刘科研1,吕 琛1,葛磊蛟2,朱新山2.基于最小二乘法的光伏电源电磁暂态输出预测[J].湖南大学学报:自然科学版,2019,(8):81~90
基于最小二乘法的光伏电源电磁暂态输出预测
Least Square Based Prediction for the Transient Output of Solar Power Source
  
DOI:
中文关键词:  光伏电源  暂态模型  预测  最小二乘法
英文关键词:solar power source  transient model  prediction  least-square
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作者单位
刘科研1,吕 琛1,葛磊蛟2,朱新山2 (1.中国电力科学研究院有限公司北京 100192 2.天津大学 电气自动化与信息工程学院天津 300072) 
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中文摘要:
      光伏电源的暂态输出预测对电网稳定性分析、电能质量控制与故障诊断等有重要意义.为此,本文首先建立理想光伏电源的离散化模型与线性预测模型.然后,对于电源模型参数固定的情况,给出了基于正则化最小二乘法的预测方案.对电源模型参数变化的情况,采用递推最小二乘法获得实时更新的预测模型参数.与标准递推最小二乘法不同,该方案采用了基于滑动矩形窗的数据更新策略,可提升RLS的跟踪性能与预测精度.实验结果表明,提出的预测方案获得了良好的预测精度,而且能够很好地适应电源模型参数发生变化的情况.
英文摘要:
      The transient output prediction of solar power source is of great significance for power grid stability analysis, power quality control and fault diagnosis. To this goal, the discrete model and linear prediction model of ideal solar power source were established. Then, a regularized least square prediction scheme was proposed to estimate the unchanged model parameters. When the power source model parameters vary, the prediction model parameters are continuously updated in real-time by the Sliding Rectangle Window (SRW) Recursive Least Square (RLS) method. Unlike the standard RLS, SRW-RLS adopts a data update strategy based on sliding rectangle window, which improves the tracking performance and prediction accuracy. The experimental results show that the proposed prediction schemes achieve good prediction accuracy and SRW-RLS is able to adapt well to the changes in the parameters of power source model.
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