Power quality improvement using a 31-level multi-level inverter with bio-inspired optimization approach - Praveena, A.; Sathishkumar, K. (Frontiers in Energy Research, 2024)
In recent years, the Power Quality (PQ) improvements have been explored through various approaches. The employment of electronic devices with renewable energy sources has expanded the harmonic level of voltage and current. Due to harmonics, the PQ of a specific electrical system gets affected. At critical load conditions, the traditional PQ mitigation approaches fail to develop the performance of the system. Therefore, in this work the Spider Monkey Optimized Convolutional Neural Network (SM-CNN) based 31 -level Multi Level Inverter (MLI) is used. This method intended for balancing the reactive power demands and enhance real power in the grid-tied Photovoltaic (PV) system. An MPPT algorithm depending on Radial Basis Function Neural Networks (RBFNN) is utilized to maximize PV power. For strengthening the voltage level of the PV and to generate higher dc voltage with a minimized switching loss, an Integrated Fly back-Boost Converter (IFBC) is introduced. The presented technique is implemented in the MATLAB/Simulink platform to figure out the estimation of PQ issues. The suggested MLI lessens the Total Harmonic Distortion (THD) value to 2.45 % with improved power factor.