| Under the background of the national "double carbon" goal,the development of mountain photovoltaic projects has ushered in a new climax.Due to the limitations of topography and terrain,mountainous photovoltaic power stations are prone to local shadow occlusion,which leads to the formation of hot spot faults in photovoltaic arrays.To solve the above problems,based on the PV module P-V output curve under normal irradiation conditions and local shadow,the central difference method and Simpson integral method were used to obtain the slope and integral area errors,judge the shading type and extent of local shadow of the PV module,and then the cumulative value of the slope of the output current I-t curve was used to detect the hot spot fault.An improved African Vulture optimization algorithm(IAVOA)is proposed,which takes the ratio of the total power of the photovoltaic array to the difference between the maximum and minimum line currents as a new objective function,so as to dynamically reconstruct the photovoltaic array more efficiently and disperse the shadows to the maximum extent to reduce the probability of hot spot failure.First,in response to the national rural revitalization requirements,Yonghe County,Shanxi Province,carried out feasibility analysis on the construction of some desert slope mountain photovoltaic power stations,and divided the slopes according to the slope,orientation and other topographic and geomorphic factors,to establish a three-dimensional model for the mountain area of the proposed photovoltaic power station.Taking full account of local climatic conditions such as illumination and weather,the optimal layout scheme of PV array was determined through the calculation of series and parallel PV modules,array spacing and azimuth inclination Angle,and the power generation prediction and energy saving and emission reduction calculation were carried out.Secondly,the single diode equivalent circuit and photovoltaic module structure of photovoltaic cells were analyzed,and the simulation model was built to study the cause mechanism and heating effect of local shadow shadowing hot spot fault.According to the PV module P-V curve under local shadow shading and the offset of slope and integral area under normal irradiation condition,the shadow type detection index was defined to judge the local shadow shading type,and the shadow intensity detection index quantified the local shadow shading level.By extracting the I-t curves of the long-term and short-term hot spot faults and the I-T curves of the three working conditions under which the local shadow occurs but does not lead to the hot spot fault,the hot spot detection index is defined and the local shadow hot spot fault detection model is optimized.Finally,an IAVOA dynamic reconstruction algorithm based on Tent chaotic mapping,individual memory search strategy and weight time-varying mechanism is proposed,and the new objective function is applied to solve the problems of slow convergence,easy to fall into local optimal and long computing time in the dynamic reconstruction of the typical swarm intelligence algorithm,and the dynamic reconstruction of photovoltaic arrays is more efficient.The performance of the proposed IAVOA reconstruction algorithm,particle swarm optimization algorithm(PSO)and African Vulture optimization algorithm(AVOA)was compared and evaluated based on three evaluation criteria,including filling factor,power increase percentage and mismatch loss,in simulating the local shadow mode that may occur in mountain photovoltaic power plants.Simulation results show that compared with PSO and AVOA,the proposed dynamic reconstruction algorithm increases the maximum power by 1.6% and 0.35%,respectively,and has the highest filling factor and the lowest mismatch loss in all the verified local shadow modes. |
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