Comprehensive Flexible Framework for Using Multi-Machine Learning Methods to Optimal Dynamic Transient Stability Prediction by Considering Prediction Accuracy and Time DOI Creative Commons

Ali Abdalredha,

Ali Reza Sobbouhi, Abolfazl Vahedi

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104728 - 104728

Опубликована: Март 1, 2025

Язык: Английский

Does New Digital Infrastructure Contribute to Energy-Carbon Performance?The Path Analysis of Heterogeneous Technological Progress DOI
X. Zeng, Jian Tang, Ming Ji

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

On the Exact Formulation of the Optimal Phase-Balancing Problem in Three-Phase Unbalanced Networks: Two Alternative Mixed-Integer Nonlinear Programming Models DOI Creative Commons
Oscar Danilo Montoya, Brandon Cortés-Caicedo, Óscar David Florez-Cediel

и другие.

Electricity, Год журнала: 2025, Номер 6(1), С. 9 - 9

Опубликована: Март 2, 2025

This article presents two novel mixed-integer nonlinear programming (MINLP) formulations in the complex variable domain to address optimal phase-balancing problem asymmetric three-phase distribution networks. The first employs a matrix-based load connection model (M-MINLP), while second uses compact vector-based representation (V-MINLP). Both integrate power flow equations through current injection method, capturing nonlinearities of Delta and Wye loads. These formulations, solved via an interior-point optimizer branch-and-cut method Julia software, ensure global optima computational efficiency. Numerical validations on 8-, 25-, 37-node feeders showed loss reductions 24.34%, 4.16%, 19.26%, outperforming metaheuristic techniques convex approximations. M-MINLP was 15.6 times faster 25-node grid 2.5 system when compared V-MINLP approach. results demonstrate robustness scalability proposed methods, particularly medium large systems, where often fail converge. advance state art by combining exact mathematical modeling with efficient computation, offering precise, scalable, practical tools for optimizing corresponding were performed using (v1.10.2), JuMP (v1.21.1), AmplNLWriter (v1.2.1).

Язык: Английский

Процитировано

0

A Real-Time Analytical Steady-State Calculation in Power System Restoration: Managing Uncertainty DOI Creative Commons

Noureyeh Zahiroddin,

M. Khalilifar, S. Mohammad Shahrtash

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104721 - 104721

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Comprehensive Flexible Framework for Using Multi-Machine Learning Methods to Optimal Dynamic Transient Stability Prediction by Considering Prediction Accuracy and Time DOI Creative Commons

Ali Abdalredha,

Ali Reza Sobbouhi, Abolfazl Vahedi

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104728 - 104728

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0