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A Probabilistic Load Flow Method based on Improved Point Estimate and Maximum Entropy

机译:一种基于改进点估计和最大熵的概率负载流法

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摘要

New devices such as wind turbine and photovoltaic connecting to the power grid brings uncertain features to the modern power system. The uncertain factors have a significant impact on the operating state and harmonic level of power system. To analyze the influence of the uncertain factors, a novel probabilistic power flow method combined the improved point estimate and maximum entropy theory is proposed in this paper. To relieve the calculation burden, the estimated points are first calculated in the standard normal distribution space. Through the transformation from standard normal probability distribution space to the original probability distribution space, the raw moments can be easily calculated. Then according to the raw moments of output variables, the probability distribution of output variables can be reconstructed through maximum entropy theory. The proposed method can effectively analyze the fundamental and harmonic state of the power system. The superiority of the proposed method is validated by the simulation with the IEEE 33 bus distribution system.
机译:诸如风力涡轮机和光伏连接到电网的新设备将不确定的功能带来了现代电力系统。不确定因素对电力系统的运行状态和谐波水平产生了重大影响。为了分析不确定因素的影响,本文提出了一种新的概率功率流法组合改进的点估计和最大熵理论。为了减轻计算负担,首先在标准正态分布空间中计算估计点。通过从标准正常概率分布空间转换到原始概率分布空间,可以容易地计算原始矩。然后根据输出变量的原始矩,可以通过最大熵理论重建输出变量的概率分布。所提出的方法可以有效地分析电力系统的基本和谐波状态。通过IEEE 33总线分配系统的模拟验证了所提出的方法的优越性。

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