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架空导线载流量的多时段联合概率密度预测
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作者单位:

1. 电网智能化调度与控制教育部重点实验室(山东大学), 山东省济南市 250061; 2. 国网山东省电力公司济宁供电公司, 山东省济宁市 272100; 3. 济南大学自动化与电气工程学院, 山东省济南市 250022; 4. 国网山东省电力公司电力科学研究院, 山东省济南市 250003

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基金项目:

山东省重点研发计划资助项目(GG201809140209);国家自然科学基金资助项目(51407111)


Multi-period Joint Probability Density Forecasting for Thermal Rating of Overhead Line
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Affiliation:

1. Key Laboratory of Power System Intelligent Dispatch and Control, Ministry of Education(Shandong University), Jinan 250061, China;2. Jining Power Supply Company, State Grid Shandong Electric Power Company, Jining 272100, China;3. School of Electrical Engineering, Jinan University, Jinan 250022, China;4. Electric Power Research Institute of State Grid Shandong Electric Power Company, Jinan 250003, China

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    摘要:

    受微气象环境影响,架空线路载流量波动性较强,难以被准确预测,掌握线路关键线挡载流量的分布规律对帮助运行人员把握线路未来载流量变化,充分利用架空线路载荷能力具有重要参考价值。文中基于架空线路关键线挡微气象历史数据,在分析载流量变化特性的基础上,结合分位点回归方法,首先进行载流量逐时段概率预测,而后进一步运用t-Copula函数评估多时段载流量概率分布的相关特性,建立未来多时段载流量动态相依模型,实现架空线路关键线挡载流量的多时段联合概率密度预测,得到较逐时段概率预测更为准确的载流量波动区间和分布信息。实例分析表明,所提方法可利用载流量时段间的关联性改善逐时段概率预测结果,有效缩小载流量预测结果的分布区间。

    Abstract:

    Influenced by the micrometeorological conditions around the overhead line, the thermal ratings of overhead line have strong volatility and are difficult to predict accurately. It is of significance for system operators to grasp the fluctuation range and distribution characteristics of the thermal rating of critical spans along the overhead line, thus guiding the operators to exploit the transfer capability of overhead lines. Based on the historical micrometeorological data of critical spans and the variation characteristics analysis of thermal rating, the quantile regression method is employed to forecast the period-by-period probability of thermal rating. Then the t-Copula function is used to evaluate the correlation characteristics of the probability distributions of multi-period thermal ratings. A dynamic dependence model for multi-period thermal rating is established to realize the joint probability density forecasting for multi-period thermal rating. Meanwhile, the more accurate fluctuation interval and distribution information of thermal rating are obtained. The case studies show that the proposed method can improve the period-by-period probability forecasting results by using the correlation of thermal rating between periods, and effectively reduce the distribution interval of thermal rating forecasting results.

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引用本文

付善强,王孟夏,杨明,等.架空导线载流量的多时段联合概率密度预测[J].电力系统自动化,2019,43(17):102-108. DOI:10.7500/AEPS20180928004.
FU Shanqiang, WANG Mengxia, YANG Ming,et al.Multi-period Joint Probability Density Forecasting for Thermal Rating of Overhead Line[J].Automation of Electric Power Systems,2019,43(17):102-108. DOI:10.7500/AEPS20180928004.

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历史
  • 收稿日期:2018-09-28
  • 最后修改日期:2019-06-26
  • 录用日期:2019-04-24
  • 在线发布日期: 2019-06-11
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