基于Copula理论和深度集成学习的快速概率TTC评估

Shaopeng Zhang, Jiongcheng Yan, Xin Li, Dong Yang, Huan Ma, Changgang Li
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引用次数: 0

摘要

风电并网使电力系统的不确定性急剧增加,给总输电能力(TTC)的计算带来新的挑战。提出了一种基于Copula和深度集成学习的概率TTC快速评估方法。考虑到地理距离较近的风电场风电输出的不确定性和风速的相关性,采用Copula函数生成情景。采用堆叠去噪自动编码器(SDAE)直接从源数据中提取特征。选择支持向量回归(SVR)作为回归量,采用bagging集成策略进一步提高TTC估计的精度。以简化后的山东电网为例,验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast Probabilistic TTC Assessment Based on Copula Theory and Deep Ensemble Learning
Wind power integration makes the uncertainty of power system increasing dramatically, which brings new challenges to the calculation of Total Transfer Capability (TTC). A fast assessment method for probabilistic TTC based on Copula and deep ensemble learning is proposed. Considering wind power output uncertainties and correlation of wind speed in geographical close wind farms, Copula function is adopted to generate scenarios. Stacked Denoising Autoencoder (SDAE) is used to extract features directly from source data. Support Vector Regression (SVR) is selected as regressor and bagging ensemble strategy is applied to further improve the accuracy of the TTC estimation. The case study in simplified Shandong grid validates the effectiveness of the proposed method.
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