A large number of nonlinear loads have an impact on the stable operation of the power system. To solve this problem, this article proposes a nonlinear load harmonic prediction method based on the architecture of Power Distribution Internet of Things. Firstly, this method integrates the characteristics of edge computing technology and Power Distribution Internet of Things technology and proposes a Power Distribution Internet of Things framework applied to nonlinear load harmonic prediction, which provides top-level design for subsequent harmonic prediction methods of Power Distribution Internet of Things; then, considering the electrical characteristics of the typical nonlinear load, the mathematical model of nonlinear load data is constructed based on the harmonic coupling admittance matrix model on the edge side. At the same time, a nonlinear load harmonic prediction model based on dynamic time warping and long-term and short-term memory network (DTW-LSTM) is established in the cloud computing center to realize high accuracy and high real-time prediction and analysis of nonlinear load harmonics. Finally, the simulation results based on the general data set show that the MAE evaluation index of the proposed method is less than 5% in the experimental group, which shows good generalization ability, and has some advantages over the current method in operation efficiency.

中文翻译：

---

基于配电物联网的非线性负荷谐波预测方法

大量的非线性负载会影响电力系统的稳定运行。为解决这一问题，本文提出了一种基于配电物联网架构的非线性负荷谐波预测方法。首先，该方法结合了边缘计算技术和配电物联网技术的特点，提出了一种用于非线性负荷谐波预测的配电物联网框架，为后续的配电网谐波预测方法提供了顶层设计。事物; 然后，考虑典型非线性负载的电气特性，基于边缘侧的谐波耦合导纳矩阵模型，建立了非线性负载数据的数学模型。同时，在云计算中心建立了基于动态时间规整和长期短期记忆网络（DTW-LSTM）的非线性负荷谐波预测模型，以实现高精度，高实时性的非线性负荷谐波预测和分析。最后，基于通用数据集的仿真结果表明，该方法在实验组中的MAE评估指标小于5％，具有良好的泛化能力，并且在操作效率方面优于现有方法。