In the rapidly evolving landscape of China’s new power system, a groundbreaking study led by Jing Wang from the College of Applied Science and Technology at Beijing Union University is shedding light on the complexities of power dynamic load signals. As the nation strides towards its “dual carbon” strategic goals, the integration of clean energy and the rise of high-power dynamic loads are causing nonlinear, random dynamic changes in electric energy signals. These fluctuations, often leading to significant deviations in electric energy measurements, pose a substantial challenge to the fairness and rationality of energy trading.
Wang’s research, published in the Journal of Engineering Sciences, delves into the heart of this issue, exploring the critical characteristics of dynamic loads that cause metering deviations. The study focuses on the energy economy and the scientific problems arising from the implementation of national strategies. “We identified the need to accurately characterize the global features of high-power dynamic load signals, which existing research has struggled to address,” Wang explains.
The research team collected complex dynamic load signals from electrified railways and electric arc furnaces, constructing discrete mathematical models to analyze and extract important features. In the waveform domain, they discovered that voltage signals exhibit approximate stability, while current signals show fast, random dynamic fluctuations. Notably, the current amplitudes follow an approximately Gaussian distribution with decreasing autocorrelation coefficients.
But the real innovation lies in the team’s exploration of the run-length domain. By constructing a binary run-length sequence of complex dynamic load signals, they analyzed and extracted features such as local and global run-length mode changes, modulation depth, and impact strength of current amplitudes. “Our method has significant advantages in simultaneously extracting local and global features of complex electric energy signals, characterizing important aspects like large-scale fluctuations, rapid time-varying changes, and strong randomness,” Wang asserts.
The implications for the energy sector are profound. By constructing constraint conditions based on the typical characteristics of the run and waveform domains, the team developed a binary m-sequence dynamic energy-testing signal model. This model reflects the typical features of dynamic load signals and the most significant factors affecting energy measurement errors, covering the maximum range of feature parameter changes.
The practical applications are already being tested. A dynamic error-testing system for electric-energy measurements has been built, and the dynamic error of the electric-energy meter has been tested under binary dynamic electric-energy-testing signal conditions. Experimental verification confirmed that the test signal reflected the typical characteristics of dynamic loads under the influence of electric-energy measurement errors.
This research, published in the Journal of Engineering Sciences (工程科学学报), provides a theoretical basis for the analysis of dynamic energy signal characteristics in complex scenarios, the construction of multifeature constraint models, and the dynamic error testing of energy meters. As the energy sector continues to evolve, Wang’s work offers a crucial step forward in ensuring the accuracy and fairness of energy trading in an increasingly complex power system.
The study not only addresses immediate challenges but also paves the way for future developments. By providing a robust framework for analyzing and modeling dynamic load signals, it equips the industry with the tools needed to navigate the complexities of modern energy systems. As Wang’s research gains traction, it could significantly influence the design and implementation of smart meters and energy trading platforms, ultimately contributing to a more efficient and equitable energy economy.