Key Technology in Wind Turbine Transmission System Condition Monitoring: A Review

2013 ◽  
Vol 442 ◽  
pp. 304-310
Author(s):  
Hai Jiang Dong ◽  
Chun Hua Zhao ◽  
Shi Qing Wan ◽  
Xian You Zhong ◽  
Wei Wang
Keyword(s):  
Signal Processing ◽  
Data Acquisition ◽  
Wind Turbine ◽  
Programming Languages ◽  
Condition Monitoring ◽  
Transmission System ◽  
Monitoring Methods ◽  
State Recognition ◽  
Key Technology ◽  
Signal Processing Methods

Key technology for the transmission system of wind turbine condition monitoring is researched in this paper. It gives a summary overview, including monitoring methods, signal processing methods and state recognition technology, data acquisition and transmission technology, programming languages. Understanding of the progress of the field is to provide technical reference and research inspiration for insiders.

scholarly journals Performance Comparison of PD Data Acquisition Techniques for Condition Monitoring of Medium Voltage Cables

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4272 ◽  
Author(s):  
Muhammad Shafiq ◽  
Ivar Kiitam ◽  
Kimmo Kauhaniemi ◽  
Paul Taklaja ◽  
Lauri Kütt ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Condition Monitoring ◽  
Partial Discharge ◽  
Performance Comparison ◽  
Practical Implementation ◽  
Monitoring Methods ◽  
Medium Voltage ◽  
Cable Networks ◽  
Improved Performance ◽  
Diagnostic Capabilities

Already installed cables are aging and the cable network is growing rapidly. Improved condition monitoring methods are required for greater visibility of insulation defects in the cable networks. One of the critical challenges for continuous monitoring is the large amount of partial discharge (PD) data that poses constraints on the diagnostic capabilities. This paper presents the performance comparison of two data acquisition techniques based on phase resolved partial discharge (PRPD) and pulse acquisition (PA). The major contribution of this work is to provide an in-depth understanding of these techniques considering the perspective of randomness of the PD mechanism and improvements in the reliability of diagnostics. Experimental study is performed on the medium voltage (MV) cables in the laboratory environment. It has been observed that PRPD based acquisition not only requires a significantly larger amount of data but is also susceptible to losing the important information especially when multiple PD sources are being investigated. On the other hand, the PA technique presents improved performance for PD diagnosis. Furthermore, the use of the PA technique enables the efficient practical implementation of the continuous PD monitoring by reducing the amount of data that is acquired by extracting useful signals and discarding the silent data intervals.

scholarly journals Remote data acquisition for condition monitoring of wind turbines

2015 ◽  
Vol 6 (2) ◽  
pp. 10
Author(s):  
Bavo De Maré ◽  
Jacob Sukumaran ◽  
Mia Loccufier ◽  
Patrick De Baets
Keyword(s):  
Data Acquisition ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Good Condition ◽  
Offshore Wind ◽  
Monitoring Systems ◽  
Offshore Wind Turbines ◽  
Condition Monitoring Systems ◽  
Remote Data

While the number of offshore wind turbines is growing and turbines getting bigger and more expensive, the need for good condition monitoring systems is rising. From the research it is clear that failures of the gearbox, and in particular the gearwheels and bearings of the gearbox, have been responsible for the most downtime of a wind turbine. Gearwheels and bearings are being simulated in a multi-sensor environment to observe the wear on the surface.

scholarly journals Vibration Baseline, a Useful Concept for Condition Monitoring of Rotating Equipment

1985 ◽  
Author(s):  
J. Gorter ◽  
A. J. Klijn
Keyword(s):  
Signal Processing ◽  
Condition Monitoring ◽  
Gas Turbines ◽  
Transmission System ◽  
Centrifugal Compressors ◽  
Analysis Methods ◽  
Measuring Techniques ◽  
Rotating Equipment ◽  
Vibration Measuring

The condition of rotating equipment can be assessed using vibration measuring techniques. A number of these techniques including signal processing and analysis methods will be discussed briefly. Finally experiences on gas turbines and centrifugal compressors, operating in the Dutch gas transmission system, will be used to define a useful vibration baseline for condition monitoring, maintenance purposes and availability.

Study on novel signal processing and simultaneous-fault diagnostic method for wind turbine

2019 ◽  
Vol 41 (14) ◽  
pp. 4100-4113
Author(s):  
Xian-Bo Wang ◽  
Pu Miao ◽  
Kun Zhang ◽  
Xiaoyuan Zhang ◽  
Jun Wang
Keyword(s):  
Signal Processing ◽  
Wind Turbine ◽  
Extreme Learning Machine ◽  
Feature Learning ◽  
Mode Decomposition ◽  
Vibrational Signals ◽  
Fault Diagnostic ◽  
Diagnostic Framework ◽  
Learning Machine ◽  
Signal Processing Methods

High-precision fault diagnosis is important for the widely installed complex industrial product, the wind turbine. However, intelligent monitoring is difficult due to the fuzzy boundaries and individual different variations of the unseen single or simultaneous-fault of such intricate equipment. To solve this problem, this study proposes an ensemble fault diagnostic framework for simultaneous and coupling failure. First, this paper develops novel signal processing methods for effective feature learning and mapping from the non-stationary and nonlinear raw vibrational signals. The adapted variational mode decomposition is introduced based on the particle swarm optimization that applies the minimum mean envelope entropy to optimize the parameters settings. Second, the novel ensemble extreme learning machine-based network is proposed to isolate the faults that applies one extreme learning machine network to count the number of fault scenarios, and the other one to identify the specific single or simultaneous-fault labels. With this scheme, the self-adaptive ensemble extreme learning machine-based fault diagnostic framework is more accurate and faster than the prevailing probabilistic classifier-based methods, as the proposed method does not rely on empirically specified decision-making threshold and generates all the candidate fault labels at the same time. Finally, this study builds the test platform and compares the overall results with the existing feature analysis methods and classifiers. The experimental results verify that the proposed framework detects both single and simultaneous-fault accurately and quickly.

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