Prominent smart grid measurements by RAP-NN to warrant the reliability of the PV system

References

• Achlerkar, P. D., S. R. Samantaray, and M. S. Manikandan. 2018. Variational mode decomposition and decision tree based detection and classification of power quality disturbances in the grid-connected distributed generation system. IEEE Transactions on Smart Grid 9 (4):3122–32. doi:10.1109/TSG.2016.2626469.
   Web of Science ®Google Scholar
• Agalgaonkar, Y. P. 2014. Control and operation of power distribution system for optimal accommodation of PV generation. London.
   Google Scholar
• Aghaei, J., and M. I. Alizadeh. 2013. Demand response in smart electricity grids equipped with renewable energy sources A review. Renewable and Sustainable Energy Reviews 18:64–72. doi:10.1016/j.rser.2012.09.019.
   Web of Science ®Google Scholar
• Agoua, X. G., R. Girard,and G. Kariniotakis. 2018. Short-term spatiotemporal forecasting of photovoltaic power production. IEEE Transactions on Sustainable Energy 9 (2):538–46. doi:10.1109/TSTE.2017.2747765.
   Web of Science ®Google Scholar
• Alam, M. J. E., K. M. Muttaqi, and D. Sutanto. 2013. Mitigation of rooftop solar PV impacts and evening peak support by managing the available capacity of distributed energy storage systems. IEEE Transactions on Power Systems 28 (4):3874–84. doi:10.1109/TPWRS.2013.2259269.
   Web of Science ®Google Scholar
• Barros, J., R. I. Diego, and M. De Apráiz. 2012. Applications of wavelets in electric power quality: Voltage events. Electric Power Systems Research 88:130–36. doi:10.1016/j.epsr.2012.02.009.
   Web of Science ®Google Scholar
• Capizzi, G., G. L. Sciuto, C. Napoli, and E. Tramontana. 2018. Advanced and adaptive dispatch for smart grids by means of predictive models. IEEE Transactions on Smart Grid 9 (6):6684–91. doi:10.1109/TSG.2017.2718241.
   Web of Science ®Google Scholar
• Costa, F. B. 2014. Fault-induced transient detection based on real-time analysis of the wavelet coefficient energy. IEEE Transactions on Power Delivery 29 (1):140–53. doi:10.1109/TPWRD.2013.2278272.
   Web of Science ®Google Scholar
• Dash, P. K., M. Padhee, and S. K. Barik. 2012. Estimation of power quality indices in distributed generation systems during power islanding conditions. International Journal of Electrical Power & Energy Systems 36 (1):18–30. doi:10.1016/j.ijepes.2011.10.019.
   Web of Science ®Google Scholar
• Eftekharnejad, S., V. Vittal, G. T. Heydt, B. Keel, and J. Loehr. 2013. Impact of increased penetration of photovoltaic generation on power systems. IEEE Transactions on Power Systems 28 (2):893–901. doi:10.1109/TPWRS.2012.2216294.
   Web of Science ®Google Scholar
• Eltawil, M. A., and Z. Zhao. 2010. Grid-connected photovoltaic power systems: Technical and potential problems—A review. Renewable and Sustainable Energy Reviews 14 (1):112–29. doi:10.1016/j.rser.2009.07.015.
   Web of Science ®Google Scholar
• Ferdinand, R. M. Cupelli, and A. Monti. 2018. Multipoint synchronized recordings in offshore wind farms with continuous measurement power quality meters. IEEE Transactions on Instrumentation and Measurement (99):1–11. doi:10.1109/TIM.2018.2829489
   Google Scholar
• Gensollen, N., V. Gauthier. M. Becker, and M. Marot. 2018. Stability and performance of coalitions of prosumers through diversification in the smart grid. IEEE Transactions on Smart Grid 9 (2):963–70. doi:10.1109/TSG.2016.2572302.
   Web of Science ®Google Scholar
• Honrubia-Escribano, A., T. García-Sánchez, E. Gómez-Lázaro, E. Muljadi, and A. Molina-Garcia. 2015. Power quality surveys of photovoltaic power plants: Characterization and analysis of grid-code requirements. IET Renewable Power Generation 9 (5):466–73. doi:10.1109/TSTE.2014.2362797.
   Web of Science ®Google Scholar
• Mahela, O. P., and A. G. Shaik. 2017. Power quality recognition in the distribution system with solar energy penetration using S-transform and Fuzzy C-means clustering. Renewable Energy 106:37–51. doi:10.1016/j.renene.2016.12.098.
   Web of Science ®Google Scholar
• Malvoni, M., M. G. De Giorgi, and P. M. Congedo. 2016. Data on Support Vector Machines (SVM) model to forecast photovoltaic power. Data in Brief 9:13–16. doi:10.1016/j.dib.2016.08.024.
   PubMed Web of Science ®Google Scholar
• Mohanty, S. R., N. Kishor, P. K. Ray, and J. P. Catalo. 2015. Comparative study of advanced signal processing techniques for islanding detection in a hybrid distributed generation system. IEEE Transactions on Sustainable Energy 6 (1):122–31. doi:10.1109/TSTE.2014.2362797.
   Web of Science ®Google Scholar
• Ray, P. K., N. Kishor, and S. R. Mohanty. 2012. Islanding and power quality disturbance detection in grid-connected hybrid power system using wavelet and $ S $-Transform. IEEE Transactions on Smart Grid 3 (3):1082–94. doi:10.1109/TSG.2012.2197642.
   Web of Science ®Google Scholar
• Shi, H., M. Xu, and R. Li. 2017. Deep learning for household load forecasting—A novel pooling deep RNN. IEEE Transactions on Smart Grid 9 (5):5271–80. doi:10.1109/TSG.2017.2686012.
   Web of Science ®Google Scholar
• Wu, W., K. Chen, Y. Qiao, and L. Z. October. 2016. Probabilistic short-term wind power forecasting based on deep neural networks. In 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), China Electric Power Research Institute, Beijing,  China. 1–8.
   Google Scholar
• Xiao, F., and Q. Ai. 2019. Data-driven multi-hidden markov model-based power quality disturbance prediction that incorporates weather conditions. IEEE Transactions on Power Systems 34 (1):402–12. doi:10.1109/TPWRS.2018.2856743.
   Web of Science ®Google Scholar