光伏电池组位置偏差检测方法研究

Research on detection method of position deviation of photovoltaic module

针对光伏电池组因位置偏差而无法进行电致发光成像的问题,对电池组位置偏差的机器视觉检测法进行了研究。通过图像预处理获取电池组的边缘特征,并利用亚像素检测法对边缘进行细分从而获得精确的边缘点坐标。再对所得的亚像素边缘点使用迭代重加权最小二乘法(IRLS)进行拟合得到边缘直线的方程。通过对比基本最小二乘法的拟合结果,证明了该算法针对边缘上的离群点具有较强的鲁棒性。而后通过位置偏差的检测实验评估了算法的性能,结果表明此算法的检测误差约1.792%,且平均检测耗时仅为224.7 ms,因此能够满足实际应用的需求。最后通过对比实验,验证了该算法相比于传统方法有着更高的精度。

To solve the problem that electroluminescence imaging cannot be performed due to the position deviation of photovoltaic(PV) modules, A machine vision inspection method for PV modules position deviation was studied. The edge features of the cell modules are obtained by image preprocessing, and the sub-pixel edge detection method is used to subdivide the edges to obtain the exact edge point coordinates. The subpixel edge points are then fitted using iterative reweighted least squares(IRLS) to obtain the equation of the edge lines. By comparing the fitting results with the basic least squares method, it is demonstrated that the algorithm is robust to outliers on the edges. The performance of the algorithm was then evaluated through position deviation detection experiments, and the results showed that the detection error of this algorithm is about 1.792%, and the average detection time is only 224.7 ms, so it can meet the requirements of practical applications. Finally, a comparison experiment was conducted to verify the higher accuracy of the algorithm compared with the traditional method.