考虑参数权重与分层映射的光伏组件健康程度检测

Health Detection of Photovoltaic Modules Considering Parameter Weights and Hierarchical Mapping

光伏系统的健康程度检测对预防故障及保障系统安全运行至关重要。选取光伏组件的光生电流、串联电阻和并联电阻为其健康参数,该文提出了一种考虑健康参数权重与辐照度分层映射的光伏组件健康程度检测方法,来提高健康程度检测的精度与适用范围。首先依据光伏组件I-V曲线,通过蝠鲼觅食优化(MRFO)算法进行参数辨识;其次根据辐照度高低将辨识结果分层映射至两种辐照度参考状态,提高低辐照状态下的映射精度;然后根据组件预期寿命内允许的功率衰减情况建立光伏组件自然衰减模型,估算参数预期值作为参考,再结合实际的参数提取结果计算参数劣化度;最后使用熵权法确定参数权重,计算表征光伏组件健康程度的健康指数。仿真与实验结果表明,该方法可对任何环境参数下的光伏组件健康程度进行有效检测。

Traditional health status detection of photovoltaic(PV)modules generally includes only states:health or fault,which is not conducive to fault prediction and system maintenance.Recently,methods have been presented to detect the health degree of PV modules.However,it is difficult to detect the health status of PV modules in a low-irradiation environment,and the effect of the parameter weights and natural attenuation is ignored.This paper proposes a detection method to characterize the health of PV modules by calculating the health index through parameter deterioration degree and parameter weights.Firstly,according to the I-V curve of the PV modules,the health parameters are identified by the manta ray foraging optimization(MRFO)algorithm,including the photo generated current Iph,series resistance Rs,and parallel resistance Rsh.Secondly,setting the high/low irradiation reference state,according to the irradiance measured when the I-V curve was acquired,the parameters identification results are hierarchically mapped,which improves the accuracy of parameter extraction in a low irradiation environment.Thirdly,the expected value of parameters after n years of natural attenuation is obtained by establishing the natural attenuation model of PV modules and used as a reference for calculating the parameter deterioration degree.Finally,taking the health parameters of PV modules in various health states as samples,the parameter weights are calculated by the entropy weight method,and the health index of the range[0,1]is calculated by combining the parameter deterioration degree.The smaller the index,the healthier the PV module.Different parameters influence the health status of PV modules,and the reliability of detection results can be increased by considering the parameter weights.Simulation and experimental results show that the convergence speed of parameter identification using MRFO is fast,and the identification error is as low as 4.647×10-4.The parameter identification results are mapped in different ways.When the irradiance is 200 W/m2,the hierarchical mapping reduces the root mean square error by 89.95%compared with the traditional mapping method.The health detection results of PV modules under a low irradiation state are consistent with the reference value using hierarchical mapping.The natural attenuation model provides the natural attenuation parameters as the expected value of parameters,combined with the parameter extraction value to calculate the parameter deterioration.The calculation results show that Rsh is prone to large detection errors.However,the parameter weight of Rsh is only 7.187%,which has little impact on the health status.The health index with parameter weights is in line with expectations.With the increase of abnormal attenuation degree of experimental modules’characteristic parameters,the health index gradually increases from 0.015 to 0.962,indicating that the health of the PV modules is decreasing.The following conclusions can be drawn.(1)MRFO has a fast convergence speed and strong optimization ability,which is appropriate for parameter identification.(2)Hierarchical mapping can effectively improve the accuracy of parameter extraction and health detection of PV modules in low-radiation environments.(3)The natural attenuation model of PV modules calculates the parameters’expected value according to the operation year of modules,which avoids natural attenuation affecting the accuracy of parameter deterioration degree.(4)The parameter weights consider the influence of parameters on the health status,which effectively improves the reliability and accuracy of the health detection of PV modules.