本文選題：雙饋電機　切入點：并網(wǎng)同步　出處：《電工技術(shù)學報》2016年05期

【摘要】：模型預測直接功率控制(MPDPC)以其原理簡單、動態(tài)響應快和控制目標靈活等優(yōu)點在并網(wǎng)變換器控制中得到了廣泛關(guān)注。它利用系統(tǒng)模型來預測下一時刻的有功功率和無功功率,通過枚舉比較得到使給定功率和反饋功率之間誤差最小的電壓矢量,相比傳統(tǒng)的基于矢量表的直接功率控制具有更好的動態(tài)和穩(wěn)態(tài)性能。為了進一步提高穩(wěn)態(tài)性能并降低采樣頻率,提出一種基于雙矢量的改進模型預測直接功率控制,即把一個控制周期分配給一個非零矢量和一個零矢量,通過優(yōu)化非零矢量的作用時間來進一步減小功率脈動。通過合理安排矢量作用順序甚至可以獲得比傳統(tǒng)MPDPC更低的開關(guān)頻率而且穩(wěn)態(tài)性能更好。在15 k W雙饋風力發(fā)電系統(tǒng)上的仿真結(jié)果表明,相比傳統(tǒng)MPDPC,所提出的雙矢量MPDPC能夠以更低的采樣頻率實現(xiàn)快速平滑的并網(wǎng)同步和靈活的有功、無功控制,并且開關(guān)頻率更低,具有較大的實用價值。
[Abstract]:Model Predictive Direct Power Control (MPDPC) is widely used in grid-connected converter control because of its simple principle, fast dynamic response and flexible control target. It uses system model to predict active and reactive power at the next moment. The voltage vector with minimum error between the given power and the feedback power is obtained by enumeration and comparison. Compared with the traditional direct power control based on vector table, it has better dynamic and steady-state performance. In order to further improve the steady-state performance and reduce the sampling frequency, an improved model based on double vectors is proposed to predict the direct power control. That is, to assign a control period to a nonzero vector and a zero vector, By optimizing the action time of nonzero vectors, the power pulsation can be further reduced. Even lower switching frequency and better steady-state performance than conventional MPDPC can be obtained by properly arranging the sequence of action of vectors. At 15kW doubly-fed wind power, the power pulsation can be further reduced. The simulation results on the power generation system show that, Compared with the traditional MPDPCs, the proposed dual-vector MPDPC can realize fast and smooth grid-connected synchronization and flexible active and reactive power control with lower sampling frequency, and the switching frequency is lower, which is of great practical value.
【作者單位】： 北方工業(yè)大學電力電子與電氣傳動北京市工程研究中心;
【基金】：國家自然科學基金(51207003、51577003) 北京市科技新星計劃(xx2013001)資助項目
【分類號】：TM614
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本文編號：1685312