Power Point Of Photovoltaic Cells Research Articles

Improvement of Particle Swarm Optimization against Slow Variations of Environmental Conditions in Maximum Power Point Tracking

Particle Swarm Optimization (PSO) is an artificial intelligence technique applied to track the maximum power point of photovoltaic cells. It has been shown to be effective against irradiance and temperature variations, including partial shading condition. However, this technique suffers from unnecessary restarts as its threshold levels are adjusted. To overcome that situation, this document presents an improved PSO that focuses on the slow irradiance variations that trigger these thresholds. Comparative results between PSO, PSO combined with the Perturb and Observe (PSO-P&O) algorithm and the proposed PSO improvement technique are shown. As a result, the proposed improvement technique avoids unnecessary restarts of the original PSO without the steady-state oscillations around the maximum power point of the PSO-P&O.

An improved energy storage switched boost grid‐connected inverter for photovoltaic applications

When the traditional two-stage boost inverter is used in photovoltaic (PV) and energy storage systems, it is necessary to connect additional bidirectional conversion devices, which will increase the loss of the system and increase the complexity of system control. Therefore, an improved energy storage switched boost (ESSB) grid-connected inverter is proposed in this paper. The system has the advantages of high integration, high gain and dead time immunity. By controlling the duty cycle of the system, the energy management of the battery can be realized. The system consists of three parts: PV cells, ESSB network and grid-connected inverter. In order to maximize the energy utilization, this paper uses the disturbance observation method to track the maximum power point of PV cells, and formulates a set of energy management strategy to control the energy flow between the three energy units. Simulation and experiments verify the superiority of the topology and the feasibility of the energy storage strategy.

Photovoltaic Energy Harvester With Fractional Open-Circuit Voltage Based Maximum Power Point Tracking Circuit

A photovoltaic (PV) energy harvester is proposed, and it adopts the fractional open-circuit voltage method to track the maximal power point of PV cells. The proposed harvester was designed and fabricated by using a 0.18- ${\mu }\text{m}$ 1P6M mixed-signal process. The input voltage of the proposed harvester may range from 0.5 to 1.1 V, and its measured peak total efficiency is 93.4%. The proposed harvester is suitable for Internet-of-Things applications, and the maximal duty cycle it can afford for a device with a 5-mA load and 20-ms activation time is 50%.

Influences of Environmental Factors on the Maximum Power Point of Photovoltaic Cells

Based on mathematical models and output characteristics of photovoltaic cells, the simulation model for engineering application is established. By the model, the output characteristics of photovoltaic cells with different environmental factors can be analyzed. In MATLAB/SIMULINK environment, the model of the PV maximum power point tracking control is constructed. The simulation results demonstrate that PV can run at the maximum power point with the change of environment factors.

Improved Golden Section Application in Maximum Power Point Tracking of Photovoltaic Power Generation

In order to increase the output power of the photovoltaic system, the maximum power point tracking is needed. As the starting point of research in the output nonlinear characteristics, analysis of the advantages and disadvantages of the conventional algorithm and have the poor dynamic and steady-state performance of the maximum power point tracking(MPPT), the improvement method of golden section(IGSS) is applied to the photovoltaic power generation system. The results indicate that the method can quickly track the maximum power point of photovoltaic cells.

A Strategy Research on MPPT Technique in Photovoltaic Power Generation System

Output mode of photovoltaic cell is a complex transcendental equation, which is rather difficult to figure out by the numerical method. Traditional MPPT requests highly of initial value with low accuracy. In this thesis, simulation model of photovoltaic cell is built to simulate environmental situations under different lights and temperatures, taking advantage of particle swarm and ant colony intelligent algorithm to track the maximum power point of photovoltaic cell. Furthermore, a kind of intelligent perturbative tracking method is put forward combined with traditional perturbative tracking and intelligent tracking. The simulation result showing that intelligent perturbative tracking strategy has superior accuracy and adaptability than traditional MPPT method.

Dual-Mode Control MPPT Algorithm Based on Improved Genetic Algorithm and Perturbation and Observation Method

To improve the tracking accuracy of the maximum power point of photovoltaic cells can improve the operational efficiency of photovoltaic power generation system and reduce the cost of photovoltaic electricity. Because the common perturbation and observation (P&O) runs at a low speed, this paper proposes a new dual-mode control MPPT algorithm based on improved genetic algorithm and perturbation & observation method. Then this method was used in the experiment. From the experimental results can be seen, the algorithm overcomes the shortcomings of the traditional perturbation and observation method can quickly and accurately tracking the maximum power point of photovoltaic cells output, you can improve system output power.