RTU Zinātniskās darbības rezultāti un resursi: Personāls

Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data
(2025)

Mihail Senyuk, Svetlana Beryozkina, Inga Zicmane, Murodbek Safaraliev, Viktor Klassen, Firuz Kamalov
Zinātniskais raksts, kas indeksēts Web of science un/vai Scopus datu bāzē

Attention-Based Load Forecasting with Bidirectional Finetuning
(2024)

Firuz Kamalov, Inga Zicmane, Murodbek Safaraliev, Linda Smail, Mihail Senyuk, Pavel Matrenin
Zinātniskais raksts, kas indeksēts Web of science un/vai Scopus datu bāzē

Directions of Application of Phasor Measurement Units for Control and Monitoring of Modern Power Systems: A State-of-the-Art Review
(2023)

Andrey Pazderin, Inga Zicmane, Mihail Senyuk, Pavel Gubin, Ilya Polyakov, Nikita Mikhlynin, Murodbek Safaraliev, Firuz Kamalov
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

Fast Algorithms for Estimating the Disturbance Inception Time in Power Systems Based on Time Series of Instantaneous Values of Current and Voltage with a High Sampling Rate
(2022)

Mihail Senyuk, Svetlana Beryozkina, Pavel Gubin, Anna Dmitrieva, Firuz Kamalov, Murodbek Safaraliev, Inga Zicmane
Zinātniskais raksts, kas indeksēts Web of science un/vai Scopus datu bāzē

Informācija par autoru

Publikācijas

Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data
(2025)

Attention-Based Load Forecasting with Bidirectional Finetuning
(2024)

Directions of Application of Phasor Measurement Units for Control and Monitoring of Modern Power Systems: A State-of-the-Art Review
(2023)

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

Fast Algorithms for Estimating the Disturbance Inception Time in Power Systems Based on Time Series of Instantaneous Values of Current and Voltage with a High Sampling Rate
(2022)

Informācija par autoru

Publikācijas

Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data (2025)

Attention-Based Load Forecasting with Bidirectional Finetuning (2024)

Directions of Application of Phasor Measurement Units for Control and Monitoring of Modern Power Systems: A State-of-the-Art Review (2023)

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

Fast Algorithms for Estimating the Disturbance Inception Time in Power Systems Based on Time Series of Instantaneous Values of Current and Voltage with a High Sampling Rate (2022)

Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data
(2025)

Attention-Based Load Forecasting with Bidirectional Finetuning
(2024)

Directions of Application of Phasor Measurement Units for Control and Monitoring of Modern Power Systems: A State-of-the-Art Review
(2023)

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

Fast Algorithms for Estimating the Disturbance Inception Time in Power Systems Based on Time Series of Instantaneous Values of Current and Voltage with a High Sampling Rate
(2022)