During the photovoltaic inspection of drones, it is necessary to obtain the position information of the photovoltaic panels in the image to determine the fault location of photovoltaic modules. The accuracy of the position and orientation system (POS) data transmitted by small drones is low because of their technical limitations, making it difficult to use them for photovoltaic panel positioning in small drone images. This paper proposes an automatic positioning method for photovoltaic panels in drone images to address this issue. Based on the drone images and the geographical coordinates of the photovoltaic panels within the inspection area, the actual location of the photovoltaic panels within the image is determined, providing accurate location information for troubleshooting. First, the proposed method establishes a coordinate transformation model and its unknown parameters based on the projection relationship between the object and image. It determines the parameter range based on the shooting conditions, selects multiple sets of parameters to determine the geographical coordinates of the photovoltaic panels within the image, and compares these with the mean square error of the back-projection difference of the photovoltaic panels within the area as a loss value. Next, it optimizes the parameters by comparing multiple sets of loss values and verifies them using overlapping images. Finally, based on the optimal parameters, coordinate conversion is performed to achieve automatic positioning of the photovoltaic panel image. In two experiments using more than 14,000 fault boards, the positioning accuracy of the proposed method reaches 97.78% and 98.54%, which increases by 13.06 percentage points and 12.96 percentage points, respectively, compared to the positioning method based on POS data. It also overcomes the influence of changes in the shooting conditions, thereby verifying the accuracy and robustness of the proposed method.