本文選題：無功優(yōu)化　切入點：有功損耗　出處：《南京郵電大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：當(dāng)前經(jīng)濟發(fā)展迅猛,因?qū)﹄娏Φ男枨罅恐饾u增加,而使電網(wǎng)系統(tǒng)的安全方面的問題也日益凸顯。而電能質(zhì)量的優(yōu)劣是確保整個電力系統(tǒng)能否安全平穩(wěn)運行的關(guān)鍵,電壓質(zhì)量又作為衡量電能質(zhì)量的一個重要參考項,因此該指標(biāo)逐漸成為電力工作人員關(guān)注的重點。同時影響電壓質(zhì)量的要素包括無功優(yōu)化,因而對電力系統(tǒng)采取合理的無功規(guī)劃尤為關(guān)鍵。合理的無功優(yōu)化,在有效的改善整個電網(wǎng)的無功分布的同時,還能在一定程度上降低系統(tǒng)的有功損耗。電力系統(tǒng)無功優(yōu)化是一繁雜的非線性規(guī)劃問題,涉及到多個變量和多條約束條件,同時在變量中離散和連續(xù)變量均有。對于在約束條件中存在的等式約束,它作為一個高階的非凸方程組,同樣包含了多個變量和約束條件。若采用一般的數(shù)學(xué)方法進(jìn)行求解,則整個計算過程復(fù)雜且低效,因此需要選擇合理的方法進(jìn)行計算求解。對于在無功優(yōu)化過程中,可以采用調(diào)整發(fā)電機的端電壓值、可投電容的無功補償以及可調(diào)變壓器變比值的措施,實現(xiàn)減少電力系統(tǒng)有功損耗的優(yōu)化目標(biāo),使電網(wǎng)安全平穩(wěn)工作。本文通過對粒子群算法和量子粒子群算法的研究,對于兩種算法在迭代尋優(yōu)后期,均有易陷入局部最優(yōu)、后期收斂速度減緩等缺陷,提出一種改進(jìn)的量子粒子群算法。該改進(jìn)算法是基于交叉因子的雙向?qū)?yōu)策略,將粒子種群全局最差點的反向點引入量子粒子群算法中粒子的位置更新公式中,從而幫助粒子在后期迭代過程中能夠跳出局部最優(yōu)。接著本文選擇將有功網(wǎng)損納入建模核心,目標(biāo)函數(shù)定為使電網(wǎng)有功網(wǎng)損盡量降低,再結(jié)合罰函數(shù)建立數(shù)學(xué)模型,用來處理目標(biāo)函數(shù)中出現(xiàn)的電壓越界和發(fā)電機無功越界的問題,再進(jìn)一步應(yīng)用到風(fēng)電場中,對其進(jìn)行無功優(yōu)化。最后列出將改進(jìn)量子粒子群算法應(yīng)用于含風(fēng)電場并網(wǎng)的電力系統(tǒng)無功優(yōu)化的算法步驟。選取以IEEE57節(jié)點系統(tǒng)作為業(yè)務(wù)應(yīng)用場景,將改進(jìn)量子粒子群算法應(yīng)用于此場景中,并和PSO算法和QPSO算法比較,由仿真結(jié)果表明該改進(jìn)算法有效的減少了電力系統(tǒng)的有功網(wǎng)損。
[Abstract]:With the rapid economic development, the demand for power is increasing gradually, and the security problems of the power system are becoming increasingly prominent. The quality of power is the key to ensure the safe and stable operation of the whole power system. Voltage quality is regarded as an important reference item to measure power quality, so this index has gradually become the focus of attention of power workers. At the same time, the factors that affect voltage quality include reactive power optimization. Therefore, it is very important to take reasonable reactive power planning for power system. Reasonable reactive power optimization can effectively improve the reactive power distribution of the whole power system. Reactive power optimization is a complicated nonlinear programming problem, which involves many variables and constraints. There are both discrete and continuous variables in the variables. As a higher order nonconvex system of equations, it also contains many variables and constraints for the equality constraints that exist in the constraint conditions. If the general mathematical method is used to solve the problem, The whole calculation process is complex and inefficient, so it is necessary to select a reasonable method to solve the problem. In the process of reactive power optimization, the terminal voltage of the generator can be adjusted. The reactive power compensation of capacitors and the variable ratio of transformers realize the optimization goal of reducing the active power loss in power system and make the power grid work safely and stably. In this paper, the particle swarm optimization algorithm and quantum particle swarm optimization algorithm are studied. In this paper, an improved Quantum Particle Swarm Optimization (QPSO) algorithm is proposed, which is based on the crossover factor. The reverse point of global worst point of particle population is introduced into the updating formula of particle position in quantum particle swarm optimization algorithm, so that particle can jump out of the local optimum in the late iteration process. Then, the active power network loss is selected to be included in the modeling core in this paper. The objective function is to minimize the loss of the active power network, and a mathematical model is established by combining the penalty function, which is used to deal with the problem of voltage and generator reactive power crossing in the objective function, and further applied to wind farm. Finally, the steps of applying the improved quantum particle swarm optimization algorithm to the reactive power optimization of the power system with wind farm connected to the grid are listed. The IEEE57 node system is selected as the service application scenario. The improved Quantum Particle Swarm Optimization (QPSO) algorithm is applied to this scenario and compared with the PSO algorithm and the QPSO algorithm. The simulation results show that the improved QPSO algorithm can effectively reduce the active power loss of the power system.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;TM614;TM714.3

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