Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes

Abstract

Data-based procedures for monitoring the operating performance of a PV system are proposed in this paper. The only information required to apply the procedures is the availability of system measurements, which are routinely on-line collected via sensors. Here, kernel-based machine learning methods, including support vector regression (SVR) and Gaussian process regression (GPR), are used to model multivariate data from the PV system for fault detection due to their flexibility and capability to nonlinear approximation. Essentially, the SVR and GPR models are adopted to obtain residuals to detect and identify occurred faults. Then, residuals are passed through an exponential smoothing filter to reduce noise and improve data quality. In this work, a monitoring scheme based on kernel density estimation is used to sense faults by examining the generated residuals. Several different scenarios of faults were considered in this study, including PV string fault, partial shading, PV modules shortcircuited, module degradation, and line-line faults on the PV array. Using data from a 20 MWp grid-connected PV system, the considered faults were successfully traced using the developed procedures. Also, it has been demonstrated that GPR-based monitoring procedures achieve better detection performance over SVRs to monitor PV systems.

A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models

“…Moreover, SVR models proved to be efficient in the presence of limited samples 66 . Additionally, SVR has been broadly applied in various applications, including wind power forecasting 67 , fault detection 8 , and solar irradiance prediction 48 . This study built six SVR models using different kernels and an optimized SVR using Bayesian optimization (Table 2 ).…”

“…During this pandemic, AI showed to be useful in predicting outbreaks and aid assemble quickly evolving data to support general health specialists in complex decision-making 7 . In addition, various AI-based tools were designed in the healthcare field 3 , 6 , 8 . For instance, a team at Boston Children’s Hospital developed an automated electronic information system called Health Map 9 .…”

“…Specifically, this study investigates the forecasting ability of the optimized GPR, a kernel-based machine learning method, in forecasting the COVID-19 time series. This choice is motivated by the desirables features of the GPR model, including its simple and flexible construction using the mean and covariance functions, its ability and superior nonlinear approximation, and the possibility to explicitly provide a probabilistic representation of forecasting outputs 8 , 45 . The contributions of this paper are summarized in the following key points.…”

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

“…Different classifiers were used to evaluate the learning model, and results indicated that RF has the capability to provide the best results. Harrou et al [69] elaborated ML prediction frameworks including SVM, Gaussian Process Regression (GPR), and mapping-based kernel machines for condition monitoring of PV systems. Six fault types specified by bridging faults and partial shading, and PV module short circuits, module degradation, and line-to-line faults, were examined using P-V analysis.…”

Optimized Gaussian Process Regression by Bayesian Optimization to Forecast COVID-19 Spread in India and Brazil: A Comparative Study

“…Specifically, this study investigates the forecasting ability of the optimized GPR, a kernel-based machine learning method, in forecasting the COVID-19 time series. This choice is motivated by the desirables features of the GPR model, including its simple and flexible construction using the mean and covariance functions, its ability and superior nonlinear approximation, and the possibility to explicitly provide a probabilistic representation of forecasting outputs [13], [14]. Specifically, the BO algorithm is employed to find the optimal hyperparameters of the GPR model to improve the forecasting quality.…”