ABSTRACT
Simulation analysis is critical for identifying possible hazards and ensuring secure operation of power systems. In practice, large-disturbance rotor angle stability and voltage stability are two frequently intertwined stability problems. Accurately identifying the dominant instability mode (DIM) between them is important for directing power system emergency control action formulation. However, DIM identification has always relied on human expertise. This article proposes an intelligent DIM identification framework that can discriminate among stable status, rotor angle instability, and voltage instability based on active deep learning (ADL). To reduce human expert efforts required to label the DIM dataset when building DL models, a two-stage batch-mode integrated ADL query strategy (preselection and clustering) is designed for the framework. It samples only the most helpful samples to label in each iteration and considers both information contents and diversity in them to improve query efficiency, significantly reducing the required number of labeled samples. Case studies conducted on a benchmark power system (China Electric Power Research Institute (CEPRI) 36-bus system) and a practical large-area power system (Northeast China Power System) reveal that the proposed approach outperforms conventional methods in terms of accuracy, label efficiency, scalability, and adaptability to operational variability.
ABSTRACT
With the increasing amounts of terminal equipment with higher requirements of communication quality in the emerging fifth generation mobile communication network (5G), the energy consumption of 5G base stations (BSs) is increasing significantly, which not only raises the operating expenses of telecom operators but also imposes a burden on the environment. To solve this problem, a two-step energy management method that coordinates 5G macro BSs for 5G networks with user clustering is proposed. The coordination among the communication equipment and the standard equipment in 5G macro BSs is developed to reduce both the energy consumption and the electricity costs. A novel user clustering method is proposed together with Benders decomposition to accelerate the solving process. Simulation results show that the proposed method is computationally efficient and can ensure near-optimal performance, effectively reducing the energy consumption and electricity costs compared with the conventional dispatching scheme.
ABSTRACT
Water electrolysis at high current density (1000 mA cm-2 level) with excellent durability especially in neutral electrolyte is the pivotal issue for green hydrogen from experiment to industrialization. In addition to the high intrinsic activity determined by the electronic structure, electrocatalysts are also required to be capable of fast mass transfer (electrolyte recharge and bubble overflow) and high mechanical stability. Herein, the 2D CoOOH sheet-encapsulated Ni2P into tubular arrays electrocatalytic system was proposed and realized 1000 mA cm-2-level-current-density hydrogen evolution over 100 h in neutral water. In designed catalysts, 2D stack structure as an adaptive material can buffer the shock of electrolyte convection, hydrogen bubble rupture, and evolution through the release of stress, which insure the long cycle stability. Meanwhile, the rich porosity between stacked units contributed the good infiltration of electrolyte and slippage of hydrogen bubbles, guaranteeing electrolyte fast recharge and bubble evolution at the high-current catalysis. Beyond that, the electron structure modulation induced by interfacial charge transfer is also beneficial to enhance the intrinsic activity. Profoundly, the multiscale coordinated regulation will provide a guide to design high-efficiency industrial electrocatalysts.
