외란 관측기를 이용한 매트릭스 컨버터로 구동되는 유도전동기 구동장치의 성능개선
An Improved Induction Motor Drive fed by Matrix Converter using a Disturbance Observer
초록/요약
This thesis concerns the matrix converter as an alternative power converter for induction motor drives. The matrix converter is direct AC/AC converter with no DC-link. The lack of reactive components in the DC-link is one of the salient advantages of the matrix converter. Furthermore, the matrix converter features full four-quadrant operation and sinusoidal input currents. The output voltage is limited to 87% of the input voltage. The matrix converter needs nine bidirectional switches to connect two three-phase voltage systems in all possible combinations. Most scientific work about matrix converters has so far regarded the modulation and control of the converter. As for an induction motor drive, indirect space vector modulation strategies for the matrix converter are reviewed. To control the motor drives using matrix converter, direct torque control scheme using space vector modulation method (DTC-SVM) which enables to minimize torque ripple and obtain unity input power factor, while maintaining constant switching frequency is used. However, speed control performance is still influenced by the unmodeled uncertainties of the plant such as parameter variations and external load disturbances. Intensive research of the design of a robust stable speed controller against inherent uncertainties in the induction motor model has been performed [6-8]. Among them, a soft computing approach using a recurrent fuzzy neural network (RFNN) is proposed. However, a complicated RFNN structure and too many updated parameters as well as unknown design constants can lead to a computational burden and infeasible real time implementation techniques. Recently, many kinds of soft computing methods such as adaptive fuzzy logic, fuzzy neural networks, and recurrent fuzzy neural network have been developed in the field of AC machine control. The radial-basis function network (RBFN) is widely used as an universal approximator in the area of nonlinear mapping due to its performance despite a simple structure. The RBFN is architecture of the instar-outstar model and constructed with input, output and hidden layers of normalized Gaussian activation functions. The RBFN has been introduced as a possible solution to the real multivariate interpolation problem, because it can be used for universal approximator like fuzzy and neural systems. However, there must be a reconstruction error if the structure of the RBFN (the number of activation functions in the hidden layer) is not infinitely rich, and these errors are introduced into the closed-loop system and make the convergence time slow, and that, in the worst case, it can deteriorate the stability. To compensate for the reconstruction error, the method of using additional sliding-mode like compensating input term is widely used, and its gain is computed with the information of the bounding constant of the system uncertainty, which is difficult to obtain. In this thesis, a speed controller using the RBFN observer is proposed. The lumped uncertainties of the induction motor system including parameter variations, external load disturbances and unmodeled dynamics are approximated by the RBFN, and an additional robust control term is introduced to compensate for the reconstruction error instead of the rich number of rules and additional updated parameters. Control input and adaptive laws for the weights in the RBFN and the bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov. Simulation and experimental results are presented to verify the effectiveness and feasibility of the proposed control system.
more목차
제1장 서론 = 1
제2장 The Matrix Converter Topology = 6
2.1 오늘날의 AC드라이브 기술 = 6
2.2 양방향 전력 흐름 AC 드라이브로서의 매트릭스 컨버터 = 6
2.3 매트릭스 컨버터의 성능 = 8
2.3.1 출력 전압 = 8
2.3.2 스위치 스트레스 = 9
2.3.3 효율 = 11
2.3.4 결론 = 14
제3장 Control of Matrix Converter[11] = 15
3.1 기본 구조 및 제어방법 = 15
3.2 공간 벡터 변조 = 17
3.3 간접 변조 = 21
3.4 간접 공간 벡터 변조 (Indirect Space Vector Modulation) = 24
3.4.1 Space Vector Rectification (SVR) = 24
3.4.2 Space Vector Inversion (SVI) = 27
3.4.3 매트릭스 컨버터에 적용되는 간접 공간벡터 변조 = 30
제4장 DTC-SVM for Matrix Converter Drives = 33
4.1 매트릭스 컨버터 드라이브의 직접 토크 제어 = 33
4.2 매트릭스 컨버터의 개선된 직접토크제어-공간벡터변조 방법 = 36
4.3 기계 방정식 = 37
4.4 deadbeat을 이용한 개선된 직접 토크 제어 = 39
제5장 방사형 기저 함수망을 이용한 DTC-SVM의 속도 제어기 = 41
5.1 유도전동기의 동적 모델 = 41
5.2 방사형 기저 함수망 (Radial Basis Function Network; RBFN) = 43
5.3 방사형 기저 함수망을 이용한 속도 제어기 = 44
제6 장 시뮬레이터를 이용한 실험 = 47
제7장 결론 및 요약 = 52
참고문헌 = 53
