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含逆变型分布式电源的负荷模型构建及参数辨识方法

Load Model Construction and Parameter Identification Method of Inverter-Interfaced Distributed Generator

  • 摘要:
      目的  分布式电源(DG)对电网负荷侧的运行特性具有重要影响,然而当前研究侧重于单一类型的分布式电源特性分析和建模,针对能够对具有一定相似性的DG进行统一描述的通用模型研究较少。
      方法  文章对1种逆变型分布式电源(IIDG)模型进行搭建,并采用1种新颖的优化算法。首先基于对IIDG特点及其共有特性的分析,搭建了含IIDG的综合负荷模型;根据所搭建的统一模型进行分析计算,推导得到逆变型分布式电源系统的状态微分方程,系统模型的输出方程。为通过连续迭代和开关函数更新计算系统各时刻的状态和响应,给出系统的状态初值的计算公式。结合样本数据,给出统一模型辨识流程。最后采用白鲨优化算法(WSO)对模型进行参数辨识;最后考虑电压跌落扰动情况,通过对该系统模型进行采样分析。
      结果  仿真结果表明,所提出的统一模型在不同程度的电压跌落的情况下,均能够较好地反映IIDG特性。
      结论  建模误差与参数辨识结果表明所提出的统一模型具有良好的自描述能力及参数稳定性。

     

    Abstract:
      Introduction  Distributed generation (DG) has a significant impact on the operational characteristics of the load side of the power grid. However, current research focuses mainly on analyzing and modeling the characteristics of a single type of distributed generation, and there is limited research on general models that can uniformly describe DGs with certain similarities.
      Method  This article constructed an inverter-interfaced distributed generator (IIDG) model and employed a novel optimization algorithm. Firstly, based on the analysis of the characteristics and common features of IIDG, a comprehensive load model containing IIDG was constructed; According to the unified model, analytical calculations were performed to derive the state differential equation of the IIDG system and the output equation of the system model. To calculate the state and response of the system at each moment through continuous iteration and switch function updates, a formula for calculating the initial value of the system's state was provided. Combined with the sample data, the unified model identification process was given. Finally, the white shark optimizer (WSO) algorithm was employed to identify the parameters of the model; considering the voltage drop disturbance, the system model was sampled and analyzed.
      Result  The simulation results show that the proposed unified model can better reflect the IIDG characteristics in the case of different levels of voltage drop.
      Conclusion  The modeling error and parameter identification results show that the proposed unified model has good self-describing ability and parameter stability.

     

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