Exploration of grid energy efficiency influencing factors by applying principal component analysis approach

[1] Belabbas, B., Denai, M., & Allaoui, T. (2020). Hierarchical energy management and control to improve the reliability and efficiency of wind farms connected to the grid. International Transactions on Electrical Energy Systems, 30(7). Search in Google Scholar

[2] Lee, H. S., & Yun, J. J. (2019). High-efficiency bidirectional buck-boost converter for photovoltaic and energy storage systems in a smart grid. IEEE Transactions on Power Electronics, 34. Search in Google Scholar

[3] Adar, M., Najih, Y., Gouskir, M., Chebak, A., & Bennouna, A. (2020). Three pv plants performance analysis using the principal component analysis (pca) method. Energy, 207, 118315. Search in Google Scholar

[4] Gao, H., Gajjar, S., Kulahci, M., Zhu, Q. X., & Palazoglu, A. (2016). Process knowledge discovery using sparse principal component analysis. Industrial & Engineering Chemistry Research, acs.iecr.6b03045. Search in Google Scholar

[5] Zhu, L., & Chen, J. (2018). Development of energy efficiency principal component analysis model for factor extraction and efficiency evaluation in large-scale chemical processes. International Journal of Energy Research. Search in Google Scholar

[6] Kyprianou, A., Phinikarides, A., Makrides, G., & Georghiou, G. E. (2015). Definition and computation of the degradation rates of photovoltaic systems of different technologies with robust principal component analysis. IEEE Journal of Photovoltaics, 5(6), 1698-1705. Search in Google Scholar

[7] Mohammadi, H., & Dehghani, M. (2015). Pmu based voltage security assessment of power systems exploiting principal component analysis and decision trees. International Journal of Electrical Power & Energy Systems, 64(jan.), 655–663. Search in Google Scholar

[8] Rohikaa, M. R., Ashok, S., & Sunitha, R. (2020). Synchrophasor based islanding detection for microgrids using moving window principal component analysis and extended mathematical morphology. IET Renewable Power Generation. Search in Google Scholar

[9] Muzzammel, R., & Raza, A. (2020). A support vector machine learning-based protection technique for mt-hvdc systems. Energies, 13. Search in Google Scholar

[10] Li, H., Zhang, Z., & Yin, X. (2020). A novel probabilistic power flow algorithm based on principal component analysis and high-dimensional model representation techniques. Energies, 13. Search in Google Scholar

[11] Bakdi, A., Bounoua, W., Guichi, A., & Mekhilef, S. (2021). Real-time fault detection in pv systems under mppt using pmu and high-frequency multi-sensor data through online pca-kde-based multivariate kl divergence. International journal of electrical power and energy systems(Feb.), 125. Search in Google Scholar

[12] Chretien, S., Clarkson, P., & Garcia, M. S. (2018). Application of robust pca with a structured outlier matrix to topology estimation in power grids. International Journal of Electrical Power & Energy Systems, 100(SEP.), 559-564. Search in Google Scholar

[13] Hafeez, G., Alimgeer, K. S., Qazi, A. B., Khan, I., & Wadud, Z. (2020). A hybrid approach for energy consumption forecasting with a new feature engineering and optimization framework in smart grid. IEEE Access, PP(99), 1-1. Search in Google Scholar

[14] Yin, S., Yang, H., Xu, K., Zhu, C., & Wang, Y. (2021). Location of abnormal energy cdonsumption and optimization of energy efficiency of hydraulic press considering uncertainty. Journal of Cleaner Production, 126213. Search in Google Scholar