Electric microgrids require accurate dynamic models for operation, control, stability, and protection studies, then adequate load modeling plays an important role. This paper presents a two-stage adaptive approach to improve the generalization capability of load models obtained with the measurement-based modeling. The load model and their respective parameters are obtained through machine learning tools like decision trees (DTs) and optimization algorithms as ant colony (ACO). In the off-line stage of the proposed approach, several parameterized load models are optimally obtained using a database of microgrid disturbances. Then, the best model to represent each disturbance is defined using a similarity criterion. This model and the disturbance characteristics are integrated into a DT (classifier), while the characteristics and the model parameters are related in a second DT (predictor). These DTs are used in an on-line stage to swiftly determine the adequate parameterized load model in the case of a new disturbance in the microgrid. The approach’s performance is compared with the conventional measurement-based load modeling in a modified CIGRE benchmark low voltage microgrid. The results evidence the advantages of the proposed adaptive approach for dynamic load modeling.

中文翻译：

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微电网动态负荷建模的一种自适应方法

电力微电网需要精确的动态模型来进行运行、控制、稳定性和保护研究，因此适当的负载建模起着重要作用。本文提出了一种两阶段自适应方法，以提高通过基于测量的建模获得的负载模型的泛化能力。负载模型及其各自的参数是通过决策树（DTs）等机器学习工具和蚁群（ACO）优化算法获得的。在所提出的方法的离线阶段，使用微电网扰动数据库优化获得几个参数化负载模型。然后，使用相似性标准定义表示每个干扰的最佳模型。该模型和扰动特性被集成到一个 DT（分类器）中，而特征和模型参数在第二个 DT（预测器）中相关。这些 DT 用于在线阶段，以在微电网中出现新扰动的情况下快速确定适当的参数化负载模型。该方法的性能与改进的 CIGRE 基准低压微电网中传统的基于测量的负载建模进行了比较。结果证明了所提出的动态载荷建模自适应方法的优势。