基于特征融合与降维的K-Means++和Elman神经网络的低压台区线损计算方法

Low voltage substation line loss calculation method based on feature fusion and dimensionality reduction using K-Means++and Elman neural networks

摘要:

为了解决低压台区线损计算在理论上因线路复杂、用户众多以及数据获取困难等问题所带来的计算难度与精度不足，本研究提出了一种结合改进K-Means++算法与Elman神经网络的创新计算方法。研究初步深入分析了低压台区线损的决定因素，并依据相关性分析构建了线损的关键特征指标集。采纳主成分分析方法实施数据降维，简化数据结构。通过改进的K-Means++算法对数据集进行有效聚类，优化模型训练过程。同时，整合粒子群优化算法进一步提升Elman神经网络的性能。通过对实际数据进行仿真验证，结果证实所提出的方法在训练效率和计算精度方面表现优异。

Abstract:

In order to solve the theoretical difficulties and insufficient accuracy in calculating line loss in low-voltage substation areas due to complex transmission lines, numerous users, and difficulties in data acquisition, this study proposes an innovative calculation method that combines improved K-Means++algorithm with Elman neural network. The study conducted a preliminary in-depth analysis of the determining factors of line loss in low-voltage substations, and based on correlation analysis, constructed a set of key characteristic indicators for line loss. Adopting principal component analysis method to implement data dimensionality reduction and simplify data structure. Effectively cluster the dataset and optimize the model training process through an improved K-Means++algorithm. Meanwhile, integrating particle swarm optimization algorithms further enhances the performance of Elman neural networks. Through simulation verification of actual data, the results confirm that the proposed method performs excellently in terms of training efficiency and computational accuracy.