一种基于环境信号的负荷建模的层次框架及其隐含的准凸性研究
A Hierarchical Framework for Ambient Signals based Load Modeling with Exploring the Hidden Quasi-convexity
摘要：
负荷建模是电力系统建模中的一个重要问题。为了更好地跟踪负荷模型的时变变化，近年来提出了基于环境信号的负荷建模方法。为了提高计算效率和模型结构的复杂性，本文提出了一种层次化的ASLM框架。通过该框架，首次探索了负荷建模问题的隐拟凸性，并可应用于更复杂的静态负荷模型结构。在上一阶段，将动载荷参数的识别视为一个优化问题。在下一阶段，通过对给定的一组动载荷参数进行线性回归，得到最优静载荷参数。然后，将回归残差作为上层优化问题的目标函数（OF）。广东电网的算例结果验证了该方法的有效性。结果表明，感应电动机模型变换后的模糊函数基本上是拟凸的，这为基于梯度的优化算法的应用提供了依据。算例分析结果表明，该方法在计算效率和模型结构复杂度方面均优于已有的ASLM方法。
英文摘要：
Load modeling is an important issue in modeling a power system. The approach of ambient signals-based load modeling (ASLM) was recently proposed to better track the time-varying changes of load models. To improve computation efficiency and model structure complexity, a hierarchical framework for ASLM is proposed in this paper. Through this framework, the hidden quasi-convexity of load modeling problem is explored for the first time, and more complicated static load model structures can be applied. In the upper stage, the identification of dynamic load parameters is regarded as an optimization problem. In the lower stage, the optimal static load parameters are obtained through linear regression for a given group of dynamic load parameters. Afterwards, the regression residuals are regarded as the objective function (OF) of the upper stage optimization problem. The proposed method is validated by the case study results in Guangdong Power Grid. The results have shown that the OF is mostly quasi-convex after the transformation of induction motor model, which provides the basis for the application of gradient-based optimization algorithm. The case study results also validate that the proposed approach has better computation efficiency and model structure complexity compared with the previous ASLM approach.
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