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An optimized swinging door algorithm for wind power ramp event detection

机译:用于风电斜坡事件检测的优化平移门算法

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Significant wind power ramp events (WPREs) are those that influence the integration of wind power, and they are a concern to the continued reliable operation of the power grid. As wind power penetration has increased in recent years, so has the importance of wind power ramps. In this paper, an optimized swinging door algorithm (SDA) is developed to improve ramp detection performance. Wind power time series data are segmented by the original SDA, and then all significant ramps are detected and merged through a dynamic programming algorithm. An application of the optimized SDA is provided to ascertain the optimal parameter of the original SDA. Measured wind power data from the Electric Reliability Council of Texas (ERCOT) are used to evaluate the proposed optimized SDA. Results show that the proposed optimized SDA method provided better performance than the L1-Ramp Detect with Sliding Window (L1-SW) method but with significantly less (almost 1,400 seconds less) computational requirements, and it was also used as a baseline to determine the optimal value of the tunable parameter in the original SDA for ramp detection.
机译:重要的风电斜坡事件(WPRE)是影响风电整合的那些事件,它们关系到电网持续可靠的运行。近年来,随着风能渗透率的提高,风能斜坡的重要性也日益增加。本文提出了一种优化的平移门算法(SDA)以提高斜坡检测性能。风能时间序列数据由原始SDA进行分段,然后通过动态编程算法检测并合并所有重要的斜坡。提供了优化的SDA的应用程序,以确定原始SDA的最佳参数。来自得克萨斯州电力可靠性委员会(ERCOT)的测得风能数据用于评估建议的优化SDA。结果表明,所建议的优化SDA方法比具有滑动窗口的L1-Ramp检测(L1-SW)方法具有更好的性能,但计算需求却明显更少(几乎减少了1400秒),并且还被用作确定用于斜坡检测的原始SDA中可调参数的最佳值。

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