Artificial‐Intelligence‐Based Reduced Sensor Voltage Control
As crucial smart grid components, these systems will provide carbon-free and sustainableenergytoconsumerswhileoperatingingrid-tiedand
As crucial smart grid components, these systems will provide carbon-free and sustainableenergytoconsumerswhileoperatingingrid-tiedand
To solve this problem, this paper develops an adversarial-based deep transfer learning model that can detect and classify short-circuit faults in DC microgrids without using historical fault data.
This study aims to illustrate the implementation of ANN-based voltage control for DER unit in a DC microgrid with a reduced number of sensors and its effectiveness over a wide range of
Abstract: This paper proposes a secondary control for a decentralized DC microgrid (DCMG) based on the DC-bus voltage (DCV) monitoring value not only to achieve power and voltage regulation but
A double modular hardware redundancy scheme has been used in the proposed controller to handle the battery current sensor faults in the microgrid.
This paper presents an artificial neural network (ANN) voltage control for a DC‐DC step‐up converter to reduce the number of sensors in the DC microgrids.
In this study, to enhance the system reliability under false data injection (FDI) attacks and DC-link voltage (DCLV) sensor failures, a hybrid control strategy for a DC microgrid (DCMG) based
In order to improve robust operating performance and enhance bus voltage stability, a learning observer-based fault-tolerant control strategy is proposed for the distributed generation in islanded
Voltage regulation and energy management in isolated microgrids are difficult tasks because of the lack of grid support. Battery storage interface control is essential for stable system voltage because of
This work investigates sensor fault diagnostics and fault-tolerant control for a voltage source converter based microgrid (model) using a sliding-mode observer.
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