Study On Maximum Power Point Tracking Method Of Photovoltaic Power System Under Partial Shading Conditions

Photovoltaic(PV)power generation is widely used due to its noiseless,non-pollution and inexhaustible characteristics,such as building integrated PV(BIPV),mobile PV equipment,large PV power station and so on.Partial shading is one of the ubiquitous phenomena in PV power system.It will lead to many adverse effects to PV system,such as output power fluctuation,hot spot effect,lost effectiveness of maximum power point tracking(MPPT)etc.,which will seriously affect the system security and reliability.Under partial shading conditions(PSCs),output characteristics curves of PV system are nonlinear and have multiple peaks,which results in the failure of conventional MPPT and control methods.Therefore,how to weaken the influence of uneven illumination of photovoltaic system has become a hot research content in the PV power generation technology.To address this issue,attentions have been placed on two aspects: the connections and configurations of the PV system,and the advanced global MPPT techniques.Under PSCs,MPPT and control research mainly focuses on the accuracy,computational problems,stability,real-time and optimal topology model,etc.,so this thesis designs the new global MPPT methods,and put forward the new PV system topology.Combined the subject ‘Impact Analysis and Control Method for Photovoltaic Power Generation System under Hot Spot Effect'(51377187)which is supported by National Science Foundation of China,this thesis ‘Study on Maximum Power Point Tracking Method of Photovoltaic System under Partial Shading Conditions' is carried on profoundly.The main contributions of this paper are as follows.Firstly,aimed at the presence of the serious non-linear and multiple peaks of the PV module or array output characteristics under the complex PSCs,and in consideration of having good performance of PSO and AFSA in multi-dimensional multi-peak function optimization and global optimization,a novel artificial fish swarm algorithm(AFSA)which is optimized by particle swarm optimization with extended memory(for short,PSOEMFSA)is proposed and applied to global MPPT of the partially shaded PV system to improve the searching precision.In this algorithm,both the velocity inertia factor and the memory and learning capacity of particle swarm optimization with extended memory(PSOEM)are introduced into AFSA.To validate the effectiveness of the novel algorithm,numerical experimental results tested on a set of numerical benchmark functions show that PSOEMFSA has better performance in searching precision than that of PSO algorithm,FSA,and PSO-FSA in most of the experiments.In addition,simulation results also show that the proposed approach is effective in MPPT under PSCs and outperfoms the traditional methods interms of searching precision.Secondly,an improved AFSA with self-adaptive global bestguided quick searching strategy is presented to apply to global MPPT of the complexly partially shaded PV system based on the proposed PSOEMFSA control method in order to further improve the convergence speed,statablity and searching precision.In this algorithm,the PSOEM-FSA is improved by hybridizing it with adaptive visual and step,and the resulting algorithm is comprehensive improvement of artificial fish swarm algorithm(for short,CIAFSA).Combining the searching capabilities of the PSOEMFSA and the self-learning ability of adaptive visual and step for AFSA,CIAFSA is developed.To validate the effectiveness of the novel algorithm,numerical experimental results tested on a set of numerical benchmark functions show that the proposed algorithm has better performance in convergence speed and statablity than other algorithms in most of the experiments.In addition,simulation results also show that the proposed approach is effective in MPPT under PSCs and outperfoms the traditional methods in terms of convergence speed and statablity.Thirdly,due to the presence of multiple peaks of the PV array output characteristics under PSCs,and in view of the response speed of actual systems and the GMPPT control precision,a novel GMPPT control method for a PV system with reduced steady-state oscillation based on a two-stage particle swarm optimization(TSPSO)algorithm is proposed.The grouping method of the shuffled frog leaping algorithm(SFLA)is incorporated in the basic PSO algorithm,which ensures fast and accurate searching of the global extremum.An adaptive speed factor is also introduced into the improved PSO to further enhance its convergence speed.Test results show that the proposed method converges in less than half the time taken by the conventional PSO method,which confirms the superiority of the proposed method over the standard PSO algorithm in terms of tracking speed and steady-state oscillations under different PSCs.At last,by analyzing the existing photovoltaic system topology and output characteristics of PV system under partial shading conditions,multilevel cascaded DC/DC converter PV system with modified PSO based on maximum power point tracking is put forward to reduce non-uniform illumination from the source to the influence of the PV system.In the proposed control system,a multilevel cascaded Buck converter is used to independently control each PV panel.In addition,PSOEM control method and a PWM with permutation of DC converter switching can predict all PV voltages balance switch utilization.Simulated results demonstrate that the proposed multilevel cascaded Buck converter can independently control each PV panel,and the proposed system model effectively improves the PV system output power quality,which increase the reliability and stability of the system.