scholarly journals Distributed Compression for Condition Monitoring of Wind Farms

2013 ◽  
Vol 4 (1) ◽  
pp. 174-181 ◽  
Author(s):  
Vladimir Stankovic ◽  
Lina Stankovic ◽  
Shuang Wang ◽  
Samuel Cheng
Keyword(s):  
Condition Monitoring ◽  
Wind Farms ◽  
Distributed Compression

Condition Monitoring of Wind Turbines Based on the Scattering Transform of Vibration Data

2021 ◽  
Author(s):  
Junyu Qi ◽  
Alexandre Mauricio ◽  
Konstantinos Gryllias
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Wind Farms ◽  
Operating Conditions ◽  
Vibration Data ◽  
Bearing Diagnostics ◽  
Scattering Transform ◽  
The One ◽  
Envelope Spectrum ◽  
High Effort

Abstract As a renewable, unlimited and free resource, wind energy has been intensively deployed in the past to generate electricity. However, the maintenance of Wind Turbines (WTs) can be challengeable. On the one hand, most wind farms operate in remote areas and on the other hand, the dimension of WTs’ tip/hub/rotor are usually enormous. In order to prevent abrupt breakdowns of WTs, a number of Condition Monitoring (CM) methods have been proposed. Focusing on bearing diagnostics, Squared Envelope Spectrum is one of the most common techniques. Moreover in order to identify the optimum demodulation frequency band, fast Kurtogram, Infogram and Sparsogram are nowadays popular tools evaluating respectively the Kurtosis, the Negentropy and the Sparsity. The analysis of WTs usually requires high effort due to the complexity of the drivetrain and the varying operating conditions and therefore there is still need for research on effective and reliable CM techniques for WT monitoring. Thus the purpose of this paper is to investigate a blind and effective CM approach based on the Scattering Transform. Through the comparison with state of the art techniques, the proposed methodology is found more powerful to detect a fault on six validated WT datasets.

scholarly journals False Alarms Analysis of Wind Turbine Bearing System

2020 ◽  
Vol 12 (19) ◽  
pp. 7867 ◽  
Author(s):  
Ana María Peco Chacón ◽  
Isaac Segovia Ramírez ◽  
Fausto Pedro García Márquez
Keyword(s):  
Wind Speed ◽  
Condition Monitoring ◽  
Wind Farms ◽  
Data Partitioning ◽  
False Alarms ◽  
Monitoring And Control ◽  
Condition Monitoring Systems ◽  
Mining Centers ◽  
Critical Components ◽  
And Control

Wind turbines are complex systems that use advanced condition monitoring systems for analyzing their health status. The gearbox is one of the most critical components due to its elevated downtime and failure rate. Supervisory Control and Data Acquisition systems are employed in wind farms for condition monitoring and control in real time. The volume and variety of the data require novel and robust techniques for data analysis. The main novelty of this work is the development of a new modelling of the temperature curve of the gearbox bearing versus wind speed to detect false alarms. An approach based on data partitioning and data mining centers is employed. The wind speed range is divided into intervals to increase the accuracy of the model, where the centers are considered representative samples in the modelling. A method based on the alarm detection is developed and studied together with the alarms report provided by a real case study. The results obtained allow the identification of critical alarm periods outside the confidence interval. It is validated that the study of alarm identification, pre-filtered data, state variable, and output power contribute to the detection of the false alarms.

scholarly journals A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2801 ◽  
Author(s):  
Pinjia Zhang ◽  
Delong Lu
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Information Flow ◽  
Wind Turbines ◽  
Energy Flow ◽  
Global Climate ◽  
Rapid Development ◽  
Wind Farms ◽  
Flow Information ◽  
Comprehensive Survey

Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power grid. How to reduce the operation and the maintenance (O&M) cost of wind farms is an obstacle to its further promotion and application. To provide reliable condition monitoring and fault diagnosis (CMFD) for WTs, this paper presents a comprehensive survey of the existing CMFD methods in the following three aspects: energy flow, information flow, and integrated O&M system. Energy flow mainly analyzes the characteristics of each component from the angle of energy conversion of WTs. Information flow is the carrier of fault and control information of WT. At the end of this paper, an integrated WT O&M system based on electrical signals is proposed.

scholarly journals Assessment of Condition Monitoring Techniques for Offshore Wind Farms

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Edwin Wiggelinkhuizen ◽  
Theo Verbruggen ◽  
Henk Braam ◽  
Luc Rademakers ◽  
Jianping Xiang ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Wind Farm ◽  
Early Stage ◽  
Wind Farms ◽  
Added Value ◽  
Offshore Wind ◽  
Monitoring Systems ◽  
Power Performance ◽  
Offshore Wind Farms ◽  
Condition Monitoring Systems

This paper discusses the results of an extensive investigation to assess the added value of various techniques of health monitoring to optimize the maintenance procedures of offshore wind farms. This investigation was done within the framework of the EU funded Condition Monitoring for Offshore Wind Farms (CONMOW) project, which was carried out from 2002 to 2007. A small wind farm of five turbines has been instrumented with several condition monitoring systems and also with the “traditional” measurement systems for measuring mechanical loads and power performance. Data from vibration and traditional measurements, together with data collected by the turbine’s system control and data acquisition (SCADA) systems, have been analyzed to assess (1) if failures can be determined from the different data sets; (2) if so, if they can be detected at an early stage and if their progress over time can be monitored; and (3) if criteria are available to assess the component’s health. Several data analysis methods and measurement configurations have been developed, applied, and tested. This paper first describes the use of condition monitoring if condition based maintenance is going to be applied instead of only scheduled and corrective maintenance. Second, the paper describes the CONMOW project and its major results, viz., the assessment of the usefulness and capabilities of condition monitoring systems, including algorithms for identifying early failures. Finally, the economic consequences of applying condition monitoring systems have been quantified and assessed.

scholarly journals Condition Monitoring and Fault Diagnosis of Wind Turbines Gearbox Bearing Temperature Based on Kolmogorov-Smirnov Test and Convolutional Neural Network Model

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2248 ◽  
Author(s):  
Peng Guo ◽  
Jian Fu ◽  
XiYun Yang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Condition Monitoring ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Main Trend ◽  
Maintenance Cost ◽  
Monitoring Method

Wind turbine condition-monitoring and fault diagnosis have important practical value for wind farms to reduce maintenance cost and improve operating level. Due to the special distribution law of the operating parameters of similar turbines, this paper compares the instantaneous operation parameters of four 1.5 MW turbines with strong correlation of a wind farm. The temperature-power distribution of the gearbox bearings is analyzed to find out the main trend of the turbines and the deviations of individual turbine parameters. At the same time, for the huge amount of data caused by the increase of turbines number and monitoring parameters, this paper uses the huge neural network and multi-hidden layer of a convolutional neural network to model historical data. Finally, the rapid warning and judgment of gearbox bearing over-temperature faults proves that the monitoring method is of great significance for large-scale wind farms.

Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning

2021 ◽  
pp. 1748006X2098426
Author(s):  
Sergio Martin-del-Campo ◽  
Fredrik Sandin ◽  
Stephan Schnabel
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Dictionary Learning ◽  
Population Distribution ◽  
Wind Farms ◽  
Data Availability ◽  
Rolling Element Bearing ◽  
Data Sampling ◽  
Selection Of

We analyze vibration signals from wind turbines with dictionary learning and investigate the relation between dictionary distances and faults occurring in a wind turbine output shaft rolling element bearing and gearbox under different data and compute constraints. Dictionary learning is an unsupervised machine learning method for signal processing, which permits learning a set of signal-specific features that have been used to monitor the condition of rotating machines, including wind turbines. Dictionary distance is one such feature, and its effectiveness depends on an adequate selection of the dictionary learning hyperparameters and the data availability, which typically is constrained in condition monitoring systems for remotely located wind farms. Here we evaluate the characteristics of the dictionary distance feature under healthy and faulty conditions of the wind turbines using different options for the selection of the pretrained dictionary, the sparsity of the signal model which determines the compute requirements, and the interval between data samples. Furthermore, we compare the dictionary distance feature to the typical time-domain features used in condition monitoring. We find that the dictionary distance based feature of a faulty wind turbine deviates by a factor of two or more from the population distribution several weeks before the gearbox bearing fault was reported, using a data sampling interval as long as 24 h and a model sparsity as low as 2.5%.

scholarly journals Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2484
Author(s):  
Giovanni Rinaldi ◽  
Philipp R. Thies ◽  
Lars Johanning
Keyword(s):  
Renewable Energy ◽  
Condition Monitoring ◽  
Asset Management ◽  
Early Stage ◽  
Wind Farms ◽  
Industrial Condition ◽  
Offshore Wind ◽  
Current Status ◽  
Operation And Maintenance ◽  
Fault Diagnosis And Prognosis

Operation and maintenance constitute a substantial share of the lifecycle expenditures of an offshore renewable energy farm. A noteworthy number of methods and techniques have been developed to provide decision-making support in strategic planning and asset management. Condition monitoring instrumentation is commonly used, especially in offshore wind farms, due to the benefits it provides in terms of fault identification and performance evaluation and improvement. Incorporating technology advancements, a shift towards automation and digitalisation is taking place in the offshore maintenance sector. This paper reviews the existing literature and novel approaches in the operation and maintenance planning and the condition monitoring of offshore renewable energy farms, with an emphasis on the offshore wind sector, discussing their benefits and limitations. The state-of-the-art in industrial condition-based maintenance is reviewed, together with deterioration models and fault diagnosis and prognosis techniques. Future scenarios in robotics, artificial intelligence and data processing are investigated. The application challenges of these strategies and Industry 4.0 concepts in the offshore renewables sector are scrutinised, together with the potential implications of early-stage project integration. The identified technologies are ranked against a series of indicators, providing a reference for a range of industry stakeholders.

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