DFIG Defects Diagnosis Method for Wind Energy Conversion Chain

Hammouchi, Fatima El; Menzhi, Lamia El; Saâd, Abdallah

doi:10.25046/aj040523

Cited by 5 publications

(3 citation statements)

References 13 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence the necessity of induction generator monitoring and online diagnosis in order to resolve this problem. Among the online diagnostic methods [19], those based on the stator current such as the Park vector [20], [21] or its  ISSN: 1693-6930 spectrum [22], [23] or their combination [24]. In this part, an approach for detecting and characterizing three types of voltage dips is presented.…”

Section: Combinaition Of Fast Fourier Transform and Lissajous Curvesmentioning

confidence: 99%

Wind turbine doubly-fed induction generator defects diagnosis under voltage dips

Kharoubi

Menzhi²

2022

TELKOMNIKA

View full text Add to dashboard Cite

Voltage dips are brief but frequent disturbances that can be defined as the most important problem affecting all electrical grids, it cannot be completely avoided and represent the main priority for the industry. This study remains part of monitoring the quality of electrical energy's field. Its aim is to apply two approaches signals for improving the failure recognition in wind turbine conversion chain. One is based on the combination of spectral analysis of stator current by fast Fourier transform and Lissajous curves technique of grid currents. The second approach is based on the stator current analysis by discrete wavelet transform which is considered an ideal tool for this purpose due to its time-frequency localization. These defects diagnosis methods have shown their efficiency in detection, visualization and analysis of voltage dips in order to develop a control strategy for a wind turbine requiring continuity of its service despite the presence of voltage dips.

show abstract

Section: Combinaition Of Fast Fourier Transform and Lissajous Curvesmentioning

confidence: 99%

Wind turbine doubly-fed induction generator defects diagnosis under voltage dips

Kharoubi

Menzhi²

2022

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

“…The spatiotemporal distribution of natural wind speed and the effects of wind shear and tower shadow effect are analyzed along with blade mass imbalance fault in DFIG WTs [3]. Frequency spectrum and Lissajous curves are studied and analysed to detect WTs flaws [4]. Zhang et al [5] proposes an image array for recognizing the defect in turbine blades using a neural network.…”

Section: Introductionmentioning

confidence: 99%

Blade imbalance fault identification in doubly fed induction generator through current signature analysis using wavelet transform

Kushwaha,

Yadav,

Maurya

2024

Bulletin EEI

View full text Add to dashboard Cite

Using wind turbines (WTs) equipped with doubly fed induction generators (DFIG) is a popular technology for generating renewable energy. To ensure safe operation, prompt maintenance, and better operational reliability, the induction generator used in wind energy must be monitored. In this paper, an analysis is carried out on stator currents of the DFIG machine in a wind farm to identify any blade imbalances in the wind farm. A fault characteristics extraction analysis is carried out on the machine stator currents to detect the fault in the system. Firstly, the mathematical equation of the DFIG blade unbalanced stator current is generated using the DFIG model. Secondly, Park's Transformation is used to modify the stator's 3-phase current. Further, by evaluating the feature frequency amplitude variation in the squared signal by doing a spectral analysis on the stator current vector's squared signal. Lastly, a Simulink model for the DFIG is developed. The suggested approach analyses the fault signal of the imbalanced blade fault at various wind velocities. The outcomes show that the suggested method for diagnosing impeller imbalance faults can successfully locate the fault.

show abstract

Section: Introductionmentioning

confidence: 99%

Risk analysis and prevention in computer security in institutional servers, a systematic review of the literature

Namo-Ochoa,

Portilla-Cosar,

Sierra-Liñan

et al. 2024

Bulletin EEI

View full text Add to dashboard Cite

In recent years, computer attacks on the server infrastructure in organizations have been increasing, and the pandemic of covid-19 and remote work have been the main causes for this massive wave of large-scale attacks, small businesses are especially vulnerable because to optimizing resources they leave aside the cyber security in their network infrastructure. The present research is a systematic review that compiles 58 articles where policies, techniques, and infrastructure for the prevention of threats in enterprise servers have been implemented and raised, these articles have been collected from major databases such as IEEE Xplore, SAGE, Science Direct, Scopus, and IOP Publishing. The results show that one of the most effective methods in preventing communications between institutional servers is public key infrastructure/SSL-TLS encryption. Most research claims that it is the most effective method as it provides a central certifier and manages the certificates for the servers allowing each of the modules or attachments within the infrastructure to identify and validate other members and to proceed with the encryption of network traffic, Finally, a security implementation model is proposed.

show abstract

DFIG Defects Diagnosis Method for Wind Energy Conversion Chain

Cited by 5 publications

References 13 publications

Wind turbine doubly-fed induction generator defects diagnosis under voltage dips

Wind turbine doubly-fed induction generator defects diagnosis under voltage dips

Blade imbalance fault identification in doubly fed induction generator through current signature analysis using wavelet transform

Risk analysis and prevention in computer security in institutional servers, a systematic review of the literature

Contact Info

Product

Resources

About