Maximum power point tracking techniques using improved incremental conductance and particle swarm optimizer for solar power generation systems

Abstract

The generation of power from solar energy by using Photovoltaic (PV) systems to convert the irradiation of the sun into electricity has been adopted over the past years. However, the PV system’s P–V and I–V characteristics become unstable when solar irradiation and temperature change. In this paper, the incremental conductance (INC) has been improved using signals to measure the current and voltage from the PV systems directly which quickly changes with the environmental conditions, and the conventional particle swarm optimization (PSO) is modified so that under multiple shaded peak PV array curves with fast-changing solar irradiance and temperature, more power is extracted at a faster rate without any tracking failure at high-speed tracking of both individual maximum power point (IMPP) and global maximum power point (GMPP) under varying solar irradiance and temperature at a longer distance to enhance the power generated. The individual and global coefficients are also improved to change with multiple shaded peak PV array curves with fast-changing solar irradiance and temperature. DC-DC converter converts DC power from one circuit to another and DC-AC inverter converts DC power to AC power. Simulation was carried out in MATLAB Simulink with different solar irradiance and temperature whereby the conventional INC and PSO were compared with the proposed INC and PSO. An experiment was carried out for a whole day from 8 am to 5 pm to test the validity of the proposed algorithm and compared it with the conventional INC and PSO by using the solar irradiance and temperature received. From both the simulation and experimental results, the proposed INC and PSO performed better by attaining high power and tracking speed with stable output results than the conventional INC and PSO.

Modern Optimization Algorithm for Improved Performance of Maximum Power Point Tracker of Partially Shaded PV Systems

“…The third technique involves starting the search agents at the expected peak location, which may be calculated using Equation (20). This technique produced somewhat shorter convergence times with a 0% failure rate than initialization with equal distance.…”

“…This strategy has been compared with conventional PSO and shows better performance [13]. The main shortcoming of this optimization algorithm is the random initialization, which may cause premature convergence to one of the LPs and a long convergence time that can be avoided with better initialization algorithms [19,20]. The strategy used in [14] improved the random initialization of particles by initializing these particles at the predicted position of peaks.…”

“…This strategy reduces the convergence time, but it should be trained for different operating voltages due to the particles using the terminal voltage, not the duty ratio [14]. The predicted positions of peaks used in this strategy are based on the anticipated peaks placed at 0.8 Voc, which needs to be more accurate, as has been discussed in [20]. Another technique employed a linear drop in inertia weight value from 0.9 to 0.4 to increase global search at the start of optimization and improve local search at the end [21].…”

“…Several strategies are used to replace the random initialization by dividing the search area (voltage or duty ratio) into equal distances and initializing the search agents at these distances [19]. This strategy is better than random initialization, but still, the convergence time can be further reduced using initialization at predicated positions of peaks [20]. This strategy has the fastest time of convergence and the lowest rate of failure compared to random initialization, but the swarm size should be equal to the number of peaks, which may limit the flexibility of the MPPT algorithms.…”