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Technical Briefs

Wind Turbine Condition and Power Quality Monitoring by the Approach of Fast Individual Harmonic Extraction

[+] Author and Article Information
Wenxian Yang

School of Marine Science and Technology,
Newcastle University,
Newcastle upon Tyne, NE1 7RU, UK
e-mail: wxwyyang@yahoo.com

Chong H. Ng

National Renewable Energy Centre,
Blyth NE24 1LZ, UK

Jiesheng Jiang

Northwestern Polytechnical University,
Xi'an 710072, China
e-mail: jiangjs@nwpu.edu.cn

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received January 16, 2012; final manuscript received December 20, 2012; published online May 31, 2013. Assoc. Editor: Christian Masson.

J. Sol. Energy Eng 135(3), 034504 (May 31, 2013) (5 pages) Paper No: SOL-12-1015; doi: 10.1115/1.4023876 History: Received January 16, 2012; Revised December 20, 2012

Increasing deployment of wind turbines requires efficient condition monitoring to ensure the safety and availability of these machines. Grid code also requests the operator to enhance the monitoring of the quality of the power generated by the wind turbine. Most commercially available wind turbine condition monitoring systems are supported by a number of vibration transducers. They consequently are complex in hardware, expensive in price, but inefficient in computation and particularly lack of power quality monitoring ability. In view of this, an innovative electrical signal analysis-based wind turbine condition and power quality monitoring technique is developed in this paper by the approach of individual harmonic extraction. The proposed technique has been verified in the lab by applying to detecting both the mechanical and electrical faults emulated on a specially designed wind turbine condition monitoring test rig. Experiments show that the proposed technique is not only sensitive to the faults, but alert to the degradation of power quality.

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References

Figures

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Fig. 1

Raw signals in simulation experiment

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Fig. 3

Frequency shift detection

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Fig. 4

Harmonic distortion detection

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Fig. 5

Signals at fixed speed

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Fig. 6

FIHE results of the signals

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Fig. 7

FIHE results when the generator rotates at variable speeds

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Fig. 8

IVs when electrical asymmetric fault occurs in the WT generator. (a) IVs for the Id(t) and Iq(t) in Fig. 6(b) IVs for the Id(t) and Iq(t) in Fig. 7.

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Fig. 9

The detection of mechanical fault occurring in the WT generator (a) FIHE results and (b) calculated IVs

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