Interval-Valued Fuzzy c-Means Algorithm and Interval-Valued Density-Based Fuzzy c-Means Algorithm

Varshney, Ayush K.; Mehra, Priyanka; Muhuri, Pranab K.; Lohani, Q. M. Danish

doi:10.1109/fuzz48607.2020.9177602

Cited by 6 publications

(1 citation statement)

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“…The possibility analysis model introduced in power flow calculations has been widely accepted. In [14], the fuzzy interval algorithm [15][16][17][18][19] is used to solve power flow calculation problems. Although it is a fuzzy model in specific cases, it represents an alternative way to handle uncertain boundaries.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Fuzzy Newton Power Flow Convergence

Zhao,

Zheng,

Zhen

et al. 2023

Energies

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In order to address the convergence issue in fuzzy power flow calculations, this paper proposes an analytical approach based on the Levenberg–Marquardt method, aiming to improve the convergence of the fuzzy Newton power flow method. Firstly, a detailed analysis is conducted on the convergence theorem and convergence behavior of the fuzzy Newton method, revealing its poor convergence when the initial values are not properly selected. The Levenberg–Marquardt method is then selected as a means to enhance the convergence of the fuzzy Newton power flow calculations, specifically to tackle the problem of initial value deviation. Since the Jacobian matrix has a significant impact on the convergence region of the power flow, this paper reconstructs the Jacobian matrix based on the Levenberg–Marquardt method, effectively enlarging the convergence region. Through validation experiments on the IEEE 118 standard nodes and simulation comparative analysis, the results confirm the method’s effectiveness in resolving the problem of initial value deviation and notably enlarging the convergence region, thereby improving the convergence of power flow calculations.

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