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Maximum entropy based probabilistic load flow calculation for power system integrated with wind power generation

机译:电力系统电力系统的最大基于熵的概率负荷流量

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Distributed generation including wind turbine (WT) and photovoltaic panel increased very fast in recent years around the world, challenging the conventional way of probabilistic load flow (PLF) calculation. Reliable and efficient PLF method is required to take into account such changing. This paper studies the maximum entropy probabilistic density function reconstruction method based on cumulant arithmetic of linearized load flow formulation, and then develops a maximum entropy based PLF (ME-PLF) calculation algorithm for power system integrated with wind power generation (WPG). Comparing to traditional Gram-Charlier expansion based PLF (GC-PLF) calculation method, the proposed ME-PLF calculation algorithm can obtain more reliable and accurate probabilistic density functions (PDFs) of bus voltages and branch flows in various WT parameter scenarios. It can solve the limitation of GC-PLF calculation method that mistakenly gaining negative values in tail regions of PDFs. Linear dependence between active and reactive power injections of WPG can also be effectively considered by the modified cumulant calculation framework. Accuracy and efficiency of the proposed approach are validated with some test systems. Uncertainties yielded by the wind speed variations, WT locations, power factor fluctuations are considered.
机译:近年来,在全球近年来,包括风力涡轮机(WT)和光伏面板在内的分布式发电,挑战了概率载荷流量(PLF)计算的传统方式。需要可靠和有效的PLF方法来考虑这种变化。本文研究了基于线性化载荷流制构的累积算法的最大熵概率密度函数重建方法,然后开发了一种基于熵的PLF(ME-PLF)计算算法,其与风力发电(WPG)集成。比较与传统的克查尔米尔扩展的PLF(GC-PLF)计算方法相比,所提出的ME-PLF计算算法可以在各种WT参数场景中获得总线电压和分支流的更可靠和准确的概率密度函数(PDF)。它可以解决GC-PLF计算方法的限制,在PDFS的尾部区域中误入歧应的负值。通过修改的累积计算框架也可以有效地考虑有效和无功功率注射之间的线性依赖性。提出方法的准确性和效率验证了一些测试系统。通过风速变化,WT位置,功率因数波动产生的不确定性。

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