Inspecting photovoltaic (PV) plants is essential to ensure optimal performance. Drones can be employed to acquire both optical and thermal data for anomaly detection. However, while visual servoing can accurately guide a drone along individual PV panel rows, the inter-panel transitions between subsequent rows rely on the Global Navigation Satellite System (GNSS) and are therefore subject to larger errors in positioning accuracy. To address this problem, this paper presents a path-planning solution that minimizes the portion of the route dependent on GNSS navigation by formulating three variants of the Traveling Salesman Problem (TSP) and analyzing their impact on path length and inter-panel transitions in simulated models of actual PV plants.