Wind Turbine Control Strategy at Lower Wind Velocity Based on Neural Network PID Control

Yao, Xingjia; Su, Xianbin; Tian, Lei

doi:10.1109/iwisa.2009.5073239

Cited by 13 publications

(8 citation statements)

References 1 publication

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“…Figure 11 shows the wind speed curves of 10 wind turbines on a feeder in the wind farms. The effect of the control strategy proposed in this paper is compared with that of the conventional capacity allocation algorithm and the maximum power allocation algorithm [23,24] by simulation. In the simulation, the reference from the superior control center is increased from 40 to 50 MW at 30 s. The reference tracking ability under the three control strategies is shown in Figure 12, and their corresponding inertia reserves of the wind farms (E W ) are shown in Figure 13.…”

Section: Verification Of Inertia Reserve Of Proposed Control Strategymentioning

confidence: 99%

Optimal Inertia Reserve and Inertia Control Strategy for Wind Farms

Cai

2020

Energies

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It is important to reduce the impact of the high penetration of wind power into the electricity supply for the purposes of the security and stability of the power grid. As such, the inertia capability of wind farms has become an observation index. The existing control modes cannot guarantee the wind turbine to respond to the frequency variation of the grid, hence, it may lead to frequency instability as the penetration of wind power gets much higher. For the stability of the power grid, a simple and applicable method is to realize inertia response by controlling wind farms based on a high-speed communication network. Thus, with the consideration of the inertia released by a wind turbine at its different operating points, the inertia control mechanism of a doubly-fed wind turbine is analyzed firstly in this paper. The optimal exit point of inertia control is discussed. Then, an active power control strategy for wind farms is proposed to reserve the maximum inertia under a given power output constraint. Furthermore, turbines in a wind farm are grouped depending on their inertia capabilities, and a wind farm inertia control strategy for reasonable extraction of inertia is then presented. Finally, the effectiveness of the proposed control strategy is verified by simulation on the RT-LAB (11.3.3, OPAL-RT TECHNOLOGIES, Montreal, Quebec, Canada) platform with detailed models of the wind farm.

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Section: Verification Of Inertia Reserve Of Proposed Control Strategymentioning

confidence: 99%

Optimal Inertia Reserve and Inertia Control Strategy for Wind Farms

Cai

2020

Energies

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“…Finally, the remaining desired states x 5d , x 6d and x 7d , which are the desired values of Ω r , Ω g , T h , are determined by solving the ordinary differential Equations (16)- (18). Recall that these equations are as follows,…”

Section: Remarkmentioning

confidence: 99%

“…For example, PID controllers and several modified PID control schemes were used to control wind energy conversion systems [17][18][19]. These types of schemes are believed to be the easiest and simplest design methods.…”

Section: Introductionmentioning

confidence: 99%

Feedback Linearization Controller for a Wind Energy Power System

2016

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This paper deals with the control of a doubly-fed induction generator (DFIG)-based variable speed wind turbine power system. A system of eight ordinary differential equations is used to model the wind energy conversion system. The generator has a wound rotor type with back-to-back three-phase power converter bridges between its rotor and the grid; it is modeled using the direct-quadrature rotating reference frame with aligned stator flux. An input-state feedback linearization controller is proposed for the wind energy power system. The controller guarantees that the states of the system track the desired states. Simulation results are presented to validate the proposed control scheme. Moreover, further simulation results are shown to investigate the robustness of the proposed control scheme to changes in some of the parameters of the system.

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“…The proportional-integral-derivative (PID) controller and several modified PID schemes have been widely developed to design the industrial control systems including wind turbines [15][16][17]. Although PID-based schemes are believed to be the easiest and simplest designing method, these kinds of schemes always lack a general effective tuning principle of the control coefficients ( , , and ) and are difficult to determine their stability regions.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…By substituting (16) into (18), combining (13), and considering the value of the gain (K ), it is not difficult to derive the following inequality:…”

Section: Robust Variable Structure Controller Designmentioning

confidence: 99%

Robust Fuzzy Control for Doubly Fed Wind Power Systems with Variable Speed Based on Variable Structure Control Technique

Zhang¹,

Wang²

2014

Mathematical Problems in Engineering

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The design of a variable structure sliding-mode controller (SMC) for a variable speed wind turbine with double-fed inductiongenerator, based on the fuzzy logic, is described in this paper. The purpose of this controller is to maximize the energy capture by operating the turbine at the optimal rotational speed as well as fast and stable generator response. The dynamics of both the turbine and the generator are modeled to exhibit their mechanical/electrical characteristics. Two global sliding-mode controllers, which eliminate the reaching phase of SMC and the sliding-mode motion starts from the beginning, are designed to guarantee the robust tracking of both the optimal blade-rotor speed and the reference generator torque/flux in the whole process, despite the parametric uncertainty and external disturbance. To reduce the adverse chattering effect of the conventional SMC, the adaptive fuzzy inference strategy is adopted to deduce the adjustable switch gain, instead of the fixed gains. Simulation results show that the proposed controller achieves global asymptotic tracking, satisfied torque/flux responses, and has better performance and higher utilization ratio of wind energy than the conventional feedback-linearization method.

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Wind Turbine Control Strategy at Lower Wind Velocity Based on Neural Network PID Control

Cited by 13 publications

References 1 publication

Optimal Inertia Reserve and Inertia Control Strategy for Wind Farms

Optimal Inertia Reserve and Inertia Control Strategy for Wind Farms

Feedback Linearization Controller for a Wind Energy Power System

Robust Fuzzy Control for Doubly Fed Wind Power Systems with Variable Speed Based on Variable Structure Control Technique

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