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Probabilistic Power Flow Calculation Based on the Data-Driven Polynomial Chaos Approximation
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Ao-yu LEI1, You-jin JIANG2, 3, Cheng-xi LIU2, 3, *, Yong MEI1, Yong-jian LUO2, 3, Hong-yue ZHEN4
Science Technology and Engineering | 2025, 25(2) : 598 - 609
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Science Technology and Engineering | 2025, 25(2): 598-609
• Papers·Electrical Technology •
Probabilistic Power Flow Calculation Based on the Data-Driven Polynomial Chaos Approximation
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Ao-yu LEI1, You-jin JIANG2, 3, Cheng-xi LIU2, 3, *, Yong MEI1, Yong-jian LUO2, 3, Hong-yue ZHEN4
Affiliations
  • 1 CSG Power Dispatch Control Center, Guangzhou 510663, China
  • 2 School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
  • 3 Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan 430072, China
  • 4 State Key Laboratory of HVDC, Electric Power Research Institute, CSG, Guangzhou 510663, China
Published: 2025-01-18 doi: 10.12404/j.issn.1671-1815.2401491
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In order to analyze the influence of uncertain factors on power system, PCA (polynomial chaos approximation) method, which is both fast and accurate, is widely used in probabilistic power flow calculation. The polynomial chaotic approximation method requires that the probability density function of the random input variable is known, and the random input variable must satisfy the independent condition. A probabilistic power flow method based on DDPCA (data driven polynomial chaos approximation) was proposed for the known random input variables which are historical data. First, DDPCA selects the optimal orthogonal polynomial according to the historical data, and then determines the Gaussian sample considering the nonlinear correlation of random input variables, and then computes the weights with Monte Carlo integral. Then, a small amount of power flow was calculated based on Gaussian samples, and the approximation coefficient was solved according to the power flow results and weights, and then the statistical characteristics of the random output variables were obtained. The proposed method was compared with the point estimation method, and the effectiveness of the proposed method was verified by the results of three examples.

data-driven  /  polynomial chaos approximation  /  Gaussian samples  /  probabilistic power flow  /  Monte Carlo integral  /  nonlinear correlation
Ao-yu LEI, You-jin JIANG, Cheng-xi LIU, Yong MEI, Yong-jian LUO, Hong-yue ZHEN. Probabilistic Power Flow Calculation Based on the Data-Driven Polynomial Chaos Approximation[J]. Science Technology and Engineering, 2025 , 25 (2) : 598 -609 . DOI: 10.12404/j.issn.1671-1815.2401491
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Year 2025 volume 25 Issue 2
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Article Info
doi: 10.12404/j.issn.1671-1815.2401491
  • Receive Date：2024-03-05
  • Online Date：2025-12-05
  • Published：2025-01-18
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History
  • Received：2024-03-05
  • Revised：2024-10-24
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Affiliations
    1 CSG Power Dispatch Control Center, Guangzhou 510663, China
    2 School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
    3 Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan 430072, China
    4 State Key Laboratory of HVDC, Electric Power Research Institute, CSG, Guangzhou 510663, China
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