Enhancing Power Quality in Distributed Generation Systems through Hybrid Active Power Filtering and Intelligent Control

The integration of distributed generation, such as PV systems, into power grids has become increasingly popular due to its environmental benefits and potential to reduce dependence on traditional fossil fuel-based generation. However, the intermittent nature of Renewable Energy Sources (RES) like PV introduces challenges related to Power Quality (PQ), including voltage and current harmonics, which can adversely impact the stability and efficiency of the grid. To address these challenges, this study proposes the use of a Hybrid Active Power Filter (HAPF), which is being able to mitigate harmonic distortions and reactive power issues and stabilize the voltage in the distribution system. The proposed HAPF is energized by a PV system-fed high-gain Interleaved Luo Converter, which offers high efficiency and improved power conversion capabilities. A Recurrent Neural Network (RNN) based harmonic extraction technique is introduced to accurately identify and quantify harmonic components in the system's voltage and current waveforms. The RNN provides an intelligent solution to adaptively extract harmonic information and facilitate precise control of the HAPF. The proposed system is simulated in MATLAB, and the results demonstrate that the RNN-based harmonic extraction in conjunction with the HAPFs energized by a PV system-fed high-gain Interleaved Luo (I-Luo) Converter significantly enhances power quality in the distributed generation system in which THD value is 1.96% which shows little harmonic distortion entering the grid.