Wind-Solar-Load Power Scenario Reduction Based on Residual Multi-Channel Convolutional Auto-Encoders

Yang Cao, Haifeng Huang, Hong Zhang, Xiaolu Li, Yuzheng Peng, Xinran Li
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Abstract

The traditional methods of generating typical scenarios are prone to information loss and feature extraction inaccuracy. Considering the characteristics of wind-solar-load data with physical individual independence, complex complementary coupling, and the quality of data dimensionality reduction, a method of generating typical wind-solar-load scenarios based on the residual multi-channel convolutional auto-encoder (RESMCAE) is proposed. With respect to data dimensionality reduction, each independent feature extraction encoder is improved by the residual module to solve the problem of gradient disappearance, reduce information loss and ensure the information integrity. The performance of the network is enhanced through integrating multi-resolution features by the decoder of multi-layer fusion structure. The KL divergence is used for optimizing the network structure, and the Adam algorithm for parameter tuning. The proposed method is verified by comparing with traditional clustering methods on Davies-Bouldin Index (DBI) and Calinski-Harabasz Index (CHI) scores. The results show that the RESMCAE is feasible.
基于残差多通道卷积自编码器的风能-太阳能负荷场景缩减
传统的典型场景生成方法容易存在信息丢失和特征提取不准确的问题。针对风电负荷数据具有物理个体独立性、复杂互补耦合和数据降维质量的特点,提出了一种基于残差多通道卷积自编码器(RESMCAE)的典型风电负荷场景生成方法。在数据降维方面,通过残差模块对每个独立的特征提取编码器进行改进,解决梯度消失问题,减少信息损失,保证信息完整性。通过多层融合结构的解码器集成多分辨率特征,提高了网络的性能。使用KL散度优化网络结构,使用Adam算法进行参数整定。通过对Davies-Bouldin指数(DBI)和Calinski-Harabasz指数(CHI)得分与传统聚类方法的比较,验证了该方法的有效性。结果表明,该方法是可行的。
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