RTU Zinātniskās darbības rezultāti un resursi: Personāls

Informācija par autoru

Vārds, Uzvārds:

Ismoil Odinaev

Publikācijas

Atrasti 3 rezultāti tiek attēloti no 1 līdz 3.

Methodology for Transient Stability Assessment and Enhancement in Low-Inertia Power Systems Using Phasor Measurements: A Data-Driven Approach
(2025)

Mihail Senyuk, Svetlana Berjozkina, Ismoil Odinaev, Inga Zicmane, Murodbek Safaraliev
Zinātniskais raksts, kas indeksēts Web of science un/vai Scopus datu bāzē

Method of Accelerated Low-Frequency Oscillation Analysis in Low-Inertia Power Systems Based on Orthogonal Decomposition
(2025)

Mihail Senyuk, Svetlana Berjozkina, Ismoil Odinaev, Inga Zicmane, Murodbek Safaraliev
Zinātniskais raksts, kas indeksēts Web of science un/vai Scopus datu bāzē

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review
(2023)

Andrey Pazderin, Firuz Kamalov, Pavel Gubin, Murodbek Safaraliev, Vladislav Samoylenko, Nikita Mikhlynin, Ismoil Odinaev, Inga Zicmane
Zinātniskais raksts, kas indeksēts Web of science un/vai Scopus datu bāzē

Informācija par autoru

Publikācijas

Methodology for Transient Stability Assessment and Enhancement in Low-Inertia Power Systems Using Phasor Measurements: A Data-Driven Approach (2025)

Method of Accelerated Low-Frequency Oscillation Analysis in Low-Inertia Power Systems Based on Orthogonal Decomposition (2025)

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review (2023)

Methodology for Transient Stability Assessment and Enhancement in Low-Inertia Power Systems Using Phasor Measurements: A Data-Driven Approach
(2025)

Method of Accelerated Low-Frequency Oscillation Analysis in Low-Inertia Power Systems Based on Orthogonal Decomposition
(2025)

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review
(2023)