%0 Journal Article %T 基于WCGAN与自注意力机制的光伏场景生成方法
A Photovoltaic Scenario Generation Method Based on WCGAN and Self-Attention Mechanisms %A 贺誉 %A 江聪美 %A 张良恒 %J Modeling and Simulation %P 11-19 %@ 2324-870X %D 2025 %I Hans Publishing %R 10.12677/mos.2025.141002 %X 以风能、光伏等清洁能源为主体的可再生能源在电力系统中的渗透率不断提高。电力系统中的可再生能源出力呈现出高度间歇性、随机性和波动性特征，这些特性将对电力系统的稳定运行产生重大的影响。同时高渗透率下的可再生能源出力也将对电力系统的调度优化控制提出更高的要求。如何精准刻画可再生能源出力特性成为了当前研究的热点，本文将Wasserstein条件生成对抗网络(WCGAN)与自注意力机制技术(SA)相结合，利用WCGAN强大的生成能力与SA数据增强技术在小数据样本训练时的优秀性能，学习可再生能源历史数据未知分布，生成符合观测数据分布规律的新样本，准确表征可再生能源出力的不确定性。经过对比实验，本文提出的方法在小样本数据上具有更佳的生成质量。
The penetration rate of renewable energy, mainly composed of clean energy such as wind and photovoltaic, in the power system is constantly increasing. The renewable energy output in the power system exhibits highly intermittent, stochastic, and fluctuating characteristics, which will have a significant impact on the stable operation of the power system. At the same time, the output of renewable energy under high penetration rates will also pose higher requirements for the optimization and control of power system scheduling. How to accurately characterize the output characteristics of renewable energy has become a hot topic in current research. This article will combine Wasserstein Conditional Generative Adversarial Network (WCGAN) with self-attention mechanisms data augmentation technology, utilizing the powerful generation ability of WCGAN and the excellent performance of self-attention mechanisms data augmentation in small data training to learn the unknown distribution of renewable energy historical data and generate new samples that conform to the distribution rules of observed data, accurately characterize the uncertainty of renewable energy output. After comparative experiments, the method proposed in this article has better generation quality on small sample data. %K 场景生成， %K 可再生能源， %K 生成对抗网络， %K 数据增强， %K 自注意力机制， %K 小样本训练
Scenario Generation %K Renewable Energy %K Generative Adversarial Networks %K Data Augmentation %K Self-Attention Mechanisms %K Small Sample Training %U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=104375