Research Papers

Dynamic Optimization of Drivetrain Gear Ratio to Maximize Wind Turbine Power Generation—Part 2: Control Design

[+] Author and Article Information
Dongmei Chen

e-mail: dmchen@me.utexas.edu
Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712

Performed on a MS Windows-based 64-bit system with an Intel Core i7-950 processor and 12.0 GB of RAM.

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received July 17, 2011; final manuscript received April 5, 2012; published online October 30, 2012. Assoc. Editor: Alexander Leonessa.

J. Dyn. Sys., Meas., Control 135(1), 011017 (Oct 30, 2012) (10 pages) Paper No: DS-11-1213; doi: 10.1115/1.4006886 History: Received July 17, 2011; Revised April 05, 2012; Accepted April 24, 2012

The cost of electrical power produced by small wind turbines impedes the use of this technology, which can otherwise provide power to millions of homes in rural regions worldwide. To encourage their use, small wind turbines must capture wind energy more effectively while avoiding increased equipment costs. A variable ratio gearbox (VRG) can provide this capability to the simple fixed-speed wind turbine through discrete operating speeds. This is the second of a two-part publication that focuses on the control of a VRG-enabled wind turbine. The first part presented a 100 kW fixed speed, wind turbine model, and a method for manipulating the VRG and mechanical brake to achieve full load operation. In this study, an optimal control algorithm is developed to maximize the power production in light of the limited brake pad life. Recorded wind data are used to develop a customized control design that is specific to a given site. Three decision-making modules interact with the wind turbine model developed in Part 1 to create possible VRG gear ratio (GR) combinations. Dynamic programming is applied to select the optimal combination and establish the operating protocol. The technique is performed on 20 different wind profiles. The results suggest an increase in wind energy production of nearly 10%.

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Fig. 2

Modified rules used with program optimization

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Fig. 1

Power curve for VRG, defining regions 2′ and 3′

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Fig. 3

Wind turbine decision-making model

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Fig. 4

Decision-making structure for preliminary programming

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Fig. 5

Example of a valid (top) and an invalid (bottom) VRG combination

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Fig. 6

Dynamic programming algorithm for computing the cost at step k

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Fig. 7

Plot showing total energy produced (top), brake wear (middle), and cost (bottom) for site 16577 (set no. 16) for weight, α = 0.960

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Fig. 8

Technique for finding optimal VRG combination, and weight variable, α, that will be used to operate the wind turbine

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Fig. 9

Surface plot showing total cost (top) and total energy (bottom) as a function of α for each of the 575,413 gear combination for site 16577 (set no. 16)

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Fig. 10

Plot showing wind speed (top), additional energy produced by the VRG (bottom, left axis) and cumulative brake pad used (bottom, right axis) over a three-year period, for site 21291 (set no. 14), programmed with a weight coefficient, α = 0.980

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Fig. 11

Results for site 21291 (set no. 14) with weight, α = 0.980, showing VRG power curve (top), gear data used for control (upper–middle), power dissipated (lower–middle), and turbine speed (bottom)

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Fig. 12

Optimal VRG ratios for each set of wind data




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