Effective fault diagnosis method for the pitch system, the drive train, and the generator with converter in a wind turbine system

Saci, Abdelmoumen; Cherroun, Lakhmissi; Hafaifa, Ahmed; Mansour, O.

doi:10.1007/s00202-021-01446-8

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…These modern approaches have been studied and applied in several works, such as; Abdellatif Mahammedi et al in [1], Ahmed Hafaifa et al in [4], Aref Eskandari et al in [8], Balamurugan et al in [10], Barun Basnet et al in [12], Fengxin Cui et al in [14], Imed Kaid et al in [17], Jianbo Yu et al in [19], Kurukuru et al in [22], Ruby Beniwal et al in [28]. Other research works have been conducted in the direction of improving the quality of electrical power generation, and detecting faults over time with high sensitivity of the monitoring system, such as the works of Abdelmoumen Saci et al in [2], Ahmed Zohair Djeddi et al in [5], Ali Kidar et al in [6], Azghandi Ali et al in [9], Fan Jia et al in [13], Ghadir Badran et al in [16], José Miguel et al in [20], Joshuva Arockia et al in [21], Mohamed Ben Rahmoune et al in [24], Mohammed Amine Deriche et al in [25][26], Sally Abdulaziz et al in [29] and Vincenzo Carletti et al in [33]. However, the need to detect and locate a failure according to the needs of the industry and the complexity of the systems calls for several diagnostic techniques, which have different criteria characteristics for the detection and diagnosis of these failures, which solve the problems of fault diagnosis conveniently.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis of photovoltaic system based on neural networks approach

Rahmoune¹,

Iratni²,

Amari³

et al. 2023

Diagnostyka

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Solar energy has become one of the most important renewable energies in the world. With the increasing installation of power plants in the world, the supervision and diagnosis of photovoltaic systems have become an important challenge with the increased occurrence of various internal and external faults. Indeed, this work proposes a new solar power plant diagnosis based on the artificial neural network approach. The developed model was to improve the performance and reliability of the power plant located in Tamanrasset, Algeria, which is subjected to varying weather conditions in terms of radiation and ambient temperature. By using the real data collected from the studied system, this approach allow to increase electricity production and address any issues that may arise quickly, ensuring uninterrupted power supply for the region. Neural networks have shown interesting results with high accuracy. This fault diagnosis approach allows to determine the time of occurrence of a fault affecting the examined PV system. Also, allow an early detection of failures and degradation of the system, which contributes to improving the productivity of this photovoltaic installation. With a significant reduction in the time needed to repair the damage caused by these faults and improve the reliability and continuity of the electrical energy production service.

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Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis of photovoltaic system based on neural networks approach

Rahmoune¹,

Iratni²,

Amari³

et al. 2023

Diagnostyka

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“…Different researches and studies have been proposed to deal with the diagnosis task of wind turbines using several approaches [3,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. These proposed methodologies adopt distinctive design of schemes, resulting in different properties according to the used techniques.…”

Section: Introductionmentioning

confidence: 99%

“…The mainly possible occurred faults are simulated with different scenarios in sensors, actuators, and system faults for all components of the horizontal three blades wind turbine: pitch system, the drive train, the generator, and the converting system. Paper [12], an effective fault diagnosis method is proposed for the previous WT model in [11]. Where, the elaborated structure is based on physical redundancy in sensors to carry out the correct residuals between all the process measurements.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

Zemali¹,

Cherroun²,

Hadroug³

et al. 2023

Diagnostyka

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This paper aims to present a robust fault diagnosis structure-based observers for actuator faults in the pitch part system of the wind turbine benchmark. In this work, two linear estimators have been proposed and investigated: the Kalman filter and the Luenberger estimator for observing the output states of the pitch system in order to generate the appropriate residual between the measured positions of blades and the estimated values. An inference step as a decision block is employed to decide the existence of faults in the process, and to classify the detected faults using a predetermined threshold defined by upper and lower limits. All actuator faults in the pitch system of the horizontal wind turbine benchmark are studied and investigated. The obtained simulation results show the ability of the proposed diagnosis system to determine effectively the occurred faults in the pitch system. Estimation of the output variables is effectively realized in both situations: without and with the occurrence of faults in the studied process. A comparison between the two used observers is demonstrated.

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Fault detection and identification for control systems in floating offshore wind farms: A supervised Deep Learning methodology

Fernandez-Navamuel,

Peña-Sanchez,

Nava

2024

Ocean Engineering

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Effective fault diagnosis method for the pitch system, the drive train, and the generator with converter in a wind turbine system

Cited by 9 publications

References 21 publications

Fault detection and diagnosis of photovoltaic system based on neural networks approach

Fault detection and diagnosis of photovoltaic system based on neural networks approach

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

Fault detection and identification for control systems in floating offshore wind farms: A supervised Deep Learning methodology

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