Repository logo
Communities & Collections
Research Outputs
Fundings & Projects
People
Statistics
New user? Click here to register.Have you forgotten your password?
  1. Home
  2. KMITL
  3. Publication
  4. Embedded Intelligent Reactive Power Control for Distributed Controllable Loads to Support Grid Voltage Considering Islanding Conditions
Loading...
Thumbnail Image

Embedded Intelligent Reactive Power Control for Distributed Controllable Loads to Support Grid Voltage Considering Islanding Conditions

Author(s)
Surinkaew, Tossaporn
Pinthurat, Watcharakorn
Yang, Jun
Ngamroo, Issarachai
Date Issued
January 1, 2025
Type
Article
DOI
10.1109/TSG.2025.3596558
Abstract
The increasing integration of distributed energy resources (DERs) along with distributed controllable loads (DCLs), presents significant challenges for maintaining grid stability and effectively incorporating these resources. Traditionally, DERs have supported microgrid (MG) stability primarily through active power control loops, which has limited their ability to manage power output effectively. This paper introduces a novel embedded intelligent reactive power control framework for DCLs, designed to bolster grid voltage stability during islanding conditions. This framework seamlessly integrates reactive power control loops as ancillary services within DCLs, which can also support other grid-forming (GFM) resources. The DCL is modeled with grid-following (GFL) control loops, which incorporate an embedded intelligent reactive power control loop into the voltage control system. The proposed intelligent framework applies a modified long-short term memory neural network to imitate reactive power behavior. The network is trained with the special loss function to minimize voltage variation. This enables the intelligent GFL control to accurately follow voltage and frequency references from the GFM resources and adapt to unexpected islanding scenarios. Simulation results in a low-inertia MG with DERs confirm the superiority of the proposed framework. It outperforms conventional methods, including mixed H2/H∞ strategy, model predictive control, and convolutional neural network. Through a comprehensive evaluation, the proposed strategy shows superior performance in transient response, voltage stability, power dynamics, statistical analysis, and damping performance.
Citation
IEEE Transactions on Smart Grid, 16(6), 4921-4937, 2025
Subjects

Distributed controlla...

distributed energy re...

embedded intelligent ...

grid-forming/followin...

islanding microgrids

Metrics
Get Involved!
  • Source Code
  • Documentation
  • Slack Channel
Make it your own

DSpace-CRIS can be extensively configured to meet your needs. Decide which information need to be collected and available with fine-grained security. Start updating the theme to match your Institution's web identity.

Need professional help?

The original creators of DSpace-CRIS at 4Science can take your project to the next level, get in touch!

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Accessibility settings
  • Privacy policy
  • End User Agreement
  • Send Feedback