A Machine Learning Initializer for Newton-Raphson AC Power Flow Convergence

Okhuegbe, Samuel N.; Ademola, Adedasola A.; Liu, Yilu

doi:10.1109/tpec60005.2024.10472261

A Machine Learning Initializer for Newton-Raphson AC Power Flow Convergence

Conference · Sun Feb 11 23:00:00 EST 2024 · 2024 IEEE Texas Power and Energy Conference (TPEC)

DOI:https://doi.org/10.1109/tpec60005.2024.10472261· OSTI ID:2447266

Okhuegbe, Samuel N. ^[1]; Ademola, Adedasola A. ^[2]; Liu, Yilu ^[3]

Univ. of Tennessee, Knoxville, TN (United States)
Univ. of Tennessee, Knoxville, TN (United States); Dominion Energy, Glen Allen, VA (United States)
Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Power flow computations are fundamental to many power system studies. Obtaining a converged power flow case is not a trivial task especially in large power grids due to the non-linear nature of the power flow equations. One key challenge is that the widely used Newton based power flow methods are sensitive to the initial voltage magnitude and angle estimates, and a bad initial estimate would lead to non-convergence. This paper addresses this challenge by developing a random-forest (RF) machine learning model to provide better initial voltage magnitude and angle estimates towards achieving power flow convergence. This method was implemented on a real ERCOT 6102 bus system under various operating conditions. By providing better Newton-Raphson initialization, the RF model precipitated the solution of 2,106 cases out of 3,899 non-converging dispatches. These cases could not be solved from flat start or by initialization with the voltage solution of a reference case. Finally, results obtained from the RF initializer performed better when compared with DC power flow initialization, Linear regression, and Decision Trees.

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-00OR22725

OSTI ID:: 2447266

Journal Information:: 2024 IEEE Texas Power and Energy Conference (TPEC), Journal Name: 2024 IEEE Texas Power and Energy Conference (TPEC)

Country of Publication:: United States

Language:: English

References (10)

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A Machine Learning Initializer for Newton-Raphson AC Power Flow Convergence

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