The efficiency of a thermal conversion system requires adequate radiative properties. In a thermophotovoltaic system, the optical filter plays a key role into the overall performance of the system. The purpose of this paper is to study a one-dimensional TiO2/SiO2 photonic crystal for application as thermophotovoltaic optical filter. The influence of the layers’ thicknesses, incidence angle, and number of periods on the spectral reflectance has been investigated through the transfer matrix method. It was found that, when one varies the number of layers from 6 to 12, an improvement of the optical properties of the spectral filter is observed. The surface wave through a film of photonic crystal generates a resonant transmission for a truncated structure. These results are in conformity with those found in the literature.

To get the maximum power point (MPP), the algorithms developed in the literature fail for the most part when the atmospheric conditions vary rapidly. This paper presents the optimization by the swarm particles (PSO) of a sliding mode controller (SMC) to optimize photovoltaic systems (PV) evolving under the conditions of a rapid variation of the sunshine. The stochastic PSO algorithm is used in particular to determine the optimal gains of the sliding mode controller, and this algorithm guarantees the stability and global fast convergence of the system. Efficiency of the proposed hybrid algorithm is proved by using a Solarex MSX-60 module connected to a step-up DC-DC converter supplying a resistive load. The performances are compared to the conventional perturb and observe technique (P&O), PSO, CUCKOO, grey wolf optimization (GWO), and Whale Optimization Algorithm (WOA) in terms of convergence speed, i.e., 0.05 s, tracking speed after extreme change in irradiance 0.03 s, high Ability to track large changes in power before 1 s. Simulation results show that this technique is effective in conditions of a rapid variation of the irradiance.

To increase the efficiency of photovoltaic (PV) systems, maximum power point (MPP) tracking of the solar arrays is needed. Under partially shaded conditions (PSCs), the solar arrays power–current (P–I) characteristic has multiple MPP. This paper presents various methods and approaches of tracking the MPP from a PV generator operating under PSC. Some comparisons, advantages, drawbacks and critical analysis of each method are discussed. It was found that, indirect methods use empirical data or mathematical expressions of numerical approximations to estimate the MPP from the PV generator’s voltage, current and irradiance. Direct methods offer the advantage of obtaining the actual maximum power from the PV generator’s voltage and current. Artificial intelligence methods do not need exact mathematical models. They can perform under parameter variation, load and supply voltage disturbances. Finally, novel methods require less number of iterations to converge, independent to the initial conditions. All these algorithms can be included in some of the DC/DC converters and MPP trackers for stand-alone or grid-connected systems.