Anomaly Detection and Classification Method for Wind Speed Data of Wind Turbines Using Spatiotemporal Dependency Structure

Li, Yang; Shen, Xiaojun; Zhang, Yangfan

doi:10.1109/tste.2023.3270865

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…The use of renewable energy sources has, therefore, become inevitable to mitigate these effects [1][2][3][4]. Among the envisaged solutions, wind systems emerge as one of the most promising options [5][6][7]. Their versatility is evident in various contexts, ranging from grid-connected farms or isolated sites to configurations of hybrid energy systems [8,9].…”

Section: Introductionmentioning

confidence: 99%

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

Abbas,

Chafouk,

Ardjoun

2024

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Abbas,

Chafouk,

Ardjoun

2024

Sensors

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“…At present, there has been modeling and analysis of various battery types of ESPSs [5]- [7] . Low practical application value is caused by the dearth of research on the modeling of ESPSs appropriate for integrating modules like battery clusters (BCs), battery management systems (BMSs), and power conversion systems (PCSs).…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis method of energy storage stations considering the correlation of battery clusters

Zhan

2024

J. Phys.: Conf. Ser.

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In the context of dual carbon, in order to address the volatility of new energy output, energy storage power stations (ESPSs) are thriving. However, it is challenging to assess the dependability of ESPS operation because of the steady functioning of individual battery clusters (BCs), which will have an impact on how safely and steadily the power system is evaluated. Therefore, a reliability model for energy storage BC units based on the structural analysis of battery structure modules is constructed in this manuscript. Then, a method for constructing a correlation model of energy storage units based on the Copula function was proposed. Finally, a simulation analysis based on the ESPSs of pertinent demonstrative application projects in Fujian Province is used as an example to confirm the viability and efficacy of the proposed reliability analysis approach.

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Dual-module multi-head spatiotemporal joint network with SACGA for wind turbines fault detection

Wang,

Yin

2024

Energy

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Anomaly Detection and Classification Method for Wind Speed Data of Wind Turbines Using Spatiotemporal Dependency Structure

Cited by 4 publications

References 26 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

Reliability analysis method of energy storage stations considering the correlation of battery clusters

Dual-module multi-head spatiotemporal joint network with SACGA for wind turbines fault detection

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