• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1059-1065
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• 2014

Wind turbine system can obtain the maximum wind energy using torque control under the rated wind speed, and wind turbine power is controlled as the rated power using pitch control over the rated wind speed. In this paper, we present a method for wind turbine pitch controller using neural networks. The purpose of the pitch control is to control generator speed and power in the above rated wind speed. To improve the neural network pitch controller, the difference between a rated and current speed of generator has been used for another input of neural networks as well as wind speed. Error back-propagation algorithm is used for training the neural network pitch controller and simulation and Matlab/Simulink is used for verifying that this system is controlled well.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.366-371
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• 2014

The neural network pitch controller using changing rate of generator speed has been suggested in this paper to regulate wind turbine power above the rated wind speed. The changing rate of generator speed is used in the suggested pitch controller as well as the difference between the rated and current generator speed. Matlab/simulink has been used for simulations and it has been shown that the suggested pitch controller regulates generator speed as the rated speed of 122.9[rad/s].

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1624-1627
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• 2005

We proposed a design technique for auto-landing guidance and control system. This technique utilizes linear controller and neural network. Main features of this technique is to use conventional linear controller and compensate for the error coming from the model uncertainties and/or reference model mismatch. In this study, the multi-perceptron neural network with single hidden layer is adopted to compensate for the errors. Glide-slope capture logic for auto-landing guidance and control system is designed in this technique. From the simulation results, it is observed that the responses of velocity and pitch angle to commands are fairly good, which are directly related to control inputs of throttle and elevator, respectively.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.863-871
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• 2013

Control of pitch angle of turbine blades is among the controlling methods in the wind turbines; this measure is taken for managing mechanical power generated by wind turbine in different wind velocities. Taking into account the high significance of the power generated by wind turbine and due to the fact that better performance of pitch angle is followed by better quality of turbine-generated power, it is therefore crucially important to optimize the performance of this controller. In the current paper, a PI controller is primarily used to control the pitch angle, and then another controller is designed and replaces PI controller through applying a new strategy i.e. alternating two ADALINE neural networks. According to simulation results, performance of controlling system improves in terms of response speed, response ripple, and ultimately, steady tracing error. The highly significant feature of the proposed intelligent controller is the considerable stability against variations of wind velocity and system parameters.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.2
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• pp.142-149
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• 2004

This paper presents a neural network controller of a grid-connected wind energy conversion system for extracting maximum power from wind and a power controller to transfer the maximum power extracted into a utility grid. It discusses the modeling and simulation of the wind energy conversion system with the controllers, which consists of an induction generator, a transformer, a link of a rectifier, and an inverter. The paper describes tile drive train model, induction generator model and grid-interface model for dynamics analysis. Maximum power extraction is achieved by controlling the pitch angle of the rotor blades by a neural network controller. Pitch control method is mechanically complicated, but the control performance is better than that of the stall regulation. The simulation results performed on MATLAB show the variation of the generator torque, the generator rotor speed, the pitch angle, and real/reactive power injected into the grid, etc. Based on the simulation results, the effectiveness of the proposed controllers is verified.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.333-336
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• 2003

For long time, the takeoff and landing control of airship was worked by human handling. With the development of the autonomous control system, the exact controls during the takeoff and landing were required and lots of methods and algorithms were suggested. This paper presents the result of airship take-off and landing by buoyancy control using air ballonet volume change and performance control of pitch angle for stable flight within the desired altitude. For the complexity of airship's dynamics, firstly, simple PID controller was applied. Due to the various atmospheric conditions, this controller didn’t give satisfactory results. Therefore, new control method was designed to reduce rapidly the error between designed trajectory and actual trajectory by learning algorithm using an artificial neural network. Generally, ANN has various weaknesses such as large training time, selection of neuron and hidden layer numbers required to deal with complex problem. To overcome these drawbacks, in this paper, the RBFN (radial basis function network) controller developed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.9
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• pp.1065-1071
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• 2012

Wind energy is becoming one of the most preferable alternatives to conventional sources of electric power that rely on fossil fuels. For stable electric power generation, constant rotating speed control of a wind turbine is performed through pitch control and stall control of the turbine blades. Recently, variable pitch control has been implemented in modern wind turbines to harvest more energy at variable wind speeds that are even lower than the rated one. Although wind turbine pitch controllers are currently optimized using a step response via the Ziegler-Nichols auto-tuning process, this approach does not satisfy the requirements of variable pitch control. In this study, the variable pitch controller was optimized by a genetic algorithm using a neural network model that was constructed by the Latin Hypercube sampling method to improve the Ziegler-Nichols auto-tuning process. The optimized solution shows that the root mean square error, rise time, and settle time are respectively improved by more than 7.64%, 15.8%, and 15.3% compared with the corresponding initial solutions obtained by the Ziegler-Nichols auto-tuning process.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1040-1048
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• 2013

In this paper, a sensorless pitch angle control method for a wind generation system is suggested. One-step-ahead prediction control law is adopted to control the pitch angle of a wind turbine in order for electric output power to track target values. And it is shown that this control scheme using the inverse dynamics of the controlled system enables us to predict current wind speed without an anemometer, to a considerable precision. The inverse input-output of the controlled system is realized by use of an artificial neural network. The proposed control and wind speed prediction method is applied to a Double-Feed Induction Generation system connected to a simple power system through computer simulation to show its effectiveness. The simulation results demonstrate that the suggested method shows better control performances with less control efforts than a conventional Proportional-Integral controller.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.2
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• pp.84-91
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• 2005

For long time, the takeoff and landing control of airship was worked by human handling. With the development of the autonomous control system, the exact controls during the takeoff and landing were required and lots of methods and algorithms were suggested. This paper presents the result of airship take-off and landing by buoyancy control using air ballonet volume change and performance control of pitch angle for stable flight within the desired altitude. For the complexity of airship's dynamics, firstly, simple PID controller was applied. Due to the various atmospheric conditions, this controller didn't give satisfactory results. Therefore, new control method was designed to reduce rapidly the error between designed trajectory and actual trajectory by learning algorithm using an artificial neural network. Generally, ANN has various weaknesses such as large training time, selection of neuron and hidden layer numbers required to deal with complex problem. To overcome these drawbacks, in this paper, the RBFN (radial basis function network) controller developed. The weight value of RBFN is acquired by learning which to reduce the error between desired input output through and airship dynamics to impress the disturbance. As a result of simulation, the controller using the RBFN is superior to PID controller which maximum error is 15M.