계통연계 인버터의 전력품질 향상을 위한 이산 공간벡터 변조 방식 기반의 향상된 모델 예측 제어 기법
- 주제(키워드) 전력전자 , 모델예측제어
- 발행기관 아주대학교
- 지도교수 이교범
- 발행년도 2018
- 학위수여년월 2018. 2
- 학위명 석사
- 학과 및 전공 일반대학원 전자공학과
- 실제URI http://www.dcollection.net/handler/ajou/000000026975
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
This paper proposes an improved model predictive current control (MPC) based on discrete space vector modulation (DSVM) to achieve high performance of output current waveforms and a fixed switching frequency. The conventional finite set model predictive control (FS-MPC) has several disadvantages such as large current ripples, variable switching frequency, and excessive acoustic noise. To overcome these problems, the proposed method adapts the DSVM strategy. The proposed DSVM strategy enables to obtain a large number of virtual voltage vectors without complex initialization process or lookup table, which are the drawbacks of the conventional DSVM strategies. To avoid the high computational burden problem caused by a large amount of virtual voltage vectors, the proposed method minimizes the number of candidate voltage vectors by selecting the optimal region in the space vector diagram. Since the MPC and DSVM strategies are adapted in the proposed method, the fast dynamic response and high quality of the output currents are achieved. The simulation and experimental results were presented to verify the effectiveness and performance of the proposed method using a two-level grid-connected voltage source inverter.
more목차
CHAPTER I. INTRODUCTION 1
CHAPTER II. THREE-PHASE TWO-LEVEL GRID CONNECTED INVERTER 5
2.1 System Configuration 5
2.2 Current Control for Grid-Connected Inverter System 6
2.3 Control System Configuration 8
CHAPTER III. CONVENTIONAL MODEL PREDICTIVE CONTROL 9
3.1 System Model for Current Prediction 9
3.2 Control System Configuration 11
CHAPTER IV. PROPOSED MODEL PREDICTIVE CONTROL 12
4.1 Discrete space vector modulation 12
4.2 Calculation Reduction Technique 15
4.3 Control System Configuration 17
CHAPTER V. SIMULATION RESULTS 18
CHAPTER VI. EXPERIMENTAL RESULTS 23
CHAPTER VII. CONCLUSIONS 31
REFERENCES 32
