Space-vector methods for two/three level inverters involve computation of dwell times for synthesizing the reference space vector which is practically impossible for high resolution inverters. Moreover, density of space vectors in the space vector diagrams for high resolution inverters is very high and that computation of dwell times may not be necessary. So, nearest space vector approach is used for controlling high resolution inverters. This paper presents a different approach to find the nearest space-vector algorithm for high-resolution multilevel inverters. The method finds coordinates of all the space vectors in the vicinity of a given reference space vector and determines the nearest one and thus controls the inverter. Working of the control strategy is verified by simulations in MATLAB/Simulink

Power generation is challenged to meet energy demand during peak hours. As a result of limited non-renewable energy resources, power utilities heavily rely on fossil fuels. Therefore, scientists and researchers are looking for some distributed generators to provide additional power during peak hours. During such period, load demand is solved using solar power. As a consequence, grid-connected solar Photovoltaic (PV) systems are catching the attention owing to their ability to significantly reduce the use of fossil fuels. Under Partial Shading Condition (PSC), this paper utilizes Luo Converter along with Cascaded Artificial Neural Network (ANN), which is a Machine Learning Based Maximum Power Point Tracking (ML-MPPT) approach for tracking optimal power from PV system. The gained DC supply is converted into AC voltage using VSI attached to the system. In addition, PI controller engaged controls the voltage at grid side and results in effective grid synchronization. Furthermore, MATLAB Simulink analysis is carried out and the outcomes reveal the effectiveness of proposed system with 98% efficiency under different PV circumstances.