基于高斯混合聚类和改进条件变分自编码的多风电场功率日场景生成方法  

Daily Power Scenario Generation Method for Multiple Wind Farms Based on Gaussian Mixture Clustering and Improved Conditional Variational Autoencoder

作　　者：李丹[1] 梁云嫣 缪书唯 方泽仁 胡越 贺帅 LI Dan;LIANG Yunyan;MIAO Shuwei;FANG Zeren;HU Yue;HE Shuai(College of Electric and New Energy,China Three Gorges University,Yichang 443002,China;Hubei Key Laboratory of Cascaded Hydropower Stations Operation&Control,Yichang 443002,China;Hubei Provincial Collaborative Innovation Center for New Energy Microgrid,Yichang 443002,China)

机构地区：[1]三峡大学电气与新能源学院,湖北宜昌443002 [2]梯级水电站运行与控制湖北省重点实验室,湖北宜昌443002 [3]新能源微电网湖北省协同创新中心,湖北宜昌443002

出　　处：《中国电力》2024年第12期17-29,共13页Electric Power

基　　金：国家自然科学基金资助项目(51807109)。

摘　　要：大量出力不确定的风电场并入电网会带来运行隐患和不可控风险,基于变分自编码器的场景生成模型方法能生成确定性场景集合以描述风电出力的不确定性。针对多风电场出力复杂的时空相关性以及在传统变分自编码器模型训练过程中可能存在的“KL坍缩”等问题,提出一种基于高斯混合聚类和改进条件变分自编码器的多风电场时空功率日场景生成方法。通过引入二维卷积技术提取时空相关性进行降维,并采用最大化最小夹角独立正则化技术,强化隐特征的独立性;采用超球面分布替代高斯分布,避免模型出现“KL坍缩”,提高模型场景生成训练的稳定性和准确性;另外,进一步考虑多风电场功率日场景的多样性和灵活性,引入高斯混合聚类技术,使模型可根据特定的条件标签生成具有差异化特征的确定性场景集。实际算例的结果表明,相较于常见方法,所提方法累积概率分布误差下降了17%~71%,时空相关性平均误差分别下降了85%~97%和55%~91%,且能精准生成不同风况类别占比的多风电场功率日场景集,提高了场景生成的多样性和灵活性。The integration of a large number of wind farms with uncertain output into the power grid will bring potential hazards in operation and uncontrollable risks.The uncertainty of wind power output is described by uncertain scenario sets generated from the variational autoencoder-based scenario generation method.Aimed at the complex spatiotemporal correlation of multi-wind farm output and the possible"KL collapse"during the traditional variational autoencoder model training,this paper proposes a daily scenario generation method of spatiotemporal power based on the Gaussian mixture model and improved conditional variational autoencoding.The two-dimensional convolution technique is introduced to extract the spatiotemporal correlation for dimension reduction,and the maximizing min-angle regularization technique is used to strengthen the independence of latent features.Hyperspherical distribution,instead of Gaussian distribution,is used to avoid"KL collapse"and improve the stability and accuracy of scene generation training.In addition,considering the diversity and flexibility of daily power scenarios of multi-wind farm,Gaussian mixture clustering technology is introduced to generate uncertain scenario sets with differentiated and changing characteristics,enabling the generation of certain scenario sets with varied characteristics according to specific condition labels.The results of real examples show that compared with conventional methods,the proposed method reduces the accumulated error distribution of probability by 17%to 71%,and the average error of temporal and spatial correlation by 85%to 97%and 55%to 91%,respectively.Besides,the proposed method can accurately generate daily power scenario sets of multi-wind farm in different wind conditions,improving scene generation's diversity and flexibility.

关 键 词：风电场景生成 高斯混合模型 特征提取 条件变分自编码器 超球面分布 正则化技术 

分 类 号：TM614[电气工程—电力系统及自动化]