The inclusion of physical information in machine learning frameworks has revolutionized
many application areas. This involves enhancing the learning process by incorporating …

The widespread penetration of inverter-based resources has profoundly impacted the
electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and …

This paper introduces Dyna-PINN, a novel physics-informed Deep Dyna-Q (DDQ)
reinforcement learning (RL) approach, designed to address the data-intensive training …

This article developed a novel real-time control approach for the sparse and safe frequency
regulation in inverter intensive microgrids (MGs). In the scenario, the inverters and external …

The large-scale integration of intermittent renewable energy resources introduces increased
uncertainty and volatility to the supply side of power systems, thereby complicating system …

Deep reinforcement learning (DRL) is decisive in addressing uncertainties in intelligent grid-
building interactions. Using DRL algorithms, this research optimizes the operational strategy …

Gold cyanide leaching is a critical step in the extraction of gold from ore. The desire for a
higher leaching rate often leads to increased cyanide concentrations, which pose safety …

This paper presents a semi-peer coordination control strategy to ensure the bus voltage
stability and effectively constrain the power trajectory, thereby mitigating safety concerns …

With the increasing integration of renewable energy sources and the shift toward a low-
carbon economy, managing the complexities of integrated electric and gas systems (IEGS) …

Emergency control is essential for maintaining the stability of power systems, serving as a
key defense mechanism against the destabilization and cascading failures triggered by …