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Research Papers

Filter-Based Fault Diagnosis of Wind Energy Conversion Systems Subject to Sensor Faults

[+] Author and Article Information
Jianhua Zhang

State Key Laboratory of Alternate Electrical Power
System With Renewable Energy Sources,
North China Electric Power University,
Beijing 102206, China

Jing Xiong

Zhuhai Power Supply Bureau,
Zhuhai 519000, China

Mifeng Ren

College of Information Engineering,
Taiyuan University of Technology,
Taiyuan 030024, China
e-mail: renmifeng@126.com

Yuntao Shi

Key Lab of Field Bus and
Automation of Beijing,
North China University of Technology,
Beijing 100144, China

Jinliang Xu

The Beijing Key Laboratory of New
and Renewable Energy,
North China Electric Power University,
Beijing 102206, China

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received April 28, 2015; final manuscript received December 21, 2015; published online April 1, 2016. Assoc. Editor: Ryozo Nagamune.

J. Dyn. Sys., Meas., Control 138(6), 061008 (Apr 01, 2016) (10 pages) Paper No: DS-15-1195; doi: 10.1115/1.4032827 History: Received April 28, 2015; Revised December 21, 2015

The operational reliability of wind energy conversion systems (WECSs) has attracted a lot of attention recently. This paper is concerned with sensor fault detection (FD) and isolation problems for variable-speed WECSs by using a novel filtering method. A physical model of WECS with typical sensor faults is first built. Due to the non-Gaussianity of both wind speed and measurement noises in WECSs, an improved entropy optimization criterion is then established to design the filter for WECSs. Different from previous entropy-filtering results, the generalized density evolution equation (GDEE) is adopted to reveal the relationship among the estimation error, non-Gaussian noises, and the filter gain. The sensors FD and isolation algorithms are then obtained by evaluating the decision rule based on the residual signals generated by the filter. Finally, simulation results show that the sensor faults in WECSs can be detected and isolated effectively by using the proposed method.

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Figures

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Fig. 1

Structure of the wind power system

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Fig. 2

Structure of sensor FD and isolation

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Fig. 3

Wind speed and measurement noise

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Fig. 4

(a) Performance index, (b) state estimation errors, and (c) filter gain

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Fig. 5

PDFs of the state estimation errors

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Fig. 6

(a) Residuals and (b) decision function of the sensor 1 fault

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Fig. 7

(a) Residuals and (b) decision function of the sensor 2 fault

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Fig. 8

(a) Residuals and (b) decision function of the sensor 3 fault

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