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Alejandro Zamora-Mendez

affiliation not provided to SSRN

SCHOLARLY PAPERS

4

DOWNLOADS

163

TOTAL CITATIONS

1

Scholarly Papers (4)

1.

Enhancing Wide-Area Damping Controllers Via Data-Assisted Power System Linear Models

Number of pages: 20 Posted: 29 Mar 2022
Universidad Nacional Autonoma de Mexico, affiliation not provided to SSRN, affiliation not provided to SSRN, Universidad del Valle, University of Tennessee, Knoxville, affiliation not provided to SSRN, Zurich University of Applied Sciences and ZHAW Zurich University of Applied Sciences
Downloads 107 (654,668)
Citation 1

Abstract:

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Wide-area damping control, Loewner interpolation method, linear quadratic Gaussian control, power oscillation, system identification

2.

Assessment of the Inertia in Electrical Grids by Estimators

Number of pages: 29 Posted: 31 Dec 2021
Juan M. M. Ramirez, Mario R. Arrieta-Paternina and Alejandro Zamora-Mendez
Unidad Guadalajara - Electrical Engineering Department, Universidad Nacional Autonoma de Mexico and affiliation not provided to SSRN
Downloads 42 (1,170,409)

Abstract:

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Discrete-time Taylor-Fourier transform, Dynamic state estimation, Frequency, inertia, Power System Monitoring, Rate-of-change-of-frequency

3.

Early warning detection system for large events in power systems

Number of pages: 16 Posted: 04 Jun 2026
affiliation not provided to SSRN, affiliation not provided to SSRN, ZHAW Zurich University of Applied Sciences, affiliation not provided to SSRN, Zurich University of Applied Sciences, affiliation not provided to SSRN and affiliation not provided to SSRN
Downloads 8

Abstract:

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PMU data, event detection, Teager-Kaiser energy operator, Higher-order statistics, WAMS

4.

Machine Learning for Power Systems: A Critical Review Bridging the Interpretability and Performance Gap

Number of pages: 27 Posted: 04 Jul 2026
Gabriel E. Mejia-Ruiz, Esteban E. Rosero-García and Alejandro Zamora-Mendez
Universidad del Valle, Universidad del Valle and affiliation not provided to SSRN
Downloads 6

Abstract:

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Machine Learning, Power Systems, Deep Reinforcement Learning, Renewable Energy Integration, Explainable AI, Smart grids