Design of a computationally efficient observer-based distributed fault detection and isolation scheme in second-order...

Area of Science:

• Control Systems Engineering
• Networked Control Systems (NCS)
• Fault Detection and Isolation (FDI)

Background:

• Networked Control Systems (NCS) are susceptible to faults that can compromise system performance and safety.
• Existing fault detection and isolation (FDI) methods for NCS can be computationally intensive and may require multiple steps.
• Distributed FDI is crucial for complex systems like power grids, but challenges remain in achieving efficient and integrated solutions.

Purpose of the Study:

• To propose a novel directional observer-based fault detection and isolation (FDI) scheme for second-order networked control systems (NCS).
• To develop two design schemes, utilizing global and partial/local network models, for distributed FDI in NCS.
• To validate the proposed scheme's effectiveness and computational efficiency compared to existing methods.

Main Methods:

• Exploitation of the directional unknown input observer (UIO) tool for distributed FDI.
• Development of two distinct design schemes incorporating global and partial/local network models.
• Computation of thresholds for robust performance in noisy environments.
• Application to power system models for empirical validation.

Main Results:

• A novel, single-step fault detection and isolation scheme was successfully developed using a single observer.
• The proposed directional observer-based scheme demonstrated effectiveness in identifying and isolating faults within NCS.
• The scheme proved computationally efficient when compared against existing FDI methodologies.
• Validation on power system models confirmed the practical applicability and reliability of the proposed approach.

Conclusions:

• The proposed directional observer-based FDI scheme offers an effective and computationally efficient solution for second-order NCS.
• Achieving both fault detection and isolation in a single step simplifies implementation and enhances diagnostic capabilities.
• The method's robustness in noisy environments and its successful application to power systems highlight its practical significance.