RoboPV: An integrated software package for autonomous aerial monitoring of large scale PV plants

Abstract

In this paper, a novel software package, called RoboPV, is introduced for autonomous aerial monitoring of PV plants. RoboPV automatically performs aerial monitoring of PV plants, from optimal trajectory planning to image processing and pattern recognition for real-time fault detection and analysis. RoboPV consists of four integrated components: boundary area detection, path planning, dynamic processing, and fault detection. To design an optimal flight path, aerial images of PV plants, which have been collected from experimental flights, are given as inputs to a developed encoder-decoder deep learning architecture to extract boundary points of PV plants automatically. Then, a novel path planning algorithm is conducted by RoboPV to design an optimal flight path with full coverage of whole regions of the PV plant. Aerial images are analysed in real-time during the flight by a high precise neural network trained for automatic fault detection. In this study, several decision-making and maneuver algorithms were developed for various real-world flight conditions to improve the performance of RoboPV during an autonomous aerial inspection. RoboPV is a modular processing library that can be installed on any micro-computer processor with a low computational power. Moreover, supporting the MAVLink communication protocol enables RoboPV to connect with an intelligent Pixhawk flight autopilot and navigate a wide range of multi-rotors. To demonstrate the performance of RoboPV, a six degrees of freedom dynamic model of a multi-rotor is developed in a SIMULINK environment with a defined aerial monitoring mission on three different real megawatt-scale PV plants. The results prove that RoboPV can execute the autonomous aerial inspection with an overall accuracy of 93% for large-scale PV plants.