Abstract The aim of this paper is to compare voltage and system loading mapping capabilities of a variety of regression algorithms, such as Adaptive Network based Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Decision Tree (DT). A voltage sensitivity matrix is generated from the power flow Jacobian matrix for a loading scenario near the unstable point. Principal Component Analysis (PCA) is used to separate the system, close to the critical point, in order to group the buses into coherent voltage controlling areas. For different reactive power injection scenarios, we have different bus voltages that can be mapped by the aforementioned regression algorithms. The algorithms are trained with limited amounts of data, in order to establish a fair comparison between them. The present work shows that ANFIS and KNN have a better performance in critical voltage and load prediction when compared to the rest. The academic IEEE 14 and 118 bus systems are employed with all its limits considered, so the results may be reproduced.

中文翻译：

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与光伏总线最大无功功率相关的临界总线电压和负载映射的机器学习回归模型的比较

摘要 本文的目的是比较各种回归算法的电压和系统负载映射能力，例如基于自适应网络的模糊推理系统 (ANFIS)、人工神经网络 (ANN)、K-最近邻 (KNN)、支持向量回归 (SVR) 和决策树 (DT)。对于不稳定点附近的负载情况，从潮流雅可比矩阵生成电压灵敏度矩阵。主成分分析 (PCA) 用于在靠近临界点的位置分离系统，以便将总线分组为相干的电压控制区域。对于不同的无功功率注入场景，我们有不同的母线电压，可以通过上述回归算法进行映射。这些算法是用有限数量的数据训练的，以便在它们之间建立公平的比较。目前的工作表明，与其他方法相比，ANFIS 和 KNN 在临界电压和负载预测方面具有更好的性能。采用学术 IEEE 14 和 118 总线系统并考虑其所有限制，因此可以重现结果。