scholarly journals Higher Order Sliding Mode Observer-Based Sensor Fault Detection in DC Microgrid’s Buck Converter

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1586
Author(s):  
Daijiry Narzary ◽  
Kalyana C. Veluvolu
Keyword(s):  
Fault Detection ◽  
Sliding Mode ◽  
Higher Order ◽  
Buck Converter ◽  
Sensor Fault ◽  
Dc Microgrid ◽  
Sensor Fault Detection ◽  
Multiple Sensor ◽  
Current Sensors ◽  
Sliding Mode Observers

Fault detection in a Direct Current (DC) microgrid with multiple interconnections of distributed generation units (DGUs) is an interesting topic of research. The occurrence of any sensor fault in the DC microgrid should be detected immediately by the fault detection network to achieve an overall stable performance of the system. This work focuses on sensor fault diagnosis of voltage and current sensors in interconnected DGUs of the microgrid. Two separate higher order sliding mode observers (HOSM) based on model dynamics are designed to estimate the voltage and current and generate the residuals for detecting the faulty sensors in DGUs. Multiplicative single and multiple sensor faults are considered in voltage and current sensors. By appropriate selection of threshold, single and multiple sensor fault detection strategies are formulated. A hierarchical controller is designed to ensure equal sharing of current among the DGUs of the DC microgrid and stabilize the system. Simulations are performed to validate the proposed approach for various configurations of the DC microgrid under various load and off noise conditions.

scholarly journals Sensor Fault Detection for a class of Uncertain Nonlinear Systems Using Sliding Mode Observers

2020 ◽  
Vol 14 (2) ◽  
pp. 27-33
Author(s):  
Farideh Allahverdi ◽  
Amin Ramezani ◽  
Mehdi Forouzanfar ◽  
◽  
◽  
...  
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Sliding Mode ◽  
Uncertain Nonlinear Systems ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Sliding Mode Observers

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

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