Bank of Extended Kalman Filters for Faults Diagnosis in Wind Turbine Doubly Fed Induction Generator

Idrissi, Imane; Chafouk, Houcine; Bachtiri, Rachid El; Khanfara, Maha

doi:10.12928/telkomnika.v16i6.10426

Cited by 3 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The test results demonstrate the effectiveness of our approach in precisely identifying all possible cases of single and multiple faults in the sensors of both converters at the abc real reference frame. This diagnostic approach demonstrates the advantage of our suggested method over other approaches presented in [29][30][31][32], which identify sensor faults in the (αβ) or (dq) frames. In addition, the contribution of this scientific paper lies in the diagnosis of current sensor faults on the GSC, compared with previous methods [29][30][31][32].…”

Section: Discussionmentioning

confidence: 74%

“…This diagnostic approach demonstrates the advantage of our suggested method over other approaches presented in [29][30][31][32], which identify sensor faults in the (αβ) or (dq) frames. In addition, the contribution of this scientific paper lies in the diagnosis of current sensor faults on the GSC, compared with previous methods [29][30][31][32].…”

Section: Discussionmentioning

confidence: 74%

“…Furthermore, other diagnostic approaches are developed to detect and locate multiple and simultaneous faults for the current sensors of the DFIG. This is done through a bank of observers based on a Kalman filter, structured according to the generalized observer scheme (GOS), as described in [30,31], or structured according to the dedicated observer scheme (DOS), as presented in [32,33].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach

Abbas,

Chafouk,

Ardjoun

2024

Sensors

Self Cite

View full text Add to dashboard Cite

Currently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However, the control of these two converters depends mainly on current sensors measurements. Nevertheless, in the case of sensor failure, control stability may be compromised, leading to serious malfunctions in the wind turbine system. Therefore, in this article, we will present an innovative diagnostic approach to detect, locate, and isolate the single and/or multiple real-phase current sensors in both converters. The suggested approach uses an extended Kalman filter (EKF) bank structured according to a generalized observer scheme (GOS) and relies on a nonlinear model for the RSC and a linear model for the GSC. The EKF estimates the currents in the converters, which are then compared to sensor measurements to generate residuals. These residuals are then processed in the localization, isolation, and decision blocks to precisely identify faulty sensors. The obtained results confirm the effectiveness of this approach to identify faulty sensors in the abc phases. It also demonstrates its ability to overcome the nonlinearity induced by wind fluctuations, as well as resolves the coupling issue between currents in the fault period.

show abstract

Section: Discussionmentioning

confidence: 74%

Section: Discussionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach

Abbas,

Chafouk,

Ardjoun

2024

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…To do this, the Kalman filter takes the previous estimate of parameters and errors and predicts the new parameters and errors based on the electrical resistance furnace model. The second step [24]- [28] will update this prediction with the new measures. These measurements (by definition noisy) will allow obtaining an estimate of the parameters and the error from the prediction made.…”

Section: Introductionmentioning

confidence: 99%

Study of designing regulator for temperature electrical resistance furnace using Kalman stochastic reconstructor

Kada

Kebir²,

Berka³

et al. 2021

IJEECS

View full text Add to dashboard Cite

<span>Electric <span>resistance furnaces are the most popular and widely used industrial electro thermal equipment which continues to be the subject of many improvements. The aim of this paper is to control the temperature of electrical furnace for noisy thermocouple sensors. It can be assessed by observing some variables, which are very difficult to observe. Due to limitations, mainly the location of thermal sensors and their noises. In this case, the temperature measurement is trained with centered Gaussian white noise. The problem of accurate temperatures estimation for such sensors is solved using Kalman filter, which is an optimized estimator that provides a computationally efficient way to estimate system state. Thus, variables that are not directly measurable can be reconstructed from the algorithm. Kalman stochastic reconstructor (KSR). We cannot use with fixed parameters to control the temperature. For this reason, this paper comes up with a KSR approach based pole placement (PL) hybrid controller to realize an algorithm for the temperature control electrical furnace. Results based on Matlab simulation show that the improved algorithm has well produced an optimal estimate of the temperature. Evolving over time from noisy measurements. Hybrid algorithm KSR approach based PL give good performance compared to PL controllers.</span></span>

show abstract

Current Sensor Fault Diagnosis in DFIG Wind Turbines: Kalman Filter-Based Dedicated Observer Scheme

Abbas,

Ardjoun,

Chafouk

2024

2024 3rd International Conference on Advanced Electrical Engineering (ICAEE)

View full text Add to dashboard Cite

Bank of Extended Kalman Filters for Faults Diagnosis in Wind Turbine Doubly Fed Induction Generator

Cited by 3 publications

References 22 publications

Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach

Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach

Study of designing regulator for temperature electrical resistance furnace using Kalman stochastic reconstructor

Current Sensor Fault Diagnosis in DFIG Wind Turbines: Kalman Filter-Based Dedicated Observer Scheme

Contact Info

Product

Resources

About