Model Predictive Control Using Multi-Step Prediction Model for Electrical Yaw System of Horizontal-Axis Wind Turbines

Song, Dongran; Li, Qingan A.; Cai, Zili; Li, Li; Yang, Jian; Su, Mei; Joo, Young Hoon

doi:10.1109/tste.2018.2878624

Cited by 58 publications

(58 citation statements)

References 26 publications

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“…When the turbines regress more quickly, the wind farm requires more LIDAR devices to combat the yaw error problem, which increases the investment costs for LIDAR and lowers the returns. These results are a good way to understand the outcome of other studies such as previous studies discussed earlier where the focus is on optimizing the yaw error control algorithm. A 2‐year yaw error regression means the yaw error control algorithm operates better and loses its calibration more slowly.…”

Section: Optimizing Wind Farm Lidar Usementioning

confidence: 62%

“…The first approach attempts to optimize the control algorithm that uses the wind speed and direction data from cup and vane anemometer (and sometimes a meteorological mast in the wind farm) and processes the data in order to overcome the bias in the yaw system controller that causes the yaw error. This approach is discussed in detail in previous studies …”

Section: Introductionmentioning

confidence: 99%

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Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications

Bakhshi

Sandborn

2020

Wind Energy

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Wind energy is an important source of renewable energy with significant untapped potential around the world. However, the cost of wind energy production is high, and efforts to lower the cost of energy generation will help enable more widespread use of wind energy. Yaw error reduces the efficiency of turbines as well as lowers the reliability of key components in turbines. Light detection and ranging (LIDAR) devices can correct the yaw error; however, they are expensive, and there is a tradeoff between their costs and benefits. In this study, a stochastic discrete-event simula-

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Section: Optimizing Wind Farm Lidar Usementioning

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications

Bakhshi

Sandborn

2020

Wind Energy

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“…Based on Equations (12)-(18), the shape factor k i (H, k 0 ) can be expressed as a function of hub height and k 0 , and the scale factor c i (H, k 0 , v m ) depends on the hub height, k 0 , and mean wind speed v mi . From Reference (18), it was gathered that the wind speed extracted by turbine i is a function of the distance x between the installation sites of two turbines and the rotor diameter D or rotor radius, so the scale factor c i can be expressed as…”

Section: Weibull Probability Density Distribution Of Offshore Wind Stmentioning

confidence: 99%

“…Recently, researchers have shown great attention to the WTDO, studying three approaches. One approach is to use optimum control algorithms in which Lidar-enhanced control [15] and model predictive control methods [16] are proposed for the torque system, and one-step model predictive control [17] and multi-step model predictive control methods [18] are proposed for the yaw system. Another approach is to optimize the blade shape, such as by an adaptive blade concept for large-scale turbines [19], and the lifting surface method, for airfoils aerodynamic shape optimization [20], so that the wind turbine can gain high energy harvesting efficiency [21].…”

Section: Introductionmentioning

confidence: 99%

Minimizing the Energy Cost of Offshore Wind Farms by Simultaneously Optimizing Wind Turbines and Their Layout

et al. 2019

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The construction and gradual installation of turbines on wind farms has been hindered by the high cost of the energy production. An effective way to minimize energy costs is via the optimal design of wind turbines and their layout, but relevant and synthetic studies are lacking. This paper proposes a method to minimize the energy cost of offshore wind farms by simultaneously optimizing the rated wind speed, the rotor radius of wind turbines and their layout. Firstly, a new, mixed mathematical formulation of the energy cost is presented, considering the Weibull distribution for wind, the characterizing parameters of wind turbines and the distance between two turbines. Secondly, to obtain the minimum energy cost, a composite optimization algorithm was developed, which consists of an iterative method and an improved particle swarm optimization algorithm. The former was used to search the minimal energy costs that relate to the design parameters of a single wind turbine, while the latter was adopted for optimizing the layout of the wind turbines iteratively. Finally, the proposed method was applied to three case studies with variable wind speed and constant wind direction. Results of the case studies show that the reduced energy cost after optimization has a range of 0–0.001 $/kWh, which confirms the effectiveness of the proposed approach. Meanwhile, the layout of the wind turbines after optimization tends to locate the two wind turbines with the biggest spacing in the wind direction, which justifies the utilization of layout optimization. Furthermore, exploring the optimally designed parameters of wind turbines revealed that the wind farms with a high mean wind speed can have a wider range of turbine capacity than those with a low wind speed, which offers more freedom for the designers when constructing offshore wind farms at wind sites with rich wind resources.

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“…With the remarkable advantages of enhancing the comprehensive utilization of wind, photovoltaic, and other sources of energy while simultaneously reducing environmental pollution, widening application, and supplementing the existing power system, Distributed Generation (DG) has attracted an increasing amount of attention and research in recent years [1][2][3][4]. Microgrids can integrate the advantages of DG while overcoming shortcomings like instability.…”

Section: Introductionmentioning

confidence: 99%

A Reactive Power-Voltage Control Strategy of an AC Microgrid Based on Adaptive Virtual Impedance

et al. 2019

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As an effective carrier of distributed generation, a microgrid is an effective way to ensure that distributed power can be reasonably utilized. However, due to the property of line impedance and other factors in a microgrid, reactive power supplied by distributed generation units cannot be shared rationally. To efficiently improve reactive power sharing, this paper proposes a reactive power-voltage control strategy based on adaptive virtual impedance. This method changes the voltage reference value by adding an adaptive term based on the traditional virtual impedance. Meanwhile, a voltage recovery mechanism was used to compensate the decline of distributed generation (DG) output voltage in the process. MATLAB/Simulink simulations and experimental results show that the proposed controller can effectively improve the steady state performance of the active and reactive power sharing. Finally, the feasibility and effectiveness of the proposed control strategy were verified.Energies 2019, 12, 3057 2 of 15 this problem, various schemes based on conventional droop control have been proposed by domestic and overseas researchers [9][10][11][12][13][14][15][16][17]. Tuladhar et al. [9] proposed to overlay the harmonic signal onto the reference fundamental voltage, which can change the magnitude of fundamental voltage according to the harmonic power generated by DG units. However, this proposal results in output voltage distortion and power quality reduction, and the harmonic wave is enhanced by the inductive circuit of the microgrid. Zhang et al.[10] utilized feeder parameters. Proper power-sharing is achieved through modifying the reactive droop parameter. However, line resistance values are difficult to obtain accurately. The global variable of bus voltage and the integral element is introduced in Sao [11] and Zhong [12], loads can be accurately shared in a steady state, but bus voltage measurement requires communication over long distances. De Brabandere [13] proposed that a decoupling matrix is established according to the resistance and inductive feeder parameters, controlling active and reactive power respectively. However, feeder parameter errors are prone to appear, which affect power-sharing. Li [14] and Ma [15] proposed a virtual impedance equivalent method to a simultaneous increase of the output impedance of each DG unit, which is introduced to reduce circular current. In Wei [16] and Yu [17], system stability is enhanced while circular current is reduced by creating a virtual resistance or impedance. However, the virtual control methods mentioned above intensify line voltage drop and lower output voltage of the DG unit; therefore, the power quality cannot be guaranteed.Another method of solving the problem is to combine communicational and non-communicational droop control methods together. He and Li [18] adopted the disturbance thought, utilized reactive information caused by feeder parameter differences, and used active disturbance for reactive droop control through simultaneous communication to reach reaso...

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Model Predictive Control Using Multi-Step Prediction Model for Electrical Yaw System of Horizontal-Axis Wind Turbines

Cited by 58 publications

References 26 publications

Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications

Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications

Minimizing the Energy Cost of Offshore Wind Farms by Simultaneously Optimizing Wind Turbines and Their Layout

A Reactive Power-Voltage Control Strategy of an AC Microgrid Based on Adaptive Virtual Impedance

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