4 research outputs found

    An Effective Model-Free Predictive Current Control for Synchronous Reluctance Motor Drives

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    The performances of a model predictive control algorithm largely depend on the knowledge of the system model. A model-free predictive control approach skips all the effects of parameters variations or mismatches, as well as of model nonlinearity and uncertainties. A finite-set model-free current predictive control is proposed in this paper. The current variations predictions induced by the eight base inverter voltage vectors are estimated by means of the previous measurements stored into lookup tables. To keep the current variations information up to date, the three current measurements due to the three most recent feeding voltages are combined together to reconstruct all the others. The reconstruction is performed by taking advantage of the relationships between the three different base voltage vectors involved in the process. In particular, 210 possible combinations of three-state voltage vectors can be found, but they can be gathered together in six different groups. A light and computationally fast algorithm for the group identification is proposed in this paper. Finally, the current reconstruction for the prediction of future steps is thoroughly analyzed. A compensation of the motor rotation effect on the input voltages is proposed, too. The control scheme is evaluated by means of both simulation and experimental evidences on two different synchronous reluctance motors

    Identification of Flux-Linkage and Torque for Permanent Magnet Synchronous Motor Considering Magnetic Saturation and Spatial Harmonics

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2022.2. ์„ค์Šน๊ธฐ.Permanent magnet synchronous motors(PMSM) have been widely used in industry thanks to the advantages such as high efficiency, torque density, and power density. In recent years, it becomes more often to use the stator and rotor core under saturated conditions even at rated current to increase the torque and power density. Thus, the effect of magnetic saturation, cross-coupling and spatial harmonics has been increased in many applications. This non ideal effect cannot be represented in the fixed parameter-based ideal model and many control algorithms considering the non ideal effect which can be represented based on the nonlinear magnetic model are proposed. Furthermore, to improve the performance of these control algorithms, a lot of research was conducted on flux-linkage identification considering the nonlinear magnetic model. However, in conventional flux-linkage identification methods, the magnetic saturation, cross-coupling and spatial harmonics were not fully considered. Especially, the harmonics of flux-linkage due to spatial harmonics were often neglected. In this study, the flux-linkage identification method including the flux-linkage variation according to both operating current and rotor position is proposed. At first, the voltage equation of PMSM is deduced to calculate the flux-linkage harmonics. Then, to experimentally acquire the electromotive force at every operating point, resonant current controller, discrete Fourier transform and inverter nonlinearity compensation were applied. Finally, the phase delay in the harmonic voltage reference is analyzed and compensated. By substituting the compensated harmonic voltage reference in the voltage equation, the flux-linkage including harmonic components is obtained. Also, the torque calculation method including the ripple components based on the identified flux-linkage is proposed. The torque equation of PMSM is induced from the energy conservative law considering the nonlinear magnetic model. This torque equation contains three components; i.e. cross product of stator current and flux-linkage, the inner product of stator current and partial derivative of flux-linkage according to rotor position, and partial derivative of magnetic energy stored in the motor according to rotor position. Since the magnetic energy stored in the permanent magnet under zero current condition is hardly known, the third component in the torque equation is difficult to calculate from the identified flux-linkage. In this study, it is revealed that the derivative of torque according to current can be obtained from the identified flux-linkage, although the torque itself cannot be calculated due to the third component. Thus, the torque can also be obtained by integrating the calculated derivative of torque. The initial torque value identification scheme is also proposed using the position control. Based on the initial value, the torque including the harmonic components is calculated through line integral. The identified torque is verified through comparison with the measured torque using a torque transducer. Finally, the validity of the identified flux-linkage map is verified using the motor simulation model implemented based on the identified flux-linkage map. Based on the assumption that the simulation result would be identical with the experiment result if the identified flux-linkage map is accurate, it is proposed to verify the identified flux-linkage through the current waveform comparison of the simulation and experiment while the same voltage is applied. Furthermore, it is shown that the identified flux-linkage map-based motor simulation model can broaden the possibility of simulation thanks to the improved simulation performance compared to the conventional simulation models.์˜๊ตฌ์ž์„ ๋™๊ธฐ ์ „๋™๊ธฐ๋Š” ๋†’์€ ํšจ์œจ๊ณผ ํ† ํฌ ๋ฐ ์ถœ๋ ฅ ๋ฐ€๋„๋ฅผ ๊ฐ€์ง€๊ธฐ ๋•Œ๋ฌธ์— ์—ฌ๋Ÿฌ ์‚ฐ์—… ๋ถ„์•ผ์—์„œ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ตœ๊ทผ ํ† ํฌ์™€ ์ถœ๋ ฅ ๋ฐ€๋„๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•ด ์ •๊ฒฉ ์ „๋ฅ˜์—์„œ๋„ ์ฒ ์‹ฌ์ด ํฌํ™”๋˜๋Š” ์˜์—ญ๊นŒ์ง€ ์‚ฌ์šฉ๋˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ์ด์— ๋”ฐ๋ผ, ์ „๋™๊ธฐ์˜ ๊ณ ์ •๋œ ์ œ์ •์ˆ˜ ๊ธฐ๋ฐ˜์˜ ์ด์ƒ์ ์ธ ๋ชจ๋ธ์—์„œ๋Š” ๊ณ ๋ ค๋˜์ง€ ์•Š์•˜๋˜ ์ž๊ธฐ ํฌํ™”(Magnetic saturation), ๊ต์ฐจ ๊ฒฐํ•ฉ(Cross-coupling)๊ณผ ๊ณต๊ฐ„ ๊ณ ์กฐํŒŒ(Spatial harmonics)์˜ ์˜ํ–ฅ์ด ๋‘๋“œ๋Ÿฌ์ง€๊ฒŒ ๋‚˜ํƒ€๋‚˜๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด ์˜ํ–ฅ๋“ค์„ ๊ณ ๋ คํ•œ ๋น„์„ ํ˜• ์ž๊ธฐ ๋ชจ๋ธ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜์—ฌ ์—ฌ๋Ÿฌ๊ฐ€์ง€ ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ์ด๋Ÿฌํ•œ ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ๋น„์„ ํ˜• ์ž๊ธฐ ๋ชจ๋ธ์„ ๊ณ ๋ คํ•œ ์ž์†์„ ์ถ”์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํ•˜๊ฒŒ ์ด๋ฃจ์–ด์กŒ๋‹ค. ํ•˜์ง€๋งŒ, ๊ธฐ์กด์— ์ œ์•ˆ๋œ ๋น„์„ ํ˜• ์ž๊ธฐ ๋ชจ๋ธ์„ ๊ณ ๋ คํ•˜์—ฌ ์ž์†์„ ์ถ”์ •ํ•˜๋Š” ์—ฐ๊ตฌ์—์„œ๋Š” ์ž๊ธฐ ํฌํ™”, ๊ต์ฐจ ๊ฒฐํ•ฉ๊ณผ ๊ณต๊ฐ„ ๊ณ ์กฐํŒŒ์˜ ์˜ํ–ฅ ์ค‘ ์ผ๋ถ€๋งŒ์„ ๋ฐ˜์˜ํ•œ ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•˜๋‹ค. ํŠนํžˆ ๊ณต๊ฐ„ ๊ณ ์กฐํŒŒ์— ์˜ํ•ด ์ƒ๊ธฐ๋Š” ์ž์†์˜ ๊ณ ์กฐํŒŒ ์„ฑ๋ถ„์— ๋Œ€ํ•œ ์ถ”์ •์ด ์ด๋ฃจ์–ด์ง€์ง€ ์•Š์•˜๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณต๊ฐ„ ๊ณ ์กฐํŒŒ์— ์˜ํ•œ ์ž์†์˜ ๊ณ ์กฐํŒŒ ์„ฑ๋ถ„์„ ํฌํ•จํ•˜์—ฌ ์ „๋ฅ˜ ์šด์ „์ ๊ณผ ํšŒ์ „์ž ์œ„์น˜์— ๋”ฐ๋ฅธ ์ž์†๋งต(Flux-linkage map)์„ ์ถ”์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋จผ์ € ์ž์†์˜ ๊ณ ์กฐํŒŒ ์„ฑ๋ถ„์„ ๊ณ„์‚ฐํ•  ์ˆ˜ ์žˆ๋„๋ก ์ „์•• ๋ฐฉ์ •์‹์„ ์œ ๋„ํ•˜์˜€๋‹ค. ๊ฐ ์ „๋ฅ˜ ์šด์ „์ ๊ณผ ํšŒ์ „์ž ์œ„์น˜์—์„œ์˜ ๊ธฐ์ „๋ ฅ ์ •๋ณด๋ฅผ ์‹คํ—˜์ ์œผ๋กœ ์ถ”์ถœํ•˜๊ธฐ ์œ„ํ•ด ๊ณต์ง„ ์ œ์–ด๊ธฐ(Resonant controller), ์ด์‚ฐ ํ‘ธ๋ฆฌ์— ๋ณ€ํ™˜(Discrete Fourier transform, DFT)๊ณผ ์ธ๋ฒ„ํ„ฐ ๋น„์„ ํ˜•์„ฑ ๋ณด์ƒ์„ ์ ์šฉํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๊ณ ์กฐํŒŒ ์ „์•• ์ง€๋ น์— ์ƒ๊ธฐ๋Š” ์‹œ์ง€์—ฐ์— ์˜ํ•œ ์œ„์ƒ ์˜ค์ฐจ๋ฅผ ๋ถ„์„ํ•˜๊ณ  ์ด๋ฅผ ๋ณด์ƒํ•˜์—ฌ ์‹ค์ œ ์ „๋™๊ธฐ์— ์ธ๊ฐ€๋œ ๊ธฐ์ „๋ ฅ์„ ๋ณต์›ํ•˜์˜€๋‹ค. ๋ณต์›๋œ ๊ธฐ์ „๋ ฅ์œผ๋กœ๋ถ€ํ„ฐ ๊ณ ์กฐํŒŒ๋ฅผ ํฌํ•จํ•œ ์ž์†๋งต์„ ๊ตฌํ•˜์˜€๋‹ค. ์ถ”์ •๋œ ์ž์†๋งต์„ ์ด์šฉํ•˜์—ฌ ๋ฆฌํ”Œ(Ripple) ์„ฑ๋ถ„์„ ํฌํ•จํ•œ ์ „๋™๊ธฐ ํ† ํฌ๋งต(Torque map)์„ ์ถ”์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋น„์„ ํ˜• ์ž๊ธฐ ๋ชจ๋ธ์„ ๊ณ ๋ คํ•˜๋ฉด ์—๋„ˆ์ง€ ๋ณด์กด ๋ฒ•์น™์œผ๋กœ๋ถ€ํ„ฐ ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ํ† ํฌ ๋ฐฉ์ •์‹์„ ํšŒ์ „์ž ๊ธฐ์ค€ ์ขŒํ‘œ๊ณ„์—์„œ ์œ ๋„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด ํ† ํฌ ๋ฐฉ์ •์‹์€ ๊ณ ์ •์ž ์ „๋ฅ˜์™€ ์‡„๊ต์ž์†์˜ ์™ธ์ (Cross product) ์„ฑ๋ถ„, ๊ณ ์ •์ž ์ „๋ฅ˜์™€ ์‡„๊ต์ž์†์˜ ํšŒ์ „์ž ์œ„์น˜์— ๋Œ€ํ•œ ํŽธ๋ฏธ๋ถ„(Partial derivative)์˜ ๋‚ด์ (Inner product) ์„ฑ๋ถ„๊ณผ ์ „๋™๊ธฐ์— ์ €์žฅ๋œ ์ž๊ธฐ ์—๋„ˆ์ง€์˜ ํšŒ์ „์ž ์œ„์น˜์— ๋Œ€ํ•œ ํŽธ๋ฏธ๋ถ„ ์„ฑ๋ถ„์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ด ์ค‘ ์•ž์˜ ๋‘ ํ•ญ์€ ์ถ”์ •๋œ ์ž์†๋งต์œผ๋กœ๋ถ€ํ„ฐ ๊ณ„์‚ฐ์ด ๊ฐ€๋Šฅํ•˜์ง€๋งŒ ์ „๋™๊ธฐ์— ์ €์žฅ๋œ ์ž๊ธฐ ์—๋„ˆ์ง€๋Š” ์˜์ „๋ฅ˜ ์ƒํ™ฉ์—์„œ ์ž์„์— ์ €์žฅ๋œ ์—๋„ˆ์ง€๋ฅผ ์•Œ ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์— ๊ณ„์‚ฐ์ด ์–ด๋ ต๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด ํ† ํฌ ๋ฐฉ์ •์‹์œผ๋กœ๋ถ€ํ„ฐ ํ† ํฌ๋ฅผ ์ง์ ‘ ๊ณ„์‚ฐํ•˜๋Š” ๋Œ€์‹  ํ† ํฌ์˜ ์ „๋ฅ˜์— ๋Œ€ํ•œ ํŽธ๋ฏธ๋ถ„์„ ๊ณ„์‚ฐํ•จ์œผ๋กœ์จ ์„ธ ๊ฐœ์˜ ํ•ญ์„ ๋ชจ๋‘ ๊ณ ๋ คํ•œ ํ† ํฌ๋ฅผ ์ถ”์ •๋œ ์ž์†๋งต์„ ์ด์šฉํ•˜์—ฌ ์–ป์„ ์ˆ˜ ์žˆ์Œ์„ ๋ฐํžŒ๋‹ค. ๋˜ํ•œ ์ œ์•ˆ๋œ ํ† ํฌ ๊ณ„์‚ฐ ๋ฐฉ๋ฒ•์—์„œ ํ•„์š”ํ•œ ํ† ํฌ์˜ ์ดˆ๊ธฐ๊ฐ’์„ ์‹คํ—˜ ์ƒ์—์„œ ํ† ํฌ ์„ผ์„œ๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ณ  ์œ„์น˜ ์ œ์–ด๋ฅผ ํ†ตํ•ด ๊ตฌํ•˜์˜€๋‹ค. ์ด ์ดˆ๊ธฐ๊ฐ’์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜์—ฌ ์„ ์ ๋ถ„(Line integral)์„ ํ†ตํ•ด ๋ฆฌํ”Œ ์„ฑ๋ถ„์„ ํฌํ•จํ•œ ํ† ํฌ๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ์ถ”์ •๋œ ํ† ํฌ๋Š” ํ† ํฌ ์„ผ์„œ๋ฅผ ์ด์šฉํ•ด ์ธก์ •๋œ ๊ฒฐ๊ณผ์™€์˜ ๋น„๊ต๋ฅผ ํ†ตํ•ด ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ถ”์ •๋œ ์ž์†๋งต์˜ ํƒ€๋‹น์„ฑ์€ ์ถ”์ •๋œ ์ž์†๋งต์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ตฌํ˜„๋œ ์ „๋™๊ธฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ฒ€์ฆ๋˜์—ˆ๋‹ค. ์ถ”์ •๋œ ์ž์†์ด ์ •ํ™•ํ•˜๋‹ค๋ฉด ์‹คํ—˜ ๊ฒฐ๊ณผ์™€ ๋™์ผํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์„ ์ˆ˜ ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์ „์••์› ์ธ๊ฐ€ ์ƒํ™ฉ์—์„œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์‹คํ—˜์˜ ์ „๋ฅ˜ ํŒŒํ˜•์„ ๋น„๊ตํ•˜์—ฌ ์ถ”์ •๋œ ์ž์†๋งต์„ ๊ฒ€์ฆํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ตฌํ˜„๋œ ์ „๋™๊ธฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์—ฌ ๊ธฐ์กด์˜ ์ „๋™๊ธฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ชจ๋ธ๋“ค์— ๋น„ํ•ด ์—ฌ๋Ÿฌ ์ œ์–ด ์ƒํ™ฉ์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ•˜๋Š” ์„ฑ๋Šฅ์„ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค.์ œ 1์žฅ ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ์˜ ๋ชฉ์  7 1.3 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 8 ์ œ 2์žฅ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ 9 2.1 ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ํŠน์„ฑ 9 2.1.1 ์‹œํ—˜์šฉ ์ „๋™๊ธฐ์˜ ํŠน์„ฑ 9 2.1.2 ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ์ž๊ธฐ ๋ชจ๋ธ 17 2.1.2.1 ์ด์ƒ์ ์ธ ์ž๊ธฐ ๋ชจ๋ธ 17 2.1.2.2 ์ž๊ธฐ ํฌํ™”๋ฅผ ๊ณ ๋ คํ•œ ์ž๊ธฐ ๋ชจ๋ธ 19 2.1.2.3 ๊ณต๊ฐ„ ๊ณ ์กฐํŒŒ์™€ ์ž๊ธฐ ํฌํ™”๋ฅผ ๊ณ ๋ คํ•œ ์ž๊ธฐ ๋ชจ๋ธ 22 2.2 ๊ณ ์ •์ž ์‡„๊ต์ž์† ์ถ”์ •์— ๋Œ€ํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ 25 2.3 ํ† ํฌ ๋ฆฌํ”Œ ์ถ”์ •์— ๋Œ€ํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ 30 ์ œ 3์žฅ ์ œ์•ˆ๋œ ์ž์†๋งต ์ถ”์ • ๋ฐฉ๋ฒ• [66] 33 3.1 ์‹คํ—˜ ํ™˜๊ฒฝ 34 3.1.1 ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์— ์‚ฌ์šฉ๋˜๋Š” ์‹คํ—˜ ์„ธํŠธ ๊ตฌ์„ฑ 34 3.1.2 ๊ฒ€์ฆ์„ ์œ„ํ•œ ํ† ํฌ ์ธก์ •์— ์‚ฌ์šฉ๋˜๋Š” ์‹คํ—˜ ์„ธํŠธ ๊ตฌ์„ฑ 35 3.2 ์ „์•• ๋ฐฉ์ •์‹์„ ์ด์šฉํ•œ ๊ณ ์ •์ž ์‡„๊ต์ž์† ๊ณ„์‚ฐ 36 3.2.1 ์ˆ˜์‹ ์ „๊ฐœ ๊ณผ์ • 37 3.2.2 FEA๋ฅผ ์ด์šฉํ•œ ์ œ์•ˆ๋œ ๊ณ„์‚ฐ ๋ฐฉ๋ฒ•์˜ ๊ฒ€์ฆ 40 3.3 ์‹คํ—˜์„ ํ†ตํ•œ ๊ธฐ์ „๋ ฅ ์ถ”์ • 44 3.3.1 ๊ณต์ง„ ์ „๋ฅ˜ ์ œ์–ด๊ธฐ 44 3.3.2 DFT๋ฅผ ์ด์šฉํ•œ ๊ณ ์กฐํŒŒ ์ „์•• ์ง€๋ น ์ €์žฅ 53 3.3.3 ์ธ๋ฒ„ํ„ฐ ๋น„์„ ํ˜•์„ฑ์— ์˜ํ•œ ์ „์•• ์™œ๊ณก ๋ณด์ƒ 57 3.3.4 ์‹คํ—˜์„ ํ†ตํ•œ ๊ธฐ์ „๋ ฅ ์ถ”์ • ๊ฒฐ๊ณผ 62 3.4 ๊ธฐ์ „๋ ฅ ๊ธฐ๋ฐ˜์˜ ์ž์†๋งต ๋ณต์› 66 3.4.1 ๊ธฐ๋ณธํŒŒ ์ „์•• ํ•ฉ์„ฑ์˜ ์˜ค์ฐจ ๋ฐ ๋ณด์ƒ ๋ฐฉ๋ฒ• 66 3.4.2 ๊ณ ์กฐํŒŒ ์ „์•• ํ•ฉ์„ฑ์—์„œ์˜ ์˜ค์ฐจ ๋ฐ ๋ณด์ƒ ๋ฐฉ๋ฒ• 78 3.4.2.1 ์ •์ƒ๋ถ„ ๊ณ ์กฐํŒŒ์—์„œ ์‹œ์ง€์—ฐ์˜ ์˜ํ–ฅ 78 3.4.2.2 ์—ญ์ƒ๋ถ„ ๊ณ ์กฐํŒŒ์—์„œ ์‹œ์ง€์—ฐ์˜ ์˜ํ–ฅ 85 3.4.2.3 ๊ณ ์กฐํŒŒ ์ „์•• ์ง€๋ น์—์„œ์˜ ์‹œ์ง€์—ฐ ๋ณด์ƒ 89 3.5 ์‹คํ—˜์ ์œผ๋กœ ์ถ”์ •๋œ ์ž์†๋งต 93 ์ œ 4์žฅ ์ž์†๋งต ๊ธฐ๋ฐ˜์˜ ์ „๋™๊ธฐ ํ† ํฌ ์ถ”์ • 96 4.1 ์ „๋™๊ธฐ์˜ ํ† ํฌ ๋ฐฉ์ •์‹ 96 4.1.1 ์—๋„ˆ์ง€ ๋ณด์กด ๋ฒ•์น™ 97 4.1.2 3์ƒ ์ „๋™๊ธฐ์—์„œ์˜ ํ† ํฌ ๋ฐฉ์ •์‹ 98 4.1.3 FEA ๊ธฐ๋ฐ˜์˜ ํ† ํฌ ๋ฐฉ์ •์‹ ๊ฒ€์ฆ 101 4.2 ์„ ์ ๋ถ„์„ ์ด์šฉํ•œ ํ† ํฌ ๊ณ„์‚ฐ ๋ฐฉ๋ฒ• [79] 104 4.2.1 ์Šค์นผ๋ผ์™€ ๋ฒกํ„ฐ์˜ ํŽธ๋ฏธ๋ถ„ [80] 105 4.2.2 ํ† ํฌ์˜ ์ „๋ฅ˜์— ๋Œ€ํ•œ ํŽธ๋ฏธ๋ถ„ 106 4.2.3 FEA ๊ธฐ๋ฐ˜์˜ ์ œ์•ˆ๋œ ํ† ํฌ ๊ณ„์‚ฐ์‹ ๊ฒ€์ฆ 108 4.3 ํ† ํฌ ์ดˆ๊ธฐ๊ฐ’์˜ ์‹คํ—˜์  ์ถ”์ • 114 4.4 ์ œ์•ˆ๋œ ํ† ํฌ ์ถ”์ • ๋ฐฉ๋ฒ•์˜ ๊ฒ€์ฆ 117 ์ œ 5์žฅ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ์‹คํ—˜ ๊ฒฐ๊ณผ 124 5.1 ์ถ”์ •๋œ ์ž์†๋งต์˜ ๊ฒ€์ฆ 124 5.1.1 FEA์—์„œ ์–ป์€ ์ž์†๋งต๊ณผ ์ถ”์ •๋œ ์ž์†๋งต์˜ ๋น„๊ต 125 5.1.2 ํ‰๊ท  ํ† ํฌ๋ฅผ ์ด์šฉํ•œ ๊ฒ€์ฆ 128 5.1.3 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ชจ๋ธ์„ ์ด์šฉํ•œ ๊ฒ€์ฆ 131 5.1.3.1 ๊ณผ๋„์ƒํƒœ ์‘๋‹ต 140 5.1.3.2 ์ •์ƒ์ƒํƒœ ์‘๋‹ต 151 5.2 ์ถ”์ •๋œ ์ž์†๋งต์˜ ํ™œ์šฉ 157 5.2.1 ์ „๋ฅ˜ ์ œ์–ด 159 5.2.2 ์—ญ๊ธฐ์ „๋ ฅ ๊ธฐ๋ฐ˜ ์„ผ์„œ๋ฆฌ์Šค ์ œ์–ด 174 5.2.3 ์•ฝ์ž์† ์ œ์–ด 185 ์ œ 6์žฅ ๊ฒฐ๋ก  ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ 194 ์ฐธ๊ณ  ๋ฌธํ—Œ 199 Abstract 204๋ฐ•

    Design and Optimise Synchronous Reluctance Machines in Sensorless Control

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    This thesis researches the design and fabrication of axially laminated, anisotropic synchronous reluctance rotors with a high saliency ratio and low torque ripple for both three- and five-phase machines. One clear novelty of the research is the first method reported that allows skew to be incorporated in the axially laminated anisotropic (ALA) rotor. The designed rotor is then built with the help of the 3D printing technique which significantly reduces the complexity of the prototyping and fabrication process. The thesis then considers the control necessary including sensorless control schemes for the three- and five-phase synchronous reluctance motors with varying levels of skew. The performance and the effectiveness of the sensorless controllers are verified by experiments for all designed rotors under the three- and five-phase excitation. The three- and five-phase system of the synchronous reluctance motor is first discussed with the stator voltage equations and equivalent circuits. The d-and q-axis inductances are evaluated using finite element analysis. Finite element analysis (FEA) is a common used method in simulating and solving the electrical engineering problems. The FEA shows that for both three- and five-phase motors, the saliency ratio can reach around 10. Further detailed optimization is performed based on the rotor barrier dimensions such as shape, arc length, rib and bridge length, width, and the number of barriers. The final designed rotor in this research is an ALA rotor with 4 poles and 9 layers of magnetic segments. The barrier used is the round-type. The experimental inductances are shown to match the FEA predictions. The method of fabricating an ALA-type rotor with skew is a significant advance in this research. The FEA analysis for both three- and five-phase motor shows that torque ripple can be significantly reduced with the skew process: for instance, the 5.5ยฐ skewed rotor and the 9.5ยฐ skewed rotor are predicted to offer the best choice for the three- and five-phase stator designs from a parametric study of skew angles. Three skewed rotors are fabricated (5.5 o, 6 o and 9.5o). The experimental results of the torque ripple are compared. For the three-phase case, the rotors with skew shows a good reduction in torque ripple, the 6ยฐ skewed rotor performs better than the 5.5ยฐ skewed rotor. For the five-phase case, the 9.5ยฐ skewed rotor provides the best torque ripple reduction experimentally. The sensorless control is achieved for both three- and five-phase systems. According to the speed demand, the high-frequency injection sensorless control is used when the speed is below 500rpm. To further reduce the transient error, two different sliding mode observer methods are used for three and five-phase systems when the speed demand is above 500rpm. For both three- and five-phase synchronous reluctance motors with non-skewed and skewed rotors, the sensorless control can be successfully implemented. The transient and steady-state errors are all controlled in an acceptable range. By suddenly adding full load at rated and zero speed, the effectiveness of the sensorless control is also verified
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