Voltage Stability Estimation Considering Variability in Reactive Power Reserves Using Regression Trees

Masato Miyazaki (), Mutsumi Aoki and Yuta Nakamura
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Masato Miyazaki: Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
Mutsumi Aoki: Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
Yuta Nakamura: Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan

Energies, 2025, vol. 18, issue 5, 1-16

Abstract: The rapid integration of renewable energy sources, such as photovoltaic power systems, has reduced the necessary for synchronous generators, which traditionally contributed to grid stability during disturbances. This shift has led to a decrease in reactive power reserves (RPRs), raising concerns about voltage stability. Real-time monitoring of voltage stability is crucial for transmission system operators to implement timely corrective actions. However, conventional methods, such as continuation power flow calculations, are computationally intensive and unsuitable for large-scale power systems. Machine learning techniques using data from phasor measurement units have been proposed to estimate voltage stability. However, these methods do not consider changes in generator operating conditions and fluctuating RPRs. As renewable energy generation increases, the operating conditions of generators vary, which leads to significant changes in system RPRs and voltage stability. In this paper, a voltage stability margin is proposed using regression trees with RPRs varying based on generator operation conditions. Simulations based on the IEEE 9-bus system demonstrate that the proposed approach provides an accurate and efficient voltage stability estimation.

Keywords: voltage stability; machine learning; P–V curve; regression tree; continuation power flow; reactive power reserves; high share of renewables energies (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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