首页|基于改进信息最大化生成对抗网络的风光出力场景可控生成方法

基于改进信息最大化生成对抗网络的风光出力场景可控生成方法

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基于深度学习的场景生成方法能够自适应挖掘历史数据中高维非线性特征,在风光出力的不确定性建模中得到了广泛应用.然而,基于深度学习的场景生成方法多为黑盒模型,存在可解释性差、生成不可控等问题.为此,提出了一种基于改进信息最大化生成对抗网络(information maximizing generative adversarial nets,InfoGAN)的风光出力场景生成方法.该方法在目标函数中增加了基于互信息的正则化项,最大化控制编码与生成场景之间的互信息,无监督学习控制编码与生成场景统计特征的映射关系,并引入Gumbel-Softmax分布提高了生成场景的质量.结合风电场和光伏电站的真实数据进行了算例分析,算例结果表明,所提方法不仅能准确描述风光出力不确定性,而且具有可解释性,能够可控生成指定风光出力场景.
Controllable Scenario Generation Method for Wind Power and Photovoltaic Output Based on Improved InfoGAN
The scenario generation method based on deep learning can adaptively capture the high-dimensional nonlinear features in historical data and has been widely used in the uncertainty modeling of wind power and photovoltaic output.However,most scenario-generation methods based on deep learning are black-box models,which have problems such as poor interpretability and uncontrollable generation.Therefore,a scenario set constructing method of wind power and photovoltaic output based on improved information maximization generation adversarial network(InfoGAN)is proposed.In this method,the mutual information regularization term is added to the objective function to maximize the mutual information between the control coding and the generation scenario,unsupervised learning controls the mapping relationship between the coding and the generation scenario statistical features,and Gumbel-Softmax distribution is introduced to improve the quality of the generation scenario.The results show that the proposed method can not only accurately describe the uncertainty of wind power and photovoltaic output but also be interpretable and generate the specified wind power and photovoltaic output scenario.

scenario generationwind power and photovoltaic outputinterpretabilityinformation maximization generation adversarial networkGumbel-Softmax distributioncontrollable generation

陈凡、陈刘明、王曼、徐鸿琪、周小雨

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南京工程学院电力工程学院,江苏省南京市 211167

智能电网保护和运行控制国家重点实验室,江苏省 南京市 211106

场景生成 风光出力 可解释性 信息最大化生成对抗网络 Gumbel-Softmax分布 可控生成

智能电网保护和运行控制国家重点实验室项目南京工程学院大学生科技创新项目南京工程学院大学生科技创新项目

SGTYHT/20-JS-221TB202317057TB202317055

2024

10.13335/j.1000-3673.pst.2023.1285

电网技术
国家电网公司

电网技术

CSTPCD北大核心
影响因子：2.821
ISSN：1000-3673
年,卷(期)：2024.48(4)
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