4.7 Article

Robust intelligent fault diagnosis strategy using Kalman observers and neuro-fuzzy systems for a wind turbine benchmark

Related references

Note: Only part of the references are listed.
Article Engineering, Electrical & Electronic

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

Abdelmoumen Saci et al.

Summary: This paper presents an effective fault diagnosis method for sensors, actuators, and system faults in a wind turbine machine. The proposed method utilizes physical and analytical redundancy of sensors and actuators to generate appropriate residuals. A crisp logic technique is used to classify actuator and sensor faults. Simulation results demonstrate the ability of this method to effectively identify faults in the principal sub-systems of the wind turbine.

ELECTRICAL ENGINEERING (2022)

Article Energy & Fuels

Evaluating the effect of wind turbine faults on power using the Monte Carlo method

Dariush Biazar et al.

Summary: Component damage and failure in sensor or actuator can lead to system faults and reduce performance. This paper investigates the effect of faults on wind turbines through experiments and ranks the severity of these faults on output power and measured variables. The results show that the impact of different faults on wind turbines varies in partial and full load regions.

WIND ENERGY (2022)

Article Engineering, Chemical

Sliding Mode Observer-Based Fault Detection and Isolation Approach for a Wind Turbine Benchmark

Vicente Borja-Jaimes et al.

Summary: This paper presents a fault detection and isolation approach based on nonlinear sliding mode observers for a wind turbine model, addressing the problems surrounding pitch and drive train system FDI. A fault diagnosis strategy using nonlinear sliding mode observer banks is proposed, which is capable of handling model uncertainties and external disturbances.

PROCESSES (2022)

Article Energy & Fuels

Bearing Fault Diagnosis under Time-Varying Speed and Load Conditions via Observer-Based Load Torque Analysis

Ming Ye et al.

Summary: This study proposes a novel bearing fault diagnosis method based on natural observer, which analyzes the effects of bearing faults in the permanent magnetic synchronous machine on load torque signals and implements techniques such as angular resampling and modified threshold determination algorithm to improve the diagnosis effectiveness.

ENERGIES (2022)

Article Green & Sustainable Science & Technology

Sensitivity analysis for evaluation of the effect of sensors error on the wind turbine variables using Monte Carlo simulation

Dariush Biazar et al.

Summary: The researchers investigate the effect of sensor error on the output power of wind turbines in different operating regions using sensitivity analysis and Monte Carlo simulation. They rank the impact of sensor errors on output power using importance factors, thereby determining the most influential sensors.

IET RENEWABLE POWER GENERATION (2022)

Article Engineering, Mechanical

Fuzzy Diagnostic Strategy Implementation for Gas Turbine Vibrations Faults Detection: Towards a Characterization of Symptom-fault Correlations

Nadji Hadroug et al.

Summary: The study introduces an original approach to gas turbine monitoring using fuzzy logic, which enables real-time monitoring of turbine efficiency and substantial improvement in operational safety and cost-effectiveness. The fault diagnosis strategy based on fuzzy approach demonstrates efficiency and robustness, leading to increased turbine work time and reduced unscheduled downtime.

JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES (2022)

Article Green & Sustainable Science & Technology

A novel wind turbine fault diagnosis method based on compressed sensing and DTL-CNN

Yan Zhang et al.

Summary: This paper presents a fault diagnosis method for identifying different fault conditions in wind turbines' rolling bearings and gears. Compressed sensing technology is used for noise reduction and feature extraction. The fault diagnosis scheme combines deep transfer learning and convolutional neural network (DTL-CNN) for fault type identification. Experimental results demonstrate the reliability and superiority of the proposed method in wind turbine fault diagnosis of rolling bearings and gears.

RENEWABLE ENERGY (2022)

Article Green & Sustainable Science & Technology

Wind speed forecasting using a hybrid model considering the turbulence of the airflow

Alma Rosa Mendez-Gordillo et al.

Summary: This study proposes a hybrid model for wind speed forecasting, combining Multiplicative Cascade and Persistence techniques. The performance of the hybrid model is found to exceed that of the individual models, as evaluated using various error metrics.

RENEWABLE ENERGY (2022)

Article Automation & Control Systems

Simultaneous fault diagnosis of wind turbine using multichannel convolutional neural networks

Samira Zare et al.

Summary: The study developed an autonomous databased fault diagnosis algorithm using a multichannel convolutional neural network, which was successfully evaluated in a 5MW wind turbine benchmark model. Results showed that the algorithm can diagnose common wind turbine faults with high accuracy.

ISA TRANSACTIONS (2021)

Article Energy & Fuels

MPPT Improvement for PMSG-Based Wind Turbines Using Extended Kalman Filter and Fuzzy Control System

Amirsoheil Honarbari et al.

Summary: This paper explores the implementation of maximum power point tracking (MPPT) in a permanent magnet synchronous generator (PMSG) system by determining the uncertainty of unpredictable parameters using the extended Kalman filter (EKF). Additionally, a fuzzy logic control (FLC) system is used to control the generator speed in order to minimize the impact of unpredictable parameters on the system. The simulation results demonstrate an enhancement in MPPT accuracy and output power efficiency.

ENERGIES (2021)

Article Green & Sustainable Science & Technology

Diagnosis of wind turbine faults with transfer learning algorithms

Wanqiu Chen et al.

Summary: This study presents a framework for fault diagnosis of wind turbine faults using transfer learning algorithms Inception V3 and TrAdaBoost, and introduces a new evaluation index 'Comprehensive Index'. Traditional machine learning algorithms perform poorly for unbalanced and differently distributed datasets, while the novel transfer learning algorithm TrAdaBoost shows superior performance in dealing with such challenges.

RENEWABLE ENERGY (2021)

Article Green & Sustainable Science & Technology

Fault diagnosis of the 10MW Floating Offshore Wind Turbine Benchmark: A mixed model and signal-based approach

Yichao Liu et al.

Summary: A mixed model and signal-based Fault Diagnosis architecture is developed to detect and isolate critical faults in Floating Offshore Wind Turbines. The effectiveness of the proposed architecture is demonstrated through a 10 MW FOWT benchmark with predefined faults. The proposed architecture outperforms other approaches in detecting and isolating critical faults under diverse operating conditions.

RENEWABLE ENERGY (2021)

Article Chemistry, Analytical

Adaptive Observer Based Fault Tolerant Control for Sensor and Actuator Faults in Wind Turbines

Jing Teng et al.

Summary: This article proposes an adaptive observer-based fault tolerant control scheme for addressing sensor and actuator faults in the core subsystems of wind turbines, utilizing the FAFE algorithm for fault detection and designing state feedback controllers to stabilize the system. Simulation results demonstrate that the proposed scheme outperforms the benchmark model in stabilizing the faulty system.

SENSORS (2021)

Review Engineering, Chemical

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

Zhiwei Gao et al.

Summary: Wind energy is playing an increasingly important role in the world energy market, but concerns about the high failure rate of wind turbine systems are growing. Therefore, improving the reliability and performance of wind turbine systems is crucial. Over the past 20 years, significant progress has been made in fault diagnosis, prognosis, and resilient control techniques for wind turbine systems.

PROCESSES (2021)

Article Engineering, Industrial

Robust diagnosis with high protection to gas turbine failures identification based on a fuzzy neuro inference monitoring approach

Choayb Djeddi et al.

Summary: The development of new monitoring and diagnostic procedures is essential for modern industry to enhance operational safety and availability. The proposed solution for gas turbine monitoring incorporates an adaptive neural-fuzzy inference system to predict component degradation and achieve high efficiency and reliability.

JOURNAL OF MANUFACTURING SYSTEMS (2021)

Article Green & Sustainable Science & Technology

Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks

Seongpil Cho et al.

Summary: The paper develops a fault detection and diagnosis method for a hydraulic blade pitch system in a spar-type floating wind turbine, using a Kalman filter for fault detection and an artificial neural network for fault diagnosis. The neural network model shows effective performance after training, validation, and test procedures.

RENEWABLE ENERGY (2021)

Article Energy & Fuels

Fault-Tolerant Control of a Wind Turbine Generator Based on Fuzzy Logic and Using Ensemble Learning

Jordi Cusido et al.

Summary: This paper introduces a Fault-Tolerant Control model based on power derating for wind turbines, using algorithms to predict gearbox health status and employing fuzzy logic to decide on power output adjustments. Simulation results show that reducing power output leads to safer operation with decreased stresses on the blades and tower, while empirically supporting reductions in generator torque and speed leading to lower gearbox bearing and oil temperatures. By implementing this power-derating FTC, downtime due to failures can be controlled and overall power production can be increased, potentially resulting in a yearly CO2 emissions reduction of over 325,000 tons if implemented globally.

ENERGIES (2021)

Article Automation & Control Systems

Diagnosing with a hybrid fuzzy-Bayesian inference approach

Jan Maciej Koscielny et al.

Summary: This article introduces a hybrid approach to integrate Bayesian and fuzzy logic methods for diagnostic purposes. By calculating the truthfulness of premises in fuzzy rules, the probability of conditional observations can be estimated, bridging the gap between Bayesian approach and fuzzy approach.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)

Article Computer Science, Artificial Intelligence

Data-driven fault diagnosis for wind turbines using modified multiscale fluctuation dispersion entropy and cosine pairwise-constrained supervised manifold mapping

Zhenya Wang et al.

Summary: A novel data-driven fault diagnosis scheme for wind turbines, using RTSMFDE and CPCSMM, is proposed in this paper. It comprehensively mines wind turbine features and outperforms existing entropy methods, while also achieving better visualization and classification effects.

KNOWLEDGE-BASED SYSTEMS (2021)

Article Green & Sustainable Science & Technology

Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data

Yanting Li et al.

Summary: This paper proposes a fault diagnosis method for wind turbines based on parameter-based transfer learning and convolutional autoencoder, suitable for small-scale data. The method can transfer knowledge from similar wind turbines and shows advantages in fault diagnosis.

RENEWABLE ENERGY (2021)

Article Chemistry, Multidisciplinary

Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis

Saverio Farsoni et al.

Summary: This paper investigates two fault diagnosis solutions for wind turbine systems using data-driven approaches based on fuzzy systems and neural networks. The developed methods are validated using a high-fidelity benchmark that simulates healthy and faulty behaviors for residual generators.

APPLIED SCIENCES-BASEL (2021)

Article Mathematics, Applied

Reliability Modeling Using an Adaptive Neuro-Fuzzy Inference System: Gas Turbine Application

Nadji Hadroug et al.

Summary: This work proposes a novel approach to reliability modeling by applying an adaptive neuro-fuzzy inference system to determine failure assessment indicators on a gas turbine, aiming to improve its performance and operational safety parameters. The application of fuzzy rules in reliability estimation is innovative and provides solutions for identifying gas turbines in complex operating environments.

FUZZY INFORMATION AND ENGINEERING (2021)

Article Thermodynamics

Global status of wind power generation: theory, practice, and challenges

Muhammad Arshad

INTERNATIONAL JOURNAL OF GREEN ENERGY (2019)

Article Green & Sustainable Science & Technology

Fault diagnosis of wind turbine based on Long Short-term memory networks

Jinhao Lei et al.

RENEWABLE ENERGY (2019)

Review Green & Sustainable Science & Technology

Machine learning methods for wind turbine condition monitoring: A review

Adrian Stetco et al.

RENEWABLE ENERGY (2019)

Article Chemistry, Multidisciplinary

Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

Silvio Simani et al.

APPLIED SCIENCES-BASEL (2019)

Article Chemistry, Multidisciplinary

Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter

Donggil Kim et al.

APPLIED SCIENCES-BASEL (2019)

Article Computer Science, Artificial Intelligence

Active fault tolerant control based on a neuro fuzzy inference system applied to a two shafts gas turbine

Nadji Hadroug et al.

APPLIED ARTIFICIAL INTELLIGENCE (2018)

Article Green & Sustainable Science & Technology

Data driven sensor and actuator fault detection and isolation in wind turbine using classifier fusion

Vahid Pashazadeh et al.

RENEWABLE ENERGY (2018)

Editorial Material Green & Sustainable Science & Technology

Real-time monitoring, prognosis, and resilient control for wind turbine systems

Zhiwei Gao et al.

RENEWABLE ENERGY (2018)

Article Green & Sustainable Science & Technology

Fault-tolerant wind turbine pitch control using adaptive sliding mode estimation

Jianglin Lan et al.

RENEWABLE ENERGY (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Fuzzy model-based faults diagnosis of the wind turbine benchmark

Ayoub El Bakri et al.

PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING IN DATA SCIENCES (ICDS2017) (2018)

Article Thermodynamics

Value of wind power - Implications from specific power

V. Johansson et al.

ENERGY (2017)

Review Green & Sustainable Science & Technology

A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management

Surya Teja Kandukuri et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2016)

Review Automation & Control Systems

Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/Set-membership approach

Rosa M. Fernandez-Canti et al.

ANNUAL REVIEWS IN CONTROL (2015)

Article Automation & Control Systems

A Benchmark Evaluation of Fault Tolerant Wind Turbine Control Concepts

Peter Fogh Odgaard et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2015)

Article Automation & Control Systems

Fault Diagnosis of a Wind Turbine Benchmark via Identified Fuzzy Models

Silvio Simani et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2015)

Article Automation & Control Systems

A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis-Part II: Signals and Signal Processing Methods

Wei Qiao et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2015)

Article Automation & Control Systems

Combination of Model-based Observer and Support Vector Machines for Fault Detection of Wind Turbines

Nassim Laouti et al.

INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING (2014)

Article Automation & Control Systems

Fault-Tolerant Control of Wind Turbines: A Benchmark Model

Peter Fogh Odgaard et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2013)

Review Multidisciplinary Sciences

Opportunities and challenges for a sustainable energy future

Steven Chu et al.

NATURE (2012)