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This division is based on the way of prediction with consideration of wind's uncertainty. Aiming at ramp prediction, methods are generally divided as deterministic and probabilistic forecasting. In the past decade, prediction of wind power ramps is kept as an area of active research, and quantities of ramp researches are reported in literature, including ramp definition, prediction, classification, detection. To mitigate the harm of ramp events to power grid, system operators have been looking for and developing an effective ramp events prediction system. Nowadays, these harmful events are defined as wind power ramp events which have characteristics of large power change within a relatively short time interval, and they negatively impact the safety and stability of the electricity power grid. From then on, similar events have also been reported in Europe, China and other countries, and been paid attention by power system operators. For example, in 2008 an unexpected wind power down-ramp event occurred in Texas, causing a serious economic loss to the Electric Reliability Council of Texas (ECROT).
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Enjoying the benefits of clear wind energy integrated in power grid, a large fluctuation caused by wind may lead to a potential disaster, especially in large-capacity wind farms. Generally, interests accompany with risks. Due to this purpose, large-scale and high-integration wind farms are being developed during recent years around the world. Comprehensive results validated the feasibility and superiority of the proposed ramp prediction approach.Ĭonsidering the instability and uncertainty of power from individual wind turbines, large wind farms with high ensemble could provide more reliable wind energy to power systems.
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Performance of both the proposed long-term wind power prediction and the corresponding ramp prediction are computed and compared with conventional models on an actual wind dataset. At the second stage, a modified swinging door algorithm is applied for ramp detection. Then, a multivariate model is built with a data-mining algorithm to correct system errors of the primary prediction, which is addressed to improve long-term prediction performance. By using wind power curve to reflect the physic mechanism of wind power generation, data from numerical weather prediction system could be used to realise long-term trend prediction.
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Considering high-performance ramp prediction requires long-term and accurate wind power prediction results this study also proposes a hybrid prediction model at the first stage. This approach contains two successive stages of work, including wind power forecasting and ramp detection. To mitigate the threat to power system caused by ramp events – large wind power fluctuation, this study proposes an advanced ramp prediction approach based on event detection framework. IET Generation, Transmission & Distribution.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology.