Revolutionizing Solar Energy: The Impact of Artificial Intelligence on Photovoltaic Systems
DOI:
https://doi.org/10.47709/ijmdsa.v2i1.2599Keywords:
Artificial intelligence, solar energy, renewable energy, solar panel technology, machine learning, grid integration, grid stability, demand response, intelligent forecasting, energy management systems, predictive maintenance, solar farms, system performance, sustainability.Abstract
References
Liu, Yan, Liu, Yang, Liu, D., Cao, T., Han, S., Xu, G., 2001. Design of CO2 hydrogenation catalyst by an artificial neural network. Comput. Chem. Eng. 25, 1711–1714. https://doi.org/10.1016/S0098-1354(01)00714-1
Luna-Rubio, R., Trejo-Perea, M., Vargas-Vázquez, D., Ríos-Moreno, G.J., 2012. Optimal sizing of renewable hybrids energy systems: A review of methodologies. Sol. Energy 86, 1077–1088. https://doi.org/10.1016/j.solener.2011.10.016
Ma, J., Bi, Z., Ting, T.O., Hao, S., Hao, W., 2016. Comparative performance on photovoltaic model parameter identification via bio-inspired algorithms. Sol. Energy 132, 606–616. https://doi.org/10.1016/j.solener.2016.03.033
Ma, K., Liu, X., Li, G., Hu, S., Yang, J., Guan, X., 2019. Resource allocation for smart grid communication based on a multiswarm artificial bee colony algorithm with cooperative learning. Eng. Appl. Artif. Intell. 81, 29–36. https://doi.org/10.1016/j.engappai.2018.12.002
Macedo, M.N.Q., Galo, J.J.M., De Almeida, L.A.L., De, A.C., 2015. Demand side management using artificial neural networks in a smart grid environment. Renew. Sustain. Energy Rev. 41, 128–133. https://doi.org/10.1016/j.rser.2014.08.035
Maleki, A., Askarzadeh, A., 2014. Artificial bee swarm optimization for optimum sizing of a stand-alone PV/WT/FC hybrid system considering LPSP concept. Sol. Energy 107, 227–235. https://doi.org/10.1016/j.solener.2014.05.016
Marra, D., Sorrentino, M., Pianese, C., Iwanschitz, B., 2013. A neural network estimator of Solid Oxide Fuel Cell performance for on-field diagnostics and prognostics applications. J. Power Sources 241, 320–329. https://doi.org/10.1016/j.jpowsour.2013.04.114
Marzband, M., Azarinejadian, F., Savaghebi, M., Pouresmaeil, E., Guerrero, J.M., Lightbody, G., 2018. Smart transactive energy framework in grid-connected multiple home microgrids under independent and coalition operations. Renew. Energy 126, 95–106. https://doi.org/10.1016/j.renene.2018.03.021
Mearian, L., 2017. Data storage goes from $1M to 2 cents per gigabyte. Computerworld 2. https://doi.org/https://www.computerworld.com/article/3182207/data-storage/cw50-data-storage-goes-from-1m-to-2- cents-per-gigabyte.html
O. Erdinc, Optimization in Renewable Energy Systems, Butterworth-Heinemann, 2017.
H. Sharma, A. Haque and Z. A. Jaffery, "Modeling and Optimisation of a Solar Energy Harvesting System for Wireless Sensor Network Nodes," Journal of Sensor and Actuator Network, vol. 7, no. 3, p. 40, 2018.
A. Dhoke, R. Sharma and T. K. Saha, "An approach for fault detection and location in solar PV systems," Solar Energy, vol. 194, pp. 197-208, 2019.
A. Appiah, X. Zhang, B. Ayawli and F. Kyeremeh, "Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques," International Journal of Photoenergy, pp. 1-19, 2019.
Syafaruddin and D. S. Zinge, "Review on Methods of Fault Diagnosis in Photovoltaic System Applications," Journal of Engineering Science and Technology Review, pp. 53-66, 2019.
F. A. O. Polo, J. F. Bermejo, J. F. G. Fernández and A. C. Márquez, "Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models," Renewable Energy, pp. 81: 227-238, 2015.
E. Garoudja, A. Chouder, K. Kara and S. Silvestre, "An enhanced machine learning based approach for failures detection and diagnosis of PV systems," Energy Conversion and Management, pp. 151: 496-513, 2017.
H. Zhu, L. Lu, J. Yao, S. Dai and Y. Hu, "Fault diagnosis approach for photovoltaic arrays based on unsupervised sample clustering and probabilistic neural network model," Solar Energy, pp. 176: 395-405, 2018.
A. M. Pavan, A. Mellit, D. D. Pieri and S. Kalogirou, "A comparison between BNN and regression polynomial methods for the evaluation of the effect of soiling in large scale photovoltaic plants," Applied Energy, pp. 108: 392-401, 2013.
Z.-C. Chen, Y. Chen, L. Wu, S. Cheng and P. Lin, "Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions," Energy Conversion and Management, p. 198: 111793, 2019.
P. Ducange, M. Fazzolari, B. Lazzerini and F. Marcelloni, "An intelligent system for detecting faults in photovoltaic fields," 11th International Conference on Intelligent Systems Design and Applications, Cordoba, pp. 1341-1346, 2011.
S. Liu, L. Dong, X. Liao, X. Cao and X. Wang, "Photovoltaic array fault diagnosis based on Gaussian Kernel Fuzzy C-means clustering algorithm," Sensors, pp. 19, 1520, 2019.
B. Kang, S. Kim, S. Bae and J. Park, "Diagnosis of output power lowering in a PV array by using the Kalman-filter algorithm," IEEE Transactions on Energy Conversion, pp. 27(4): 885-894, 2012.
M. K. Alam, F. Khan, J. Johnson and J. Flicker, "A comprehensive review of catastrophic faults in PV arrays: Types, detection, and mitigation techniques," IEEE Journal of Photovoltaics, pp. 5(3): 982-997, 2015.
Y. Zhao, Fault analysis in solar photovoltaic arrays, Boston, MA: The Department of electrical and computer Engineering. Northeastern University,, 2010. 61
A. Y. Appiah, X. Zhang, B. Ayawli, B. Kwame and K. Frimpong, "Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques," International Journal of Photoenergy, 2019.
L. Chen, S. Li and X. Wang, "Quickest fault detection in photovoltaic systems," IEEE Transactions on Smart Grid, p. 9(3): 1835–1847, 2016.
K.-H. Chao, P.-Y. Chen, M.-H. Wang and C.-T. Chen, "An intelligent fault detection method of a photovoltaic module array using wireless sensor networks," International Journal of Distributed Sensor Networks, pp. 10(5): 1-12, 2014.
F. Reil, A. Sepanski, W. Herrmann, J. Althaus, W. Vaaßen and H. Schmidt, "Qualification of arcing risks in PV modules," 38th IEEE Photovoltaic Specialists Conference, p. 727–730, 2012.
S. Lu, B. Phung and Daming Zhang, "A comprehensive review on DC arc faults and their diagnosis methods in photovoltaic systems," Renewable and Sustainable Energy Reviews, pp. 89, 88-98, 2018.
J. Flicker and J. Johnson, "Electrical simulations of series and parallel PV arc-faults," 39th IEEE Photovoltaic Specialists Conference, p. 3165–3172, 2013.
Z. Wang and R. Balog, "Arc fault and flash signal analysis in DC distribution systems using wavelet transformation," IEEE Transactions on Smart Grid, pp. 6(4): 1955-1963, 2015.
Fadhel, C. Delpha, D. Diallo, I. Bahri, A. Migan, M. Trabelsi and M. Mimouni, "PV shading fault detection and classification based on I-V curve using principal component analysis: Application to isolated PV system," Solar Energy, pp. 179: 1-10, 2019.
M. Dhimish, V. Holmes, B. Mehrdadi and M. Dales, "Diagnostic method for photovoltaic systems based on six layer detection algorithm," Electric Power Systems Research, pp. 151: 26-39, 2017.
Z. Yi and A. H. Etemadi, "Fault detection for photovoltaic systems based on multi-resolution signal decomposition and fuzzy inference systems," IEEE Transactions On Smart Grid, pp. 8(3): 1274-1283, 2017.
M. Dhimish, V. Holmes, B. Mehrdadi and M. Dales, "Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection," Renewable Energy, pp. 117: 257-274, 2018.
A. Belaout, F. Krim, A. Mellit, B. Talbi and A. Arabi, "Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification," Renewable Energy, pp. 127: 548-558, 2018.
F. Harrou, Y. Sun, B. Taghezouit, A. Saidi and M.-E. Hamlati, "Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches," Renewable Energy, pp. 116: 22-37, 2018.
F. Jufri, O. Seongmun and J. Jung, "Development of photovoltaic abnormal condition detection system using combined regression and support vector machine," Energy, pp. 176: 457-467, 2019.
M. Alajmi, S. Aljahdali, S. Alsaheel, M. Fattah and M. Alshehri, "Machine learning as an efficient diagnostic tool for fault detection and localization in solar photovoltaic arrays," EPiC Series in Computing, vol. 63, pp. 21-33, 2019. 62
Y. Zhao, L. Yang, B. Lehman, J.-F. d. Palma, J. Mosesian and R. Lyons, "Decision treebased fault detection and classification in solar photovoltaic arrays," in 27th Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Orlando, FL, 2012.
R. Benkercha and S. Moulahoum, "Fault detection and diagnosis based on C4.5 decision tree algorithm for grid connected PV system," Solar Energy, pp. 173: 610-634, 2018.
W. Chine, A. Mellit, V. Lughi, A. Malek, G. Sulligoi and A. M. Pavan, "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, pp. 90: 501-512, 2016.
H. Mekki, A. Mellit and H. Salhi, "Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules," Simulation Modelling Practice and Theory, pp. 67: 1-13, 2016.
G. Liu, L. Zhu, X. Wu and J.-J. Wang, "Time series clustering and physical implication for photovoltaic array systems with unknown working conditions," Solar Energy, pp. 180: 401- 411, 2019.
S. R. Madeti and S. Singh, "Modeling of PV system based on experimental data for fault detection using kNN method," Solar Energy, pp. 173: 139-151, 2018.
P. Lin, Y. Lin, Z.-C. Chen, L. Wu, L. Chen and S. Cheng, "A density peak-based clustering approach for fault diagnosis of photovoltaic arrays," International Journal of Photoenergy, p. 4903613, 2017.
A. Fathy, M. Elarini and A. Othman, "A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system," J Adv Res, p. 5:397–408, 2014.
A. E. B. Abu-Elanien and M. M. A. Salama, "Survey on the Transformer Condition Monitoring," in 2007 Large Engineering Systems Conference on Power Engineering, Montreal, Que., 2007.
E. Simonson, "Transformer Rating and Transformer Life," in IEE Colloquium on Transformer Life Management, 1998.
S. Ji, P. Shan, Y. Li, D. Xu and J. Cao, "The vibration measuring system for monitoring core and winding condition of power transformer," 2001.
B. Pahlavanpour and A. Wilson, "Analysis of transformer oil for transformer condition monitoring," in IEE Colloquium on An Engineering Review of Liquid Insulation, 1997.
P. M. Nirgude, B. Gunasekaran, Channakeshava, A. D. Rajkumar and B. P. Singh, "Frequency Response Analysis Approach For Condition Monitoring of Transformer," in Annual Report Conference on Electrical Insulation and Dielectric Phenomena, 2004.
A. Li, X. Yang, H. Dong, Z. Xie and C. Yang, "Machine Learning-Based Sensor Data Modeling Methods for Power Transformer PHM," Sensors, 2018.
K. Bacha, S. Souahlia and M. Gossa, "Power transformer fault diagnosis based on dissolved gas analysis by support vector machine," Electr. Power Syst. Res., pp. 83, 73–79, 2012.
J. Li, Q. Zhang, K. Wang, J. Wang, T. Zhou and Y. Zhang, "Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine," IEEE Trans. Dielectr. Electr. Insul., pp. 23, 1198–1206, 2016. 63
H. Zheng, Y. Zhang, J. Liu, H. Wei, J. Zhao and R. Liao, "A novel model based on wavelet LS-SVM integrated improved PSO algorithm for forecasting of dissolved gas contents in power transformers," Electr. Power Syst. Res., pp. 155, 196–205, 2018.
X. Wang, T. Wang and B. Wang, "Hybrid PSO-BP Based Probabilistic Neural Network for Power Transformer Fault Diagnosis," in In Proceedings of the International Symposium on Intelligent Information Technology Application, Nanchang, China, 2009.
A. Trappey, C. Trappey, L. Ma and J. Chang, "Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions," Comput. Ind. Eng., pp. 84, 3–11, 2015.
C. Zhang, L. Xu, X. Li and H. Wang, "A method of fault diagnosis for rotary equipment based on deep learning," in In Proceedings of the 2018 Prognostics and System Health Management Conference (PHM-Chongqing), Chongqing, China, 2018.
Liang, G., Zhao, J., Luo, F., Weller, S.R., Dong, Z.Y., 2017. A Review of False Data Injection Attacks Against Modern Power Systems. IEEE Trans. Smart Grid 8, 1630–1638. https://doi.org/10.1109/TSG.2015.2495133
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