Adaptive Power Electronics Control for High-Penetration Renewable Energy Systems Using Deep Neural Networks

Abstract

The ever adoption of renewable energy sources like solar photovoltaic and wind power into the contemporary electrical grids has changed the face and functioning of power grids in a great way. Despite the fact that the renewable energy technologies have environmental and economic advantages, they are intermittent and unpredictable, which implies a number of issues concerning grid stability, voltage regulation, and the quality of power. Power electronic converters are critical and play a vital role towards effective energy conversion and grid integration of renewable sources. Nonetheless, traditional control measures, especially Proportional-Integral (PI) controllers, do not have the ability to stabilise the optimal operation when operating conditions vary very quickly because of its reliance on constant parameters and the assumptions of linear system. As a way of overcoming these constraints, this paper will present an adaptive power electronics control system where Deep Neural Networks (DNNs) are used in renewable energy systems with high penetration. The suggested method is based on the data-driven learning mechanism, which allows the controller to process real-time measurements of the system such as voltage, current, power output, and grid conditions and to alter the control parameters of power electronic converters dynamically. With the nonlinear modelling and pattern recognition of deep learning, the DNN-based controller is able to easily represent the behaviour of a complex system and react swiftly to changes in the renewable energy generation and load demand. Simulation experiments to assess the effectiveness of the proposed control strategy are done in different operating scenarios. The findings suggest that the DNN-based adaptive controller is much more successful in the enhancement of voltage stability, overall harmonic distortion, and the overall dynamic response of the system in comparison to the traditional PI-based control techniques. Moreover, the smart control system enhances the effectiveness of energy conversion and leads to the credible integration of renewable sources into the grid. Thus, the suggested solution can be considered as an encouraging way forward in the sphere of sophisticated smart grids and renewable energy-based power systems in the future.