Prof. Dr. Aleksandr Kulikov | Energy and Sustainability | Research Excellence Award

Prof. Dr. Aleksandr Kulikov | Energy and Sustainability | Research Excellence Award | Lecturer | Nizhny Novgorod State Technical University | Russia

Prof. Dr. Aleksandr Kulikov is a distinguished scholar in electric power engineering, widely recognized for his advanced expertise in relay protection, emergency automation, distributed generation, digital signal processing, and the cybersecurity of modern electric networks. Prof. Dr. Aleksandr Kulikov holds multiple higher education degrees across engineering and economics, including a doctoral-level qualification in electric power systems, complemented by earlier academic work in radar, radio navigation, and economic system reliability. His professional experience spans leadership positions in major energy enterprises, where he served as Deputy Chief Engineer, Director of trunk electric grid operations, scientific supervisor for government-funded technology programs, and a long-serving professor at the Nizhny Novgorod State Technical University. Throughout his career, Prof. Dr. Aleksandr Kulikov has demonstrated outstanding commitment to academic excellence, supervising master’s students, postgraduate researchers, and doctoral candidates, many of whom have successfully defended their dissertations under his mentorship. His research interests cover a wide spectrum of modern power-system challenges, including high-voltage line fault detection, emergency-mode processing, machine-learning-based grid diagnostics, selective control of power-quality indicators, and the reliability of renewable-integrated microgrids. His research skills span analytical modeling, digital signal processing, industrial frequency analysis, reliability assessment, fault location techniques, and the development of novel algorithms and decision-support systems for complex power networks. Prof. Dr. Aleksandr Kulikov has contributed more than three hundred scientific works, including publications in Scopus-indexed and Web of Science–indexed journals, IEEE conference proceedings, and high-impact engineering journals. His inventions and patents further demonstrate his innovative contributions to advancing electric power technologies. Among his numerous awards and honors are prestigious regional, national, and international distinctions, including medals, honorary diplomas, and recognition for excellence in scientific innovation and contributions to the development of electrical grid infrastructure. His leadership roles include membership in dissertation councils, editorial board service for multiple scientific journals, and expert involvement in federal technology-development programs. Prof. Dr. Aleksandr Kulikov continues to advance the field through active research, academic mentorship, and scientific collaboration, maintaining a significant influence on the evolution of intelligent power systems and modern energy engineering. His longstanding dedication to the scientific community, strong record of innovation, and impact on the reliability of national power systems solidify his status as a leading figure deserving of international recognition.

Profile: ORCID 

Featured Publications 

1. Kulikov, A., Loskutov, A., Ilyushin, P., Kurkin, A., & Sluzova, A. (2025). High-voltage overhead power line fault location through sequential determination of faulted section technologies.

2. Ilyushin, P., Papkov, B., Kulikov, A., & Suslov, K. (2025). Algorithm and methods for analyzing power consumption behavior of industrial enterprises considering process characteristics.

3. Kulikov, A. L., Sevostyanov, A. A., & Ilyushin, P. V. (2023). Analysis of electrical-power quality in modern power-supply systems with selective control of several indicators.

4. Kulikov, A., Ilyushin, P., Loskutov, A., & Filippov, S. (2023). Application of search algorithms in determining fault location on overhead power lines according to the emergency mode parameters.

5. Kulikov, A., Loskutov, A., Bezdushniy, D., & Petrov, I. (2023). Decision tree models and machine learning algorithms in the fault recognition on power lines with branches.

6. Kulikov, A., Ilyushin, P., & Suslov, K. (2023). Enhanced readability of electrical network complex emergency modes provided by data compression methods.

7. Kulikov, A., Ilyushin, P., Suslov, K., & Filippov, S. (2023). Estimating the error of fault location on overhead power lines by emergency state parameters using an analytical technique.