Artificial Intelligence and Automated Monitoring of Marine Wind Power and Ecosystems

Abstract

 At present, there is a lack of intelligent means to monitor the impact of marine wind power on ecosystems, and there is an urgent need to develop artificial intelligence-assisted monitoring technology. This paper discusses the application of artificial intelligence in marine wind power and ecosystem monitoring, focusing on technologies such as intelligent sensing, data collection, deep learning-driven monitoring models, intelligent decision-making and adaptive control systems. Marine sensor network optimization and anti-jamming underwater communication protocols based on edge computing improve the stability and reliability of data acquisition. This research paper focuses on developing and applying advanced artificial intelligence technologies for the intelligent monitoring of marine wind power and ecosystems. The novelty lies in the integration of multiple AI - enabled techniques, such as intelligent sensing, deep - learning - driven monitoring models, and intelligent decision - making systems, to address the complex challenges in real - time monitoring, fault diagnosis, and ecological impact assessment of marine wind farms. The wind farm can generate 870,000 kilowatt-hours of electricity annually, with an energy conversion efficiency of 16.3%. Although the conversion efficiency is relatively low, it has shown outstanding results in ecological protection. Through intelligent monitoring equipment, the protected area of marine life habitats has increased by 55%, while the survival rate of endangered species has been dramatically improved from the original 33% to 72.1%. These results were obtained through extensive experiments conducted at an actual marine wind farm equipped with our intelligent monitoring system. Specifically, the 42.5 MW wind turbines and automatic monitoring systems were installed in the test area, and data was collected continuously over a period of 12 months. The collected data was then analyzed using the methods described in the following sections to obtain the presented numerical results, such as the energy conversion efficiency, the increase in the protected area of marine life habitats, and the improvement in the survival rate of endangered species.