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首页> 外文会议>IEEE International Conference on Advanced Power System Automation and Protection >Assessment of Wind Power Ramp Events Based on Stacked Denoising Autoencoder
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Assessment of Wind Power Ramp Events Based on Stacked Denoising Autoencoder

机译：基于堆叠降噪自动编码器的风电匝道事件评估

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摘要

Wind power ramp events had a significant impact on the power balance of power system and may lead to load shedding. A data driven method was proposed for wind power ramp events assessment in this paper. The K-means clustering algorithm was used to divide the samples to several classes. The stacked denoising autoencoder was used to extract layer features to train support vector machine. Historical and forecast data of wind power, load power, conventional unit and pumped storage station power were taken as inputs. The output was whether ramp event occurred. A severity function was constructed to assess the severity grade which was predicted to be a wind power ramp event based on effect theory. The credibility of the assessment result was represented by confidence interval. Simulation results of a provincial power grid showed that the prediction method in this paper was more accurate and credibility was high enough to help the dispatchers to take measures for the security of power grid.
机译：风力发电斜坡事件对电力系统的功率平衡有重大影响，并可能导致负载减少。本文提出了一种数据驱动的风电斜坡事件评估方法。使用K均值聚类算法将样本分为几类。堆叠的去噪自动编码器用于提取图层特征以训练支持向量机。输入风能，负荷功率，常规机组和抽水蓄能电站功率的历史和预测数据作为输入。输出是是否发生斜坡事件。构造了严重程度函数以评估严重程度等级，该严重程度等级基于效应理论被预测为风力发电斜坡事件。评估结果的可信度以置信区间表示。某省级电网的仿真结果表明，本文的预测方法更加准确，可信度高，可以帮助调度员采取措施保障电网的安全。

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