【摘要】：隨著風(fēng)電場并網(wǎng)容量的逐漸增加，其給電網(wǎng)帶來的不利影響也愈加明顯，如果完全依靠傳統(tǒng)電源對所造成的影響進(jìn)行補(bǔ)償，可能會因此增加傳統(tǒng)電源的備用容量，加大系統(tǒng)的運(yùn)行成本。所以，，隨著分布式電源并入電網(wǎng)容量的增加，需對含高滲透率風(fēng)電場的電力系統(tǒng)的有功功率策略進(jìn)行研究。 首先對雙饋式風(fēng)力發(fā)電系統(tǒng)進(jìn)行建模，分析其各模塊的數(shù)學(xué)模型，研究其網(wǎng)側(cè)逆變器和轉(zhuǎn)子側(cè)逆變器的控制策略。在此基礎(chǔ)上設(shè)計(jì)了考慮風(fēng)速變化及穩(wěn)態(tài)頻率誤差的一次調(diào)頻控制器，減少系統(tǒng)頻率偏差。并提出了基于直流靈敏度的風(fēng)電場間有功功率的調(diào)控策略，通過此調(diào)控策略可以調(diào)控各個風(fēng)電場的出力，從而解決系統(tǒng)發(fā)生大擾動時有功潮流的變化問題。同時對所提出的控制策略分別進(jìn)行了驗(yàn)證分析，結(jié)果表明所提的一次調(diào)頻控制策略可以有效改善系統(tǒng)的頻率偏差，基于直流靈敏度的風(fēng)電場間有功功率的調(diào)控策略可以有效消除支路的潮流越限。 其次針對電力系統(tǒng)接納風(fēng)電備用容量不足的情況，同時綜合考慮風(fēng)功率預(yù)測、風(fēng)電場裝機(jī)容量等因素，提出基于風(fēng)功率預(yù)測的比例算法、基于風(fēng)功率預(yù)測的優(yōu)化算法兩種風(fēng)電場間限值優(yōu)化策略，使風(fēng)電場自主參與系統(tǒng)調(diào)峰。通過實(shí)例對傳統(tǒng)的基于風(fēng)電場裝機(jī)容量的優(yōu)化分配算法及所提出的兩種優(yōu)化策略分別進(jìn)行驗(yàn)證，結(jié)果表明基于風(fēng)功率預(yù)測的優(yōu)化算法和基于風(fēng)功率預(yù)測的比例算法均明顯優(yōu)于傳統(tǒng)的基于風(fēng)電場裝機(jī)容量的分配算法，其中基于風(fēng)功率預(yù)測的優(yōu)化算法最優(yōu)。 最后提出基于機(jī)組分類的風(fēng)電場內(nèi)各機(jī)組間的優(yōu)化分配策略。根據(jù)場內(nèi)各個機(jī)組不同的運(yùn)行特性，計(jì)算出各機(jī)組的特征矩陣，運(yùn)用模糊聚類分析方法對機(jī)組進(jìn)行分類，并根據(jù)遺傳尋優(yōu)算法對分類機(jī)組進(jìn)行機(jī)組組合，選取最優(yōu)的機(jī)組運(yùn)行組合，通過基于風(fēng)功率預(yù)測的優(yōu)化算法對最優(yōu)的機(jī)組運(yùn)行組合進(jìn)行具體的功率數(shù)值分配。并通過實(shí)例對所提優(yōu)化策略進(jìn)行驗(yàn)證，結(jié)果表明此策略可以準(zhǔn)確地按照調(diào)度指令對風(fēng)電場內(nèi)機(jī)組間的功率進(jìn)行優(yōu)化分配。
[Abstract]:With the gradual increase of wind farm grid capacity, the negative impact on power grid becomes more and more obvious. If we rely on the traditional power supply to compensate for the impact, it may increase the reserve capacity of traditional power supply. Increase the operating cost of the system. Therefore, with the increase of the capacity of distributed generation integrated into power grid, the active power strategy of power system with high permeability wind farm should be studied. Firstly, the model of doubly-fed wind power generation system is built, the mathematical models of each module are analyzed, and the control strategies of grid-side inverter and rotor-side inverter are studied. On this basis, a primary frequency modulation controller considering the variation of wind speed and the error of steady frequency is designed to reduce the frequency deviation of the system. An active power control strategy based on DC sensitivity is proposed, which can be used to control the output of each wind farm, thus solving the problem of the change of active power flow in the event of large disturbance of the system. At the same time, the proposed control strategies are verified and analyzed respectively. The results show that the proposed primary frequency modulation control strategy can effectively improve the frequency deviation of the system. The active power regulation strategy based on DC sensitivity between wind farms can effectively eliminate the branch power flow limit. Secondly, considering the wind power forecast, wind farm installed capacity and other factors, a proportion algorithm based on wind power prediction is proposed. The optimization algorithm based on wind power prediction can make wind farm participate in the system peak shaving independently by optimizing the limit value between two kinds of wind farm. The traditional optimal allocation algorithm based on installed capacity of wind farm and the proposed two optimization strategies are verified by examples. The results show that the optimization algorithm based on wind power prediction and the proportion algorithm based on wind power prediction are obviously superior to the traditional allocation algorithm based on wind farm installed capacity, among which the optimization algorithm based on wind power prediction is optimal. Finally, an optimal allocation strategy for wind farms based on unit classification is proposed. According to the different operating characteristics of each unit in the field, the characteristic matrix of each unit is calculated, and the unit is classified by using fuzzy cluster analysis method, and the unit combination is carried out according to the genetic optimization algorithm. The optimal unit operation group is selected and the optimal unit operation unit is allocated numerically by the optimal wind power prediction algorithm. An example is given to verify the proposed optimization strategy. The results show that the proposed strategy can accurately allocate the power between units in a wind farm according to the dispatching instructions.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM614

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