyers B, Mikofski M, Anderson M. A fast parameterized model for predicting PV system performance under partial shade conditions. In: IEEE 43rd Photovolt Specialists Conf (PVSC), Portland, OR; 2016. p. 3173–8. Javed, 2017, Modeling of photovoltaic soiling loss as a function of environmental variables, Sol Energy, 157, 397, 10.1016/j.solener.2017.08.046 Pulipaka, 2016, Modeling of soiled PV module with neural networks and regression using particle size composition, Sol Energy, 123, 116, 10.1016/j.solener.2015.11.012 Massi Pavan, 2013, A comparison between BNN and regression polynomial methods for the evaluation of the effect of soiling in large scale photovoltaic plants, Appl Energy, 108, 392, 10.1016/j.apenergy.2013.03.023 Hashemi, 2020, Snow loss prediction for photovoltaic farms using computational intelligence techniques, IEEE J Photovolt, 10, 1044, 10.1109/JPHOTOV.2020.2987158 “Photovoltaic data acquisition,” [online] Available: http://maps.nrel.gov/pvdaq/; [accessed 21 Feb. 2020]. U.S. Local Climatological Data, “National Ocean and Atmospheric Administration (NOAA),” [online] Available: http://www.ncdc.noaa.gov/; [accessed 3 March 2020]. Ye, 2013 Hosseini, 2018, Determination of photovoltaic characteristics in real field conditions, IEEE J Photovolt, 8, 572, 10.1109/JPHOTOV.2018.2797974 “Shell PowerMax Eclipse 80-c,” [online] Available: https://www.shell.com; [accessed 25 Feb. 2020]. Khenar, 2019, Particle swarm optimisation-based model and analysis of photovoltaic module characteristics in snowy conditions, IET Renewable Power Gener, 13, 1950, 10.1049/iet-rpg.2018.5840 Reinders, 1999, Technical and economic analysis of grid-connected PV systems by means of simulation, Prog Photovolt: Res Appl, 7, 71, 10.1002/(SICI)1099-159X(199901/02)7:1<71::AID-PIP248>3.0.CO;2-X Baltus CWA, Eikelboom JA, Van Zolingen RJC. Analytical monitoring of losses in PV systems. In: 14th European Photovolt Sol Energy Conf, Barcelona, Spain; 1997. p. 1-5. Wang, 2019, A review of deep learning for renewable energy forecasting, Energy Convers Manag, 198, Oct, 10.1016/j.enconman.2019.111799 Boehmke, 2019 Hochreiter, 1997, Long short-term memory, Neural Comput, 9, 1735, 10.1162/neco.1997.9.8.1735