| 采用混合高斯模型及边缘变换技术的蒙特卡洛随机潮流方法 |
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| 引用本文： | 徐青山,黄煜,刘建坤,卫鹏.采用混合高斯模型及边缘变换技术的蒙特卡洛随机潮流方法[J].电力系统自动化,2016,40(16):23-30. |
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| 作者姓名： | 徐青山 黄煜 刘建坤 卫鹏 |
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| 作者单位： | 东南大学电气工程学院, 江苏省南京市 210096,东南大学电气工程学院, 江苏省南京市 210096,国网江苏省电力公司电力科学研究院, 江苏省南京市 210003,国网江苏省电力公司电力科学研究院, 江苏省南京市 210003 |
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| 基金项目： | 国家自然科学基金资助项目(51377021);中央高校基本科研业务费专项资金资助项目(2242016K41064);国家电网公司科技项目“新能源发电预测误差对电网安全运行影响评价方法研究” |
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| 摘 要： | 提出一种计及输入变量相关性的改进蒙特卡洛随机潮流方法。该方法针对系统输入变量多样性和随机性的特点,建立其混合高斯模型并根据测量数据进行参数估计。在蒙特卡洛仿真基础上,引入均匀设计抽样技术提高采样效率,并通过边缘变换和Cholesky分解产生具有相关性的输入变量样本。采用多重线性化手段在加快计算速度的同时减小潮流方程线性化引起的截断误差。对IEEE 30和IEEE 118节点系统的测试分析,验证了所提方法的准确性、快速性和有效性。
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| 关 键 词： | 随机潮流 混合高斯模型 相关性 均匀设计抽样 多重线性化 |
| 收稿时间： | 2015/12/7 0:00:00 |
| 修稿时间： | 5/5/2016 12:00:00 AM |
Probabilistic Load Flow Method Using Monte Carlo Simulation Based on Gaussian Mixture Model and Marginal Transformation |
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| XU Qingshan,HUANG Yu,LIU Jiankun and WEI Peng.Probabilistic Load Flow Method Using Monte Carlo Simulation Based on Gaussian Mixture Model and Marginal Transformation[J].Automation of Electric Power Systems,2016,40(16):23-30. |
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| Authors: | XU Qingshan HUANG Yu LIU Jiankun and WEI Peng |
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| Affiliation: | School of Electrical Engineering, Southeast University, Nanjing 210096, China,School of Electrical Engineering, Southeast University, Nanjing 210096, China,State Grid Jiangsu Electric Power Research Institute, Nanjing 210003, China and State Grid Jiangsu Electric Power Research Institute, Nanjing 210003, China |
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| Abstract: | A probabilistic load flow method considering the correlation between input variables based on improved Monte Carlo simulation(MCS)is proposed. Regarding the behaviors of diversity and randomness for variable input, the method establishes the Gaussian mixture model(GMM)for variable input and performs parameter estimation by the use of measured data. Uniform design sampling(UDS)is introduced to improve the sampling efficiency, and correlated samples are generated by marginal transformation and Cholesky decomposition. Moreover, multi-linearization is applied to reduce the truncated error as well as time consumption. The simulation results of IEEE 30-bus and IEEE 118-bus test system verify the effectiveness, accuracy and practicability of the proposed method. This work is supported by National Natural Science Foundation of China(No. 51377021), Fundamental Research Funds for the Central Universities(No. 2242016K41064)and State Grid Corporation of China. |
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| Keywords: | probabilistic load flow Gaussian mixture model correlation uniform design sampling multi-linearization |
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