【摘要】：能源短缺是社會(huì)一大熱點(diǎn)問(wèn)題,伴隨著技術(shù)的發(fā)展,整個(gè)社會(huì)用電量不斷攀升。我國(guó)電機(jī)用電量占全國(guó)總電量的一半以上,而且電機(jī)控制系統(tǒng)運(yùn)行效率普遍比較低。對(duì)電機(jī)控制系統(tǒng)節(jié)能優(yōu)化運(yùn)行進(jìn)行研究有著巨大的經(jīng)濟(jì)效益和現(xiàn)實(shí)需求。永磁同步電機(jī)具有效率高、可靠性好等優(yōu)點(diǎn),可以滿足運(yùn)動(dòng)控制系統(tǒng)高效、節(jié)能的市場(chǎng)需求,現(xiàn)已成為節(jié)能控制系統(tǒng)的首選。永磁同步電機(jī)是一個(gè)非線性的機(jī)/電能量轉(zhuǎn)換系統(tǒng),其控制系統(tǒng)中存在如電流耦合、磁鏈飽和、參數(shù)變化等諸多干擾,這些干擾直接影響永磁同步電機(jī)控制系統(tǒng)的性能。論文研究提高永磁同步電機(jī)節(jié)能控制系統(tǒng)的性能以及魯棒性,有著非常高的理論和實(shí)踐意義。電機(jī)參數(shù)變化對(duì)控制系統(tǒng)產(chǎn)生干擾,論文由永磁同步電機(jī)參數(shù)在線估計(jì)著手。電機(jī)參數(shù)在線估計(jì)可分為動(dòng)態(tài)模型線性化和辨識(shí)算法兩部分。論文首先對(duì)永磁同步電機(jī)動(dòng)態(tài)模型線性化過(guò)程進(jìn)行改進(jìn),使用帕德線性化方法獲得更高精度的永磁同步電機(jī)動(dòng)態(tài)線性模型。然后,引入多新息遞推最小二乘算法對(duì)永磁同步電機(jī)參數(shù)進(jìn)行在線參數(shù)估計(jì),使用模糊控制算法改進(jìn)在線參數(shù)估計(jì)算法中遺忘因子大小選擇策略,使得永磁同步電機(jī)多參數(shù)在線估計(jì)在精度、速度和穩(wěn)定性等方面都有了較為明顯的提升。永磁同步電機(jī)常用的兩種節(jié)能控制策略為損耗模型法和輸入功率搜索法。損耗模型是根據(jù)電機(jī)參數(shù)對(duì)可控?fù)p耗(電氣損耗)建立的數(shù)學(xué)模型。控制器根據(jù)損耗模型計(jì)算得到電流參考值,損耗模型精度及魯棒性受電機(jī)參數(shù)影響。因此永磁同步電機(jī)參數(shù)在線估計(jì)的研究有助于提高損耗模型控制器的性能和魯棒性。本文使用上述在線參數(shù)估計(jì)算法實(shí)時(shí)估算電機(jī)參數(shù),把參數(shù)估計(jì)值帶入損耗模型控制器,獲得更準(zhǔn)確的電流參考值。在線參數(shù)估計(jì)降低了電機(jī)參數(shù)變化對(duì)損耗模型的干擾,增強(qiáng)了控制算法的魯棒性,實(shí)現(xiàn)了電機(jī)控制系統(tǒng)更高效的運(yùn)行。輸入功率搜索法是一種局部尋優(yōu)算法。搜索法通過(guò)檢測(cè)逆變器直流側(cè)功率,控制電機(jī)輸入電壓,最終找到電機(jī)輸入功率最低的穩(wěn)定工作點(diǎn)。輸入功率搜索控制策略存在收斂速度慢、轉(zhuǎn)矩波動(dòng)大等缺點(diǎn)。本文利用電機(jī)損耗模型計(jì)算輸入功率最低工作點(diǎn)存在區(qū)間,大大的降低了搜索法初始搜索范圍,進(jìn)而提高了算法收斂速度。最后文章在dSPACE半實(shí)物實(shí)驗(yàn)平臺(tái)上搭建了永磁同步電機(jī)基于損耗模型節(jié)能策略的控制系統(tǒng)。通過(guò)實(shí)驗(yàn)驗(yàn)證了在線參數(shù)估計(jì)算法對(duì)于損耗模型節(jié)能策略效率及魯棒性的提升。
[Abstract]:Energy shortage is a hot issue in society. With the development of technology, the electricity consumption of the whole society is rising. The electric power consumption in China accounts for more than half of the total electricity consumption in China, and the efficiency of motor control system is generally low. It has great economic benefit and realistic demand to study the energy saving optimization operation of motor control system. Permanent magnet synchronous motor (PMSM) has the advantages of high efficiency and good reliability. It can meet the market demand of high efficiency and energy saving of motion control system. It has become the first choice of energy-saving control system. Permanent magnet synchronous motor (PMSM) is a nonlinear mechanical / electrical energy conversion system. There are many disturbances in the control system such as current coupling, flux saturation, parameter change and so on. These disturbances directly affect the performance of PMSM control system. The research on improving the performance and robustness of PMSM energy-saving control system is of great theoretical and practical significance. The parameters of permanent magnet synchronous motor (PMSM) are estimated on line. The on-line estimation of motor parameters can be divided into two parts: dynamic model linearization and identification algorithm. Firstly, the linearization process of PMSM dynamic model is improved, and the PMSM dynamic linear model with higher precision is obtained by using Pad linearization method. Then, multi-innovation recursive least square algorithm is introduced to estimate the parameters of PMSM on line, and fuzzy control algorithm is used to improve the selection strategy of forgetting factor in the on-line parameter estimation algorithm. It can improve the precision, speed and stability of PMSM. Loss model method and input power search method are two common energy saving control strategies for permanent magnet synchronous motor (PMSM). The loss model is a mathematical model based on the parameters of the motor to control the loss (electrical loss). The controller calculates the current reference value according to the loss model. The precision and robustness of the loss model are affected by the motor parameters. Therefore, the on-line parameter estimation of PMSM is helpful to improve the performance and robustness of the loss model controller. In this paper, the on-line parameter estimation algorithm is used to estimate the motor parameters in real time, and the parameter estimation value is brought into the loss model controller to obtain more accurate current reference value. On-line parameter estimation reduces the disturbance of motor parameters to the loss model, enhances the robustness of the control algorithm, and realizes the more efficient operation of the motor control system. Input power search is a local optimization algorithm. By detecting the DC side power of the inverter and controlling the input voltage of the motor, the search method finally finds the stable working point with the lowest input power of the motor. The input power search control strategy has some disadvantages, such as slow convergence speed and high torque fluctuation. In this paper, the minimum operating point of input power is calculated by using the motor loss model, which greatly reduces the initial search range of the search method and improves the convergence speed of the algorithm. At last, the control system of PMSM based on loss model is built on the dSPACE hardware-in-the-loop experimental platform. Experimental results show that the online parameter estimation algorithm can improve the efficiency and robustness of the energy saving strategy of the loss model.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP273;TM341

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