Control strategy for current limitation and maximum capacity utilization of grid connected PV inverter under unbalanced grid conditions.

Under grid voltage sags, over current protection and exploiting the maximum capacity of the inverter are the two main goals of grid-connected PV inverters. To facilitate low-voltage ride-through (LVRT), it is imperative to ensure that inverter currents are sinusoidal and remain within permissible limits throughout the inverter operation. An improved LVRT control strategy for a two-stage three-phase grid-connected PV system is presented here to address these challenges. To provide over current limitation as well as to ensure maximum exploitation of the inverter capacity, a control strategy is proposed, and performance the strategy is evaluated based on the three generation scenarios on a 2-kW grid connected PV system. An active power curtailment (APC) loop is activated only in high power generation scenario to limit the current's amplitude below the inverter's rated current. The superior performance of the proposed strategy is established by comparison with two recent LVRT control strategies. The proposed method not only injects necessary active and reactive power but also minimizes overcurrent with increased exploitation of the inverter's capacity under unbalanced grid voltage sag.

Performance investigation of state-of-the-art metaheuristic techniques for parameter extraction of solar cells/module.

One of the greatest challenges for widespread utilization of solar energy is the low conversion efficiency, motivating the needs of developing more innovative approaches to improve the design of solar energy conversion equipment. Solar cell is the fundamental component of a photovoltaic (PV) system. Solar cell's precise modelling and estimation of its parameters are of paramount importance for the simulation, design, and control of PV system to achieve optimal performances. It is nontrivial to estimate the unknown parameters of solar cell due to the nonlinearity and multimodality of search space. Conventional optimization methods tend to suffer from numerous drawbacks such as a tendency to be trapped in some local optima when solving this challenging problem. This paper aims to investigate the performance of eight state-of-the-art metaheuristic algorithms (MAs) to solve the solar cell parameter estimation problem on four case studies constituting of four different types of PV systems: R.T.C. France solar cell, LSM20 PV module, Solarex MSX-60 PV module, and SS2018P PV module. These four cell/modules are built using different technologies. The simulation results clearly indicate that the Coot-Bird Optimization technique obtains the minimum RMSE values of 1.0264E-05 and 1.8694E-03 for the R.T.C. France solar cell and the LSM20 PV module, respectively, while the wild horse optimizer outperforms in the case of the Solarex MSX-60 and SS2018 PV modules and gives the lowest value of RMSE as 2.6961E-03 and 4.7571E-05, respectively. Furthermore, the performances of all eight selected MAs are assessed by employing two non-parametric tests known as Friedman ranking and Wilcoxon rank-sum test. A full description is also provided, enabling the readers to understand the capability of each selected MA in improving the solar cell modelling that can enhance its energy conversion efficiency. Referring to the results obtained, some thoughts and suggestions for further improvements are provided in the conclusion section.