Dr. Anton Loskutov | Energy and Sustainability | Research Excellence Award

Dr. Anton Loskutov | Energy and Sustainability | Research Excellence Award | Associate Professor | Nizhny Novgorod State Technical University | Russia

Dr. Anton Loskutov is an accomplished researcher in intelligent electrical networks, power system automation, and high-voltage fault-location technologies, recognized for his extensive contributions to smart-grid development and advanced diagnostic algorithms. He has established himself as a leading specialist through his strong academic background, having completed his doctoral education in electrical engineering with research focused on automation devices, distributed generation systems, and sequential decision-making algorithms for network reliability. Dr. Anton Loskutov’s professional experience spans multiple collaborative engineering projects where he has worked closely with multidisciplinary teams to design, model, and optimize high-voltage systems, develop machine learning–based recognition methods for emergency modes, and implement simulation-driven approaches for improving relay protection and network automation. His research interests include power-line fault detection, traveling-wave pattern recognition, machine learning applications in electrical networks, data-compression techniques for emergency-mode analysis, and algorithmic control strategies for distributed generation environments. He is proficient in high-level research skills such as advanced modeling, ANSYS-based structural simulations, sequential analysis, algorithm development, signal-processing methods, and machine learning techniques applied to real-time power-system automation. Throughout his academic and professional journey, he has demonstrated strong analytical ability, technical precision, and a solution-oriented mindset that supports cutting-edge innovations in the energy sector. Dr. Anton Loskutov has earned recognition from peers and institutions for his publications in reputable journals, contributions to IEEE-indexed conferences, and collaborative advancements in next-generation intelligent grid technologies. His honors include repeated acknowledgements for impactful research studies, participation in international engineering conferences, and contributions to the development of advanced control methods for distributed generation networks. With a robust research portfolio, strong interdisciplinary expertise, and continued engagement in scientific collaborations, he continues to strengthen global understanding of smart-grid stability, automation algorithms, and data-driven fault-recognition systems. In conclusion, Dr. Anton Loskutov stands out as a highly dedicated and innovative researcher whose work significantly enhances the reliability, efficiency, and intelligence of modern electrical networks, making him an influential contributor to the advancing field of power-system engineering.

Profile: ORCID 

Featured Publications

1. Loskutov, A. (2025). High-voltage overhead power line fault location through sequential determination of faulted section. 2025. Citations: 5

2. Loskutov, A. (2023). Fault location method for overhead power line based on a multi-hypothetical sequential analysis using the Armitage algorithm. 2023. Citations: 18

3. Loskutov, A. (2023). Decision tree models and machine learning algorithms in the fault recognition on power lines with branches. 2023. Citations: 22

4. Loskutov, A. (2023). Enhanced readability of electrical network complex emergency modes provided by data compression methods. 2023. Citations: 12

5. Loskutov, A. (2023). Application of search algorithms in determining fault location on overhead power lines according to the emergency mode parameters. 2023. Citations: 15

6. Loskutov, A. (2022). WSPRT methods for improving power system automation devices in the conditions of distributed generation sources operation. 2022. Citations: 20

7. Loskutov, A. (2022). Relay protection and automation algorithms of electrical networks based on simulation and machine learning methods. 2022. Citations: 30