Machine Learning - Gaussian Process Regression (ML-GPR) based Robust H-infinity controller design for Solar PV System to achieve High Performance and Guarantee Stability

The combined action of Machine Learning and Control System Algorithm is proposed in this Renewable Energy System. The reason for proposing the Renewable Energy System, which is the clean energy source from the nature and it’s free of cost. Here the Renewable Energy system includes Solar PV System. Since this energy system has a higher scope of installation in most countries. For that, we propose a controller which achieves high performance and Guarantees Stability. In this proposed system the disturbance and Uncertain parameters are considered both internal and external parameters. To overcome this problem much Robust Control design is being already implemented in the Control Engineering Field to attain System Stability. Whereas this proposed method is a new approach to examining the System Stability by combining Machine Learning - Gaussian Process Regression (ML-GPR) with Robust H-infinity Controller. The major approach used in Machine Learning-GPR is to gather the data of the initial system and gradually decrease the Uncertainty, which results in improving the performance. Finally, ML-GPR learns a model with Uncertainty bounds. Now we combine a Control Framework (i.e., H-infinity Controller) that Guarantees Stability for this uncertain model. The design Environment used for the experimental verification is MATLAB/Simulink software. The Simulation Results confirmed the effectiveness of the newly proposed Control Strategy.