This paper presents a photovoltaic (PV)-assisted Unified Power Quality Conditioner (UPQC) for enhancing power quality in grid-connected microgrids. The main novelty lies in the integration of a Sequence-Aware Gated Attention Neural Network (SA-GANN) and a Chaotic Parrot Optimization Algorithm tuned PI (CPOA-PI) contr...
In recent years, wind power generation has attained significant attention, leading to the installation of numerous wind power plants worldwide. Most wind turbines employ induction generators, which typically absorb substantial reactive power (VARs) from the grid. This reactive power demand can result in voltage drops a...
Voltage instability remains a major operational challenge in large-scale transmission networks, particularly in developing power systems with increasing load demand. This paper proposes a coordinated on-load tap changer (OLTC) control strategy based on a Hybrid Particle Swarm Optimization–Grey Wolf Optimization (HPSOGW...
These results validate the effectiveness of recurrent neural networks, particularly LSTM, for modeling complex processes in traction networks and provide a reliable algorithmic basis for implementing predictive real-time intelligent control of compensating devices, such as FACTS and UPQC systems.
In distribution network and microgrids, energy storage (ES) systems possess four-quadrant operational capabilities, making them inherently high-quality resources for reactive power (RP) regulation. However, existing research has primarily focused on optimizing the active power of ES to achieve economic objectives, whil...
With such operation mode, the smart energy systems require a high-power quality and fast regulation of electrical signals, making artificial intelligence (AI)guided control increasingly important for AC-DC power converters under continuously changing operating conditions. To bridge the conventional fixed-gain controlle...
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