One of the important tasks for increasing the efficiency of photovoltaic (PV) system is the development and improvement of the maximum power point tracking algorithms (MPPT). These MPPT algorithms lead to the ability to catch efficiently the global maximum power point of the partially shaded PV array. One of these trackers is the particle swarm optimization (PSO) algorithm which is one of the Soft computing techniques. The conventional PSO based trackers have many advantages such as the simplicity of hardware implementation and independence from the installed system. The actual problem of the practical application of PSO is the determination of its parameters to ensure high effectiveness of extracting the global MPP. Analysis of scientific papers devoted to the PSO algorithm has shown that there is currently no methodology for the optimal parameters' selection of PSO algorithm based maximum power trackers for the PV system. This paper aims to create a convenient and reasonable method for choosing the optimal parameters of the PSO algorithm, taking into account the topology and parameters of the DC-DC converter and the configuration of solar panels. A new method for selecting the parameters of a buck converter connected to a battery has been presented. The optimal value of the sampling time for the digital MPP controllers, providing their maximum performance; has been determined based on a new methodology. Matlab/Simulink software package is used as the main research tool. The prominent outcomes identify that the modified PSO and its designed parameters best meet the requirements of the MPPT controller for the PV system.

In the microgrid systems, three-phase inverter becomes the main power electronic interface for renewable distributed energy resources (DERs), especially for the islanded microgrids in which the power quality is easily affected by unbalanced and nonlinear loads, this is due to the fact that the voltage and frequency of the microgrid are not supported by the main power grid but determined only by the inverters. Therefore, the compensation of the load unbalances and harmonics in autonomous microgrid inverters are getting more attention in power quality research areas. The main purpose of this paper is to represent an overview of the control strategies of various inverters for unbalanced load compensation

The Finite control set model predictive control (FCS-MPC) is recently introduced to control inverters without the modulation stage. The absence of the modulation stage gives an unpredictable performance of the control system. In this paper, the performance of FCS-MPC is assessed by comparing with PID control which is based on Scalar Pulse Width Modulation (PWM). The two control techniques are applied for load voltage regulation of the autonomous four-leg voltage source inverter (FLVSI). Practically, the predictive control requires a large number of calculations, resulting in high computation time and delay. In this paper, a new finite control set model predictive voltage control (MPVC) algorithm is proposed to predict the load voltages for 15 switching states instead of 16 switching states for reducing the computation time required for the control algorithm. Moreover, the algorithm is optimized by removing the repeated computations and the delay is compensated using the two-step prediction horizon principle. An accurate discrete-time state-space model of the autonomous FLVSI with output LC-filter is used for predicting the load voltages considering the neutral inductance and damping resistance of the LC filter. A simple PID control scheme with decoupled feedforward voltage and current loops is used in the DQ0 reference frame, while MPVC operates in the ABC reference frame. The simulation and experimental results are used to show the full assessment of the MPVC. The prominent outcomes show the ability of the proposed MPVC algorithm to provide high power quality under unbalanced and non-linear load conditions with high stability and robustness.

A maximum power point tracking (MPPT) is a method to extract the optimal operating point of the photovoltaic power generation system. Under partial shading