The grassland wind power station is characterized by high wind force levels, undulating terrain, and uneven ground surfaces. When ground inspection robots perform patrol tasks under different wind force conditions, it is challenging to balance path indicators with safety, thus posing higher demands on path planning methods. This paper proposed an A* path planning algorithm enhanced with terrain factors（A* algorithm (TF-A*）. Initially, this paper designed a gradient factor and a step factor based on the undulating terrain with high and low bumps and the terrain with pits and steep slopes of the grassland wind power station, and applied these factors to optimize the cost function and the heuristic function. Building upon these enhancements, the paper successfully developed an A* path planning algorithm enhanced with terrain factors. Subsequently, this paper meticulously tailored the parameters for both the slope factor and the step factor to accommodate varying wind force conditions. By taking into account the significant wind force levels prevalent in the grassland wind power station, which has substantially improved the safety and stability of the inspection robots. Following that, a series of experimental evaluations were meticulously executed, including short-distance and long-distance simulation tests, as well as real-world field experiments, to assess the efficacy of TF-A* path planning algorithm. The experimental outcomes have revealed that the TF-A* path planning method has significantly surpassed the traditional A* algorithm, with a notable 44.55% and 34.82% increase in path length metrics, and a substantial 22.06% and 2316% reduction in search time metrics across two distinct weather conditions. Specifically, under conditions of low wind force, the method strategically prioritizes distance metrics, where-as under high wind force, it adeptly integrates both distance metrics and operational safety into its considerations. It provides a novel approach for robot inspection and path planning in unstructured and uneven terrains of grassland wind power station.