Maximum Power Tracking And Cleaning Strategy Of Photovoltaic System Under Dust Deposition Condition

In the future,the main theme of our national energy development will still be renewable energy,and solar photovoltaic power generation,as a representative of renewable energy,will play a vital role in achieving the goal of carbon neutralization and carbon peak.However,most of China’s photovoltaic power stations are located in the Western Gobi desert,and the problem of ash on the surface of photovoltaic modules is serious.If the ash is not removed in time,the power generation efficiency of large-area photovoltaic modules will be reduced or the modules will be damaged,resulting in significant economic losses.In view of the above problems,this paper studies the maximum power tracking control method of photovoltaic system under ash deposition condition and the cleaning strategy of photovoltaic system ash deposition.The specific contents are as follows:Firstly,based on the photovoltaic module and grid connection control theory,the grid connection model of photovoltaic module and inverter is established,and the output characteristics and maximum power tracking algorithm of photovoltaic module are simulated and analyzed.The component ash deposition may lead to local shadow,and then lead to multi peak of P-V curve.The traditional disturbance observation method can only track the first peak of P-V curve and can not maximize the power generation.Therefore,a global maximum power tracking control method based on cuckoo search algorithm is designed,which can quickly search the global maximum power point under shadow conditions,ensure photovoltaic utilization,and reduce the power oscillation problem of the traditional disturbance observation method.Then,the causes and influence characteristics of the surface area of the photovoltaic module are analyzed,and the coupling model of the efficiency shows that the radius,quality and arrangement of the dust particles are seriously affected by the light area.In addition,the ash accumulation will lead to the photovoltaic module partially in a high temperature state,which leads to serious damage to the photovoltaic module.An experimental platform with two sets of photovoltaic arrays is established to entally analyze the impact of ash accumulation on photovoltaic power generation efficiency.The results show that with the increase of ash accumulation density,the assembly light transmission rate is constantly reduced,while the power generation is constantly reduced,and the appropriate photovoltaic modules cleaning will greatly improve the photovoltaic power generation power.Finally,considering that module cleaning involves multi-dimensional decisions such as cleaning cost,shutdown of photovoltaic power station and expected cleaning income,wavelet neural network is used to predict the ash deposition degree of photovoltaic modules and obtain the law of the impact of ash deposition time on power generation.On this basis,considering cleaning cost,cleaning time A multi-objective dynamic cleaning scheme decision-making model for weather conditions and revenue of photovoltaic power station.The test results show that compared with the traditional fixed cleaning strategy,the proposed dynamic cleaning scheme can realize the balance among cleaning cost,time and income.