Tracking Strategy Of Maximum Power Point Of Photovoltaic Cells In Partial Shadow

Solar energy has become an important choice for renewable energy due to its clean and safe,continuous availability,and easy accessibility.However,the photoelectric conversion efficiency of photovoltaic(PV)systems is affected by many factors,including light intensity,ambient temperature,and local shadow conditions(PSC),etc.Ideally,solar photovoltaic cells work under uniform light,the PV characteristic curve is a single peak curve,using traditional methods or a single group optimization algorithm can quickly and accurately locate the maximum power point(MPP).However,in actual situations,due to the occlusion of clouds,buildings,etc.,the battery components receive uneven light,and the output PV characteristic curve is a multi-extreme curve,which leads to the traditional Maximum Power Point Tracking(MPPT)method easily and incorrectly falling into a local optimum,resulting in a sharp drop in the output power of the photovoltaic system.In order to improve the power generation efficiency of the PV system under PSC,the paper has done the following research.The paper established a 3×2 photovoltaic array model and analyzed the output characteristics of photovoltaic cells under different conditions through simulation experiments.It focused on the analysis of the difference in the coverage area,degree and method of the shadow under partial shadow conditions.The influence of the characteristic curve and the degree of power mismatch,this study can be used as an evaluation basis for substation site selection and photovoltaic cell installation angle.The paper conducts mathematical derivation and simulation analysis on the application of the early MPPT method in photovoltaic arrays.Finally,an improvement is made on the basis of Lion Swarm Optimization(LSO),and an improved Lion Swarm Optimization(ILSO)with enhanced local search capabilities is proposed.LSO has excellent global search capabilities,but the algorithm in the later stage of the iteration,it is easy to oscillate back and forth between multiple feasible solutions,and it is difficult to accurately track the only optimal solution.The improved method of the paper is to introduce a reverse search mechanism and an adjustment factor to enhance the search power of the cub in the later iteration of the algorithm to reduce the oscillation.At the same time,on the basis of LSO,give full play to the female lion’s communication ability to avoid falling into the local optimum.ILSO not only retains the excellent global search capability of LSO,but also improves the local search capability,which can enable the photovoltaic power generation system to track the MPP faster and more accurately.The ILSO proposed in the paper was applied to MPPT,and a variety of lighting conditions were set for simulation verification.At the same time,it was compared with the simulation experiment of LSO and PSO applied to MPPT.The results showed that the proposed maximum power tracking based on ILSO under PSC It has a more stable effect.Compared with LSO and PSO,it has the advantages of faster convergence,smaller oscillation and higher accuracy under uniform illumination,partial shadow and time-varying illumination.