Artificial neural networks based harmonics estimation for real university microgrids using hourly solar irradiation and temperature data
The need for renewable energy is increasing day by day due to different factors such as increasing energy demand, environmental considerations as well as the will to decrease the share of fossil fuel-based generation. Due to their relative low-cost and ease of installation, PV systems are leading th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Energy Nexus |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772427123000025 |
| _version_ | 1797867417104285696 |
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| author | Nurcan Yarar Mustafa Yagci Serkan Bahceci Ahmet Onen Taha Selim Ustun |
| author_facet | Nurcan Yarar Mustafa Yagci Serkan Bahceci Ahmet Onen Taha Selim Ustun |
| author_sort | Nurcan Yarar |
| collection | DOAJ |
| description | The need for renewable energy is increasing day by day due to different factors such as increasing energy demand, environmental considerations as well as the will to decrease the share of fossil fuel-based generation. Due to their relative low-cost and ease of installation, PV systems are leading the way for renewable energy deployments around the globe. However, there are meticulous studies that need to be undertaken for realization of such projects. Studying local weather and load patterns for proper panel sizing or considering grid components to determine cable and transformer sizing can be named as some examples for pre-installation studies. In addition to these, post-installation impact studies, e.g. accurate harmonic analysis contribution, is more important to ensure safe and secure operation of the overall system. These steps need to be taken for all PV installation projects. The aim of this study is to show a step-by-step analysis of the effect of a real PV system on the network and to improve the prediction and give a new perspective to the harmonic estimation by using the hourly temperature and radiation data together. At the first phase of the study, a detail real-time 250 kW PV system was modeled for real university campus, and then harmonic estimation based on hourly solar irradiation and hourly temperature was performed with artificial neural networks (ANN) and nonlinear autoregressive exogenous (NARX). The accuracy of the prediction made with ANN was 0.98, and the accuracy of the prediction made with NARX was 0.96.Researchers in PV sizing and control field as well as engineers in power quality area would find these findings beneficial and useful. Use of ANNs and NARX for such analysis indicates the trend in this field that can be targeted by new research projects. |
| first_indexed | 2024-04-09T23:39:51Z |
| format | Article |
| id | doaj.art-d40d1a3b0b424c7ba2301d4b523240bd |
| institution | Directory Open Access Journal |
| issn | 2772-4271 |
| language | English |
| last_indexed | 2024-04-09T23:39:51Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Nexus |
| spelling | doaj.art-d40d1a3b0b424c7ba2301d4b523240bd2023-03-19T04:38:53ZengElsevierEnergy Nexus2772-42712023-03-019100172Artificial neural networks based harmonics estimation for real university microgrids using hourly solar irradiation and temperature dataNurcan Yarar0Mustafa Yagci1Serkan Bahceci2Ahmet Onen3Taha Selim Ustun4Electrical and Electronics Engineering, Erciyes University, Kayseri, Turkey; Electrical and Electronics Engineering, Necmettin Erbakan University, Konya, TurkeyElectrical and Electronics Engineering, Necmettin Erbakan University, Konya, TurkeyElectrical and Electronics Engineering, Erciyes University, Kayseri, TurkeyElectrical and Electronics Engineering, Abdullah Gul University, Kayseri, Turkey; Electrical and Computer Engineering, Sultan Qaboos University, Muscat, Oman; Corresponding author.Fukushima Renewable Energy Institute, AIST, JapanThe need for renewable energy is increasing day by day due to different factors such as increasing energy demand, environmental considerations as well as the will to decrease the share of fossil fuel-based generation. Due to their relative low-cost and ease of installation, PV systems are leading the way for renewable energy deployments around the globe. However, there are meticulous studies that need to be undertaken for realization of such projects. Studying local weather and load patterns for proper panel sizing or considering grid components to determine cable and transformer sizing can be named as some examples for pre-installation studies. In addition to these, post-installation impact studies, e.g. accurate harmonic analysis contribution, is more important to ensure safe and secure operation of the overall system. These steps need to be taken for all PV installation projects. The aim of this study is to show a step-by-step analysis of the effect of a real PV system on the network and to improve the prediction and give a new perspective to the harmonic estimation by using the hourly temperature and radiation data together. At the first phase of the study, a detail real-time 250 kW PV system was modeled for real university campus, and then harmonic estimation based on hourly solar irradiation and hourly temperature was performed with artificial neural networks (ANN) and nonlinear autoregressive exogenous (NARX). The accuracy of the prediction made with ANN was 0.98, and the accuracy of the prediction made with NARX was 0.96.Researchers in PV sizing and control field as well as engineers in power quality area would find these findings beneficial and useful. Use of ANNs and NARX for such analysis indicates the trend in this field that can be targeted by new research projects.http://www.sciencedirect.com/science/article/pii/S2772427123000025Power qualityHarmonic estimationCampus PV SystemsMicrogrid |
| spellingShingle | Nurcan Yarar Mustafa Yagci Serkan Bahceci Ahmet Onen Taha Selim Ustun Artificial neural networks based harmonics estimation for real university microgrids using hourly solar irradiation and temperature data Energy Nexus Power quality Harmonic estimation Campus PV Systems Microgrid |
| title | Artificial neural networks based harmonics estimation for real university microgrids using hourly solar irradiation and temperature data |
| title_full | Artificial neural networks based harmonics estimation for real university microgrids using hourly solar irradiation and temperature data |
| title_fullStr | Artificial neural networks based harmonics estimation for real university microgrids using hourly solar irradiation and temperature data |
| title_full_unstemmed | Artificial neural networks based harmonics estimation for real university microgrids using hourly solar irradiation and temperature data |
| title_short | Artificial neural networks based harmonics estimation for real university microgrids using hourly solar irradiation and temperature data |
| title_sort | artificial neural networks based harmonics estimation for real university microgrids using hourly solar irradiation and temperature data |
| topic | Power quality Harmonic estimation Campus PV Systems Microgrid |
| url | http://www.sciencedirect.com/science/article/pii/S2772427123000025 |
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