PROBABILISTIC FLOW CALCULATION OF POWER SYSTEM CONSIDERING WIND POWER BASED ON SPARSE POLYNOMIAL CHAOS EXPANSION WITH PARTIAL LEAST SQUARES METHOD

被引：0
|
作者：
Dong X. [1 ,2 ]
Liang C. [1 ,2 ]
Ma X. [1 ,2 ]
Li Y. [1 ,2 ]
Yang J. [1 ,2 ]
机构：
[1] State Grid Gansu Electric Power Co., Electric Power Research Institute, Lanzhou
[2] Power Grid Loss Reduction and Energy Saving Technology Laboratory of State Grid, Lanzhou
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 06期
关键词：
distributed generation; partial least squares regression; polynomial chaotic expansion; probabilistic power flow; random response surface; wind power;
D O I：
10.19912/j.0254-0096.tynxb.2022-0073
中图分类号：
学科分类号：
摘要：
Considering the uncertainty of new environmental protection and green energy such as wind power or photovoltaic power generation，the traditional deterministic calculation method of power flows is difficult to comprehensively reflect the operation of the power system. For the computational amount of traditional Monte Carlo probability power flows algorithm, combining the partial least squares regression algorithm and the multi-class proxy model，this paper proposes a probability power flow algorithm for partial least squarer polynomial sparse expansion. Using the pseudo-cross-correction error adaptive mechanism of the bias minimum square regression algorithm，this paper contributes larger polynomial to the polynomial development，and obtains a sparse expression of the polynomial expansion，overcomes the dimensional disaster facing the multi-term expansion probability power flows of the input variable. The simulation calculation is carried out in an improved IEEE-9 and IEEE-30 examples and compared with the traditional method. The reslts verify the effectiveness of the method. © 2023 Science Press. All rights reserved.
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页码：351 / 359
页数：8
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