Advanced AI for Integrated Financial Optimization of Wind Energy Assets

REOptimize Systems（REOS）是为了利用爱丁堡大学开发的研究而成立的，该研究采用了一种独特的方法来提高风力涡轮机的效率。通过先进的建模和新型机器学习技术的应用，这些算法最大限度地减少了系统中的端到端损失。这项技术正在申请专利，是爱丁堡大学7年研究的结果。该算法的成功已在小型风力涡轮机中得到实验证明，并发现能量捕获增加6%。根据具体的涡轮机和位置，能量捕获增加6%可以使运营商的净利润增加50%-100%。如果只有一半的英国涡轮机实现6%的收益，那么一年内将额外增加3000 GWhr的发电量并节省130万吨碳。这相当于从街道上减少约29万辆汽油乘用汽车。然而，这6%的增益仅在大约100 kW额定功率的中等规模风力中得到证明。预计更大的涡轮机将从更好的控制位置开始，这将使我们能够实现3%左右的增益。REOS目前正在准备一个试点项目，以验证兆瓦级西门子2.3-92涡轮机的技术，这是英国陆上船队的主力。现在，通过这个新项目，REOS将开发新的机器学习技术，并将其集成到一个单一的平台中，该平台将为风电资产提供端到端的财务优化，并对整个风力发电系统进行真正的全面了解。该项目将开发和集成：* 持续的每涡轮机设置优化 * 先进的假警报检测，以增加服务时间 * 先进的基于人工智能的风电场控制这将在风能资产的控制和性能方面产生阶跃变化，目的是保持大型现代风电场的能源输出增加6%。这将有助于创造可持续的创新，并帮助实现向净零的过渡。

REOptimize Systems (REOS), was formed to exploit research developed at University of Edinburgh, which has implemented a unique approach to increasing the efficiency of wind turbines. Through advanced modelling and the application of novel machine learning techniques the algorithms minimise the end-to-end losses in the system. This technique has patents pending and is the result of 7 years of research at The University of Edinburgh. The success of the algorithms has been proven experimentally in small-scale wind turbines, and found to yield increases in energy capture of 6%. A 6% increase in energy capture can drive net profit increases for the operator on the order of 50%-100% depending on the specific turbine and location. If only half of UK turbines achieved a 6% gain, it would result in an additional 3000 GWhr of generation and a saving of 1.3 million tonnes of carbon in a single year. This is equivalent to removing around 290,000 petrol passenger cars from the streets. However, this 6% gain has been proven only in medium-scale wind at approximately 100 kW ratings. It is expected that larger turbines will start from a position of better control which will allow us to achieve gains on the order of 3%. REOS is currently preparing a pilot project to validate the technique on a MW scale Siemens 2.3-92 turbine, which is a workhorse of the UK onshore fleet. Now, through this new project, REOS will develop and integrate novel machine learning technologies into a single platform which will provide end-to-end financial optimization of wind power assets, with a truly holistic view of the entire wind system. The project will develop and integrate: * Continuous per-turbine settings optimization * Advanced detection of false alarms to increase in-service time * Advanced AI-based wind farm control This will create a step-change in the control and performance of wind energy assets with the aim of maintaining the gain of 6% increase in energy output in large modern wind farms. This will contribute to creating sustainable innovation and help deliver the transition to net-zero.