An Intelligent Approach to Active and Reactive Power Control in a Grid-Connected Solar Photovoltaic System

The increasing demand of electrical energy and environmental concerns are invigorating the use of renewable energy resources for power generation. Renewable energy resources can provide an attractive solution for present and future energy requirements. In this scenario, solar photovoltaic systems are becoming prominent and sustainable solutions with numerous advantages. However, the utilization of solar photovoltaic systems in distribution generation makes it mandatory to deploy efficient and organized control measures for integrating solar photovoltaic plants with the grid. In this paper, the control of grid-tied solar photovoltaic systems using a Kalman filter-based generalized neural network is presented with a variable step size perturb and observe-based maximum power point tracking controller to extract the maximum power from a solar photovoltaic plant. The presented system provides power-quality enhancement and supports a three-phase AC grid. The proposed approach extracts the load currents' primary components for efficient harmonics elimination, synchronizes the system with the grid and provides a fast response during rapidly changing conditions. The results of the proposed control technique are also compared with the artificial neural network-based control technique for validation purposes. The proposed algorithm is found more suitable for using a smaller number of unknown weights and training patterns with reduced computational time.

Automated summary

The increasing demand for electrical energy and environmental concerns have led to the use of renewable energy resources for power generation. Solar photovoltaic systems are becoming a popular and sustainable solution, requiring efficient control measures for integration with the grid. A proposed control system using a Kalman filter-based neural network and a maximum power point tracking controller is presented in this paper, offering power-quality enhancement and support for a three-phase AC grid. The algorithm shows promising results with reduced weights and training patterns for faster computation.