| 含非正态分布概率潮流计算的改进型两点估计法 |
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| 引用本文： | 车玉龙,吕晓琴,王晓茹,韩润. 含非正态分布概率潮流计算的改进型两点估计法[J]. 电力自动化设备, 2019, 39(12) |
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| 作者姓名： | 车玉龙 吕晓琴 王晓茹 韩润 |
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| 作者单位： | 兰州交通大学自动化与电气工程学院,甘肃兰州730070;西南交通大学电气工程学院,四川成都611756;西南交通大学电气工程学院,四川成都,611756;国网银川供电公司,宁夏银川,750004 |
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| 基金项目： | 兰州交通大学青年基金资助项目(2016022)；甘肃省高等学校创新能力提升项目(2019B-049) |
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| 摘 要： | 采用两点估计法进行考虑不确定性电力系统的概率潮流计算时容易产生较大的误差。提出一种可用于计算含服从非正态分布负荷的概率潮流问题的改进型两点估计法。该方法通过增加均一概率的估计点进行额外计算,不需要进行正态变换和估计高阶矩,其计算精度高于两点估计法。采用含服从非正态分布负荷的5节点系统和IEEE 14节点系统作为算例,与蒙特卡洛仿真的结果比较验证了提出的改进型两点估计法的准确性。
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| 关 键 词： | 概率潮流 蒙特卡洛仿真 点估计法 非正态分布 |
| 收稿时间： | 2019-04-22 |
| 修稿时间： | 2019-10-16 |
Improved two point estimation method for probabilistic power flow calculation with non-normal distribution |
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| Affiliation: | School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China,School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China,School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China and State Grid Yinchuan Power Supply Company, Yinchuan 750004, China |
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| Abstract: | Large error is easily generated when the two point estimation method is applied to calculate the probabilistic power flow of power system considering uncertainties. An I2PEM(Improved Two Point Estimation Method) is proposed, which can be used to calculate the probabilistic power flow problem with loads subjected to non-normal distribution. The method performs additional calculation by adding the estimation points with uniform probability, without the need of normal transformation and estimation of higher-order moments, and its calculation accuracy is higher than that of the two point estimation method. Taking 5-bus system and IEEE 14-bus system with non-normal distributed loads as examples, the accuracy of the proposed method is verified by comparing with the results of Monte Carlo simulation. |
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| Keywords: | probabilistic power flow Monte Carlo simulation point estimation method non-normal distribution |
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