Research On Detection Of Maximum Power Point Based On Improved Particle Swarm Optimization Algorithm

With the shortage of non-renewable energy, the development of the society couldn’t leave the energy using and the new energy research had become the research hot spot. And solar energy as a huge amount of renewable energy, its energy to the earth’s surface is equal to hundreds of millions of barrels of oil burning. It’s clean, renewable, good expansibility, make it gradually attention by people and the future might become a substitute for non-renewable energy which would be perfect. Now it was the energy supply in the future energy system which could become an important part. Therefore, as the shortage of energy, the use of solar energy had become a hot academic research. Using of solar energy has many ways, but the most common way was the photovoltaic power generation. Photovoltaic cells in practical applications, due to the change of sun’s position, houses and other shelter and uneven lighting conditions, which led to hot spot phenomenon, which affected the power output of photovoltaic cells. Based on the theory of the basic model of photovoltaic cells, the paper research on output characteristics of photovoltaic power generation in the case of shade and the maximum power tracking algorithm.Firstly, the research introduced the research background of this project, at the same time, domestic and foreign research present situation of photovoltaic power generation were expounded; On this basis, it introduced the basic principle and mathematical model of photovoltaic cell; Then introduced the cause and the solution of maximum power point, and analysed its operation, and simulated by MATLAB / SIMULINK software for its output characteristics; In addition, further work on the basic principle of maximum power tracking algorithm and traditional shade case were introduced and analyzed their advantages and disadvantages. By comparing these algorithms, this paper proposed an improved particle swarm algorithm which had strong global search capability, optimization rate and other advantages, to supplement the existing algorithms, so the the maximum power tracking became fast, stable and accurate.When the photovoltaic cells in partial shade, the output power would be affected, and its output characteristic curve would appear more peak. Traditional maximum power tracking algorithm couldn’t realize the tracking control of multi-peak conditions and the local maximum power tracking algorithm for computing complex, the maximum power tracking control at a slower pace, not good tracking control. To solve the above problem, this paper used particle swarm optimization, and this algorithm had been improved. In order to increase the particle optimization of capacity, speed and particle inertia factor were improved, in addition, in order to prevent particles from falling into local optimal solution, the algorithm was introduced in sensitive particle. According to the changes of the sensitive factor to fitness, you could judge whether the external environment changed in a timely manner. Using improved particle swarm algorithm could quickly find the maximum power point, when the environment mutation, could immediately find new maximum power point, it could maked up the defect of the other algorithms. In order to verify feasibility of the algorithm, this paper, by using MATLAB/SIMULINK software simulation was set up a complete system, aiming at the static and dynamic environment are simulated respectively, to verify the accuracy and feasibility of the algorithm.