In the rapidly evolving landscape of autonomous systems, one of the most pressing challenges is optimizing the performance of on-board equipment, particularly in the energy sector. As drones and other autonomous vehicles become increasingly integral to energy infrastructure monitoring and maintenance, the need for efficient, high-performance information and measurement systems has never been greater. Enter the work of V.Yu. Trofimov, a researcher at the Baltic State Technical University “Voenmeh” named after D.F. Ustinov in Saint Petersburg, who has been delving into the intricacies of digital frequency multipliers to address these very issues.
Trofimov’s research, published in the journal ‘Измерение, мониторинг, управление, контроль’ (Measurement, Monitoring, Management, Control), focuses on the development of digital frequency multipliers that can significantly enhance the performance of autonomous information and measurement systems. The crux of the problem lies in the need for a clock frequency that is much higher than what is required for autonomous operation, a requirement that often conflicts with the goal of minimizing current consumption.
“Increasing the performance of remotely controlled on-board equipment requires a clock frequency significantly higher than that needed for autonomous operation,” Trofimov explains. “This creates a contradiction that needs to be resolved, especially when precise frequency ratios are essential.”
Traditional methods of frequency multiplication have their limitations. Multipliers using pulse pack generators, for instance, suffer from large unevenness in output pulses. On the other hand, those employing automatic frequency adjustment lack instantaneous mode output and can have transient durations exceeding several tens of seconds. Trofimov’s solution? A combined method that leverages the strengths of both approaches.
The combined method proposed by Trofimov involves generating pulse packs and auto-tuning with phase detection. This hybrid approach ensures instantaneous output to the mode and minimizes the non-uniformity of output pulses, addressing the shortcomings of existing methods. “The combined method of frequency multiplication based on generation of pulse packs and auto-tuning with phase detection combines the positive qualities of both techniques,” Trofimov notes.
So, what does this mean for the energy sector? As autonomous systems become more prevalent in energy infrastructure, the ability to efficiently and accurately measure and monitor various parameters is crucial. Trofimov’s research paves the way for more reliable and high-performance autonomous information and measurement systems, which can lead to improved efficiency, reduced downtime, and enhanced safety in energy operations.
The implications of this research are far-reaching. As the energy sector continues to embrace automation and digitalization, the need for advanced frequency multiplication techniques will only grow. Trofimov’s work not only addresses current challenges but also sets the stage for future developments in the field. By combining the best of both worlds—pulse pack generation and auto-tuning with phase detection—the combined method offers a promising solution that could revolutionize the way autonomous systems operate in the energy sector.
As the energy industry continues to evolve, the work of researchers like Trofimov will be instrumental in shaping the future of autonomous information and measurement systems. Their contributions will undoubtedly drive innovation and pave the way for more efficient, reliable, and high-performance solutions in the years to come.