Real-Time Sensor Fault Tolerant Control of DC-DC Converters in DC Microgrids Using a Switching Unknown Input Observer

DC Microgrids are rapidly developing, driven by the increasing integration of renewable sources and dynamic loads. Their growing adoption in critical applications raises the need for operational safety and reliability, particularly under fault conditions. This paper presents a sensor fault-tolerant control (FTC) for DC-DC power converters in DC Microgrids designed to maintain resilient and reliable operation. The proposed FTC leverages a single Switching Unknown Input Observer (SW-UIO) at the bottom control level to estimate arbitrary sensor faults with a high accuracy (0.3% fault estimation error), allowing a real-time identification and precise control reconfiguration to accommodate sensor faults with minimal computational burden. The approach is validated under diverse conditions, including time-varying loads, complete sensor failure, noise abnormality, and simultaneous faults/failure, achieving a consistent fault accommodation of 1μs from fault occurrence to compensation. Real-time simulation has been performed in the OPAL-RT environment to assess the efficacy and superiority of the proposed sensor fault tolerant strategy in comparison to reference FTC strategies.

Authors

Institutions

Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3571650
Citations
3
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
3.64

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Real-Time Sensor Fault Tolerant Control of DC-DC Converters in DC Microgrids Using a Switching Unknown Input Observer

Abdelhafid Benyounes, Toufik Rekioua, Amal H. Alharbi, Said Barkat et al.
3 citations
IEEE Access
Microgrid Control and Optimization
3.64
article

Real-Time Sensor Fault Tolerant Control of DC-DC Converters in DC Microgrids Using a Switching Unknown Input Observer

Abdelhafid Benyounes, Toufik Rekioua, Amal H. Alharbi, Said Barkat, Abdelaziz Rabehi, Syphax Ihammouchen, El‐Sayed M. El‐kenawy, Mohammed Said Ouahabi
article en
3 citations

Abstract

DC Microgrids are rapidly developing, driven by the increasing integration of renewable sources and dynamic loads. Their growing adoption in critical applications raises the need for operational safety and reliability, particularly under fault conditions. This paper presents a sensor fault-tolerant control (FTC) for DC-DC power converters in DC Microgrids designed to maintain resilient and reliable operation. The proposed FTC leverages a single Switching Unknown Input Observer (SW-UIO) at the bottom control level to estimate arbitrary sensor faults with a high accuracy (0.3% fault estimation error), allowing a real-time identification and precise control reconfiguration to accommodate sensor faults with minimal computational burden. The approach is validated under diverse conditions, including time-varying loads, complete sensor failure, noise abnormality, and simultaneous faults/failure, achieving a consistent fault accommodation of 1μs from fault occurrence to compensation. Real-time simulation has been performed in the OPAL-RT environment to assess the efficacy and superiority of the proposed sensor fault tolerant strategy in comparison to reference FTC strategies.

IEEE AccessVol. 13
Princess Nourah bint Abdulrahman University (SA), X-Fab (Germany) (DE), Ziane Achour University of Djelfa (DZ), University Mohamed Boudiaf of M'sila (DZ), Bahrain Polytechnic (BH)
Princess Nourah Bint Abdulrahman University
Affordable and clean energy
Openalex Percentile: Top 7%
Microgrid Control and Optimization
3.64
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.