DOI:10.35833/MPCE.2020.000738 |
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Data-driven Power Flow Method Based on Exact Linear Regression Equations |
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Page view: 163
Net amount: 372 |
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Author:
Yanbo Chen1,Chao Wu1,Junjian Qi2
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Author Affiliation:
1.the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, China;2.the Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA
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Foundation: |
This work was supported in part by National Natural Science Foundation of China (No. 52077076) and in part by the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (No. LAPS202118). |
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Abstract: |
Power flow (PF) is one of the most important calculations in power systems. The widely-used PF methods are the Newton-Raphson PF (NRPF) method and the fast-decoupled PF (FDPF) method. In smart grids, power generations and loads become intermittent and much more uncertain, and the topology also changes more frequently, which may result in significant state shifts and further make NRPF or FDPF difficult to converge. To address this problem, we propose a data-driven PF (DDPF) method based on historical/simulated data that includes an offline learning stage and an online computing stage. In the offline learning stage, a learning model is constructed based on the proposed exact linear regression equations, and then the proposed learning model is solved by the ridge regression (RR) method to suppress the effect of data collinearity. In online computing stage, the nonlinear iterative calculation is not needed. Simulation results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy. |
Keywords: |
Data driven ; exact linear regression equation ; Fast-decoupled power flow ; Newton-Raphson method |
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Received:October 12, 2020
Online Time:2022/05/12 |
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