지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학위논문
- 저자정보
- 발행연도
- 2014
- 저작권
- 제주대학교 논문은 저작권에 의해 보호받습니다.
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초록· 키워드
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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 two methods for wind turbine pitch controller using neural
networks. The first method is that the difference between a rated and current
speed of generator has been used for another input of neural network
controller as well as wind speed. As a result, the generator speed and power
are maintained as the rated ones. However vibrations of pitch angle and
generator speed have been considered to be improved. And the second
methods is that the changing rate of generator speed has been used for
another input of neural network controller as well as the difference between
the rated and current generator speed. As a result, problems of the first
method have been improved. Error back-propagation algorithm is used for
training the neural network pitch controller and Matlab/Simulink is used to
confirm the performance of proposed method
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 two methods for wind turbine pitch controller using neural
networks. The first method is that the difference between a rated and current
speed of generator has been used for another input of neural network
controller as well as wind speed. As a result, the generator speed and power
are maintained as the rated ones. However vibrations of pitch angle and
generator speed have been considered to be improved. And the second
methods is that the changing rate of generator speed has been used for
another input of neural network controller as well as the difference between
the rated and current generator speed. As a result, problems of the first
method have been improved. Error back-propagation algorithm is used for
training the neural network pitch controller and Matlab/Simulink is used to
confirm the performance of proposed method
목차
제 1 장 서론 2제 2 장 풍력발전 시스템의 구성 52.1 Aerodynamics 52.2 Drive train 72.3 Generator 92.4 Controller 10제 3 장 신경망 피치제어기 123.1 피치제어를 위한 신경망 123.2 풍속과 발전기의 회전속도를 고려한 신경망 피치제어 143.2.1 알고리즘 143.2.2 훈련요소 163.3 발전기의 회전속도 변화율을 고려한 신경망 피치제어 183.3.1 알고리즘 183.3.2 훈련요소 20제 4 장 시뮬레이션 및 결과 224.1 풍력발전기의 Matlab/Simulink 모델 224.1.1 Aerodynamics model 234.1.2 Drive Train model 234.1.3 Generator model 244.1.4 Torque Controller model 244.1.5 Pitch actuator 254.2 신경망 제어기의 훈련을 위한 Matlab/Simulink 모델 264.3 신경망 피치제어시스템의 Matlab/Simulink 모델 314.3.1 풍속과 발전기의 회전속도를 고려한 신경망 피치제어시스템 314.3.2 발전기의 회전속도 변화율을 고려한 신경망 피치제어시스템 35제 5 장 결 론 39참고 문헌 41
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