| dc.description.abstract | While the development of human civilization has brought progress, it’s also caused global warming due to the large emissions of GHGs generated by our over-reliance on fossil fuels. This human-induced climate change not only will escalate the occurrence of extreme weather, but also may impact the ecology, causing serious problems such as species extinction or food crisis. Therefore, reducing GHG emissions has become an important strategy to control global warming. According to statistics data, electricity and heat production is the economic sector that produces the highest carbon emissions. As the demand for energy in today′s society will only increase, the methods of using renewable energy to replace traditional power generation have gradually been valued. Among these means, the rapidly growing solar power generation has become a trend in recent years. The solar panel that we often hear is the core equipment of photovoltaic power generation which is the most mainstream generation method in the solar energy market nowadays. A solar power plant consists of large areas of solar panels that convert sunlight into electricity. However, cracks, broken surface glass, hot spots, foreign objects, or stains on the solar panel may affect its generation and life, hence regular inspection is necessary to keep it in its best condition. Nevertheless, with the different types of solar panels coming out, the traditional maintenance method that relies on manual labor is time-consuming, dangerous, and difficult. Thus, drone technology, which has recently matured, has become a new way to inspect solar panels.
To sum up, this research focuses on the path planning for floating solar plant inspection by using drone. Discuss the processes and methods from how to convert problem in realistic situation, to what constraints should be considered to build the algorithm after obtaining the network. The must-be-passed points in the inspection task, the location of the recharging station, and the optimal path for the drone are the three main issues we aim at. For that, we established a standard process for the network conversion of the realistic situation to answer the first issue, and proposed an adjusted algorithm based on Genetic Algorithm which can fit the condition to solve this drone routing problem. After the algorithm, there′s a set of recommended flight paths for inspection task and the recommended optimal recharging station setting positions can be obtained, thus the second and third issues can be completed. Finally, this study considers the combination of three types of information, including the coverage area and distribution of the power plant site, the need for subsequent image analysis, and the use model of drone, and considers the overlap rate as the key to problem conversion. The Zhang Bin solar panel plant has successfully been converted into a network with 4,798 nodes. And under the condition that the minimum limit of Genetic Algorithm iterations set to 300, the algorithm starts from a good initial solution while ensuring the feasibility of the solution obtained in the process, converges into an acceptable path solution that takes 1,605 minutes within a limited iteration, and recommends a relatively stable recharging station position (987.76, 438.62).
Keywords: floating solar plant; solar photovoltaics; photovoltaic inspection; unmanned aerial vehicles (UAV); drone; drone routing problem. | en_US |