Regular cleaning of the photovoltaic (PV) panel is crucial for maintaining optimal photovoltaic power generation efficiency. However, manual cleaning methods for PV panels are often inadequate and costly, underscoring the need for the introduction of PV cleaning robots. A significant challenge in the development of these robots is the recognition of PV panel poses. We propose the YOLOv8n-Photovoltaic-Pose (YOLOv8n-PP) method, a lightweight pose recognition algorithm, to address this issue. It is specifically designed for PV panel cleaning robots. Built upon the YOLOv8 framework, YOLOv8n-PP incorporates the Mobile-ViT visual module, which is both lightweight and mobile-friendly. This integration helps mitigate the effects of varying target poses from the robot’s mobile perspective. Additionally, we introduce the LMPDIoU boundary box regression loss to enhance the precision of PV panel recognition. Furthermore, we have developed a diverse and comprehensive dataset for PV panel poses to improve the model’s generalization capabilities. Our method shows improvements in both precision and recall, providing an effective solution for PV pose recognition.

The i_p-i_q harmonic detection method, which is based on instantaneous reactive power theory, involves cumbersome and complex computations. In addition, the adoption of a low-pass filter (LPF) degrades the dynamic response performance of harmonic detection. To achieve accurate and fast detection of grid harmonic currents for efficient power grid compensation, this paper proposes a total current harmonic detection method using a dual second-order generalized integrator (DSOGI). This method eliminates the calculation steps of the active and reactive components of load current that are required in the conventional i_p-i_q method. More importantly, it replaces the LPF in the traditional detection scheme with a positive-sequence fundamental component extraction structure based on the DSOGI. Simulations and experimental tests are conducted on the proposed method under balanced grid conditions; the total harmonic distortion (THD) is approximately 2%, and the system stabilizes within 0.04 s. The detection speed and accuracy of the proposed method are superior to those of the traditional i_p-i_q harmonic detection method, the sinusoidal amplitude integrator (SAI)-based method, and the complex coefficient filter (CCF)-based method.