本文關(guān)鍵詞： 系統(tǒng)無功優(yōu)化 風(fēng)電并網(wǎng) 混沌粒子群算法 蛙跳算法　出處：《山東大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：我國風(fēng)力資源豐富,作為綠色、無污染、可再生的清潔資源之一,風(fēng)電的發(fā)展給電力行業(yè)帶來了無限前景。由于風(fēng)力的特點,風(fēng)電的輸出具有隨機性和波動性,在大規(guī)模的風(fēng)電并網(wǎng)時會給系統(tǒng)的穩(wěn)定性帶來沖擊。對于電力系統(tǒng)來說,無功功率的平衡影響著系統(tǒng)的電壓質(zhì)量,是決定系統(tǒng)能否保持穩(wěn)定運行的重要條件。電力系統(tǒng)的無功優(yōu)化是一個極具復(fù)雜性的問題,多變量、多約束。大規(guī)模風(fēng)電接入電網(wǎng)后,給無功優(yōu)化帶來了新的挑戰(zhàn),風(fēng)速難以預(yù)測,輸出功率數(shù)據(jù)難以得到；傳統(tǒng)計算潮流的算法對于風(fēng)電節(jié)點不再適合；用于優(yōu)化問題的各種算法都存在一定的缺點,亟需改進。本文針對風(fēng)速難以預(yù)測的問題,建立了滿足精度要求、計算方便的風(fēng)電場功率輸出模型。以Weibull分布近似表征風(fēng)速,利用風(fēng)電場景概率模型,計算得到風(fēng)場輸出功率。選用牛頓-拉夫遜法進行系統(tǒng)潮流計算,考慮風(fēng)電并網(wǎng)節(jié)點的特殊性,該算法對于并網(wǎng)節(jié)點只需修改風(fēng)電節(jié)點相應(yīng)的雅克比矩陣元素。無功優(yōu)化的過程就是一個最優(yōu)潮流求解的過程,選擇合適的算法對于最優(yōu)解的獲取至關(guān)重要。本文選用獲得廣泛認可的粒子群算法作為尋優(yōu)算法,以系統(tǒng)的有功網(wǎng)損最小作為目標(biāo)函數(shù),同時加入對節(jié)點電壓越限和發(fā)電機無功越限的懲罰項。針對粒子群算法易陷入局部最優(yōu)的缺點,引入混沌粒子群算法。在混沌粒子群算法的基礎(chǔ)上,本文提出了一種新的算法——蛙跳混沌粒子群算法(Frog-Chaotic Particle Swarm Optimization, F-CPSO)。蛙跳算法作為一種新的啟發(fā)式進化算法,具有良好的計算性能和全局尋優(yōu)能力。為克服粒子群算法易陷入局部最優(yōu)的缺點,F-CPSO在混沌粒子群算法的基礎(chǔ)上做了改進：對初始種群以粒子適應(yīng)度函數(shù)值的大小為準(zhǔn)則將種群分組,在族群和全局內(nèi)同步尋優(yōu)；在速度更新公式中加入對族群最優(yōu)粒子的學(xué)習(xí)因子；每一次的迭代過程中,隨機產(chǎn)生一個新的粒子替換適應(yīng)值最差的粒子,增加種群的多樣性。為驗證改進算法的有效性,本文以IEEE-30節(jié)點系統(tǒng)為例進行了仿真,在IEEE-30節(jié)點系統(tǒng)加入風(fēng)電節(jié)點。采用MATLAB語言進行編程仿真,并與標(biāo)準(zhǔn)粒子群算法(SPSO)進行對比,經(jīng)過仿真的結(jié)果分析驗證,改進后的粒子群算法能夠有效地降低系統(tǒng)的有功網(wǎng)損,提高系統(tǒng)的電壓合格率,相比于標(biāo)準(zhǔn)粒子群算法,改進后的粒子群算法具有更好的全局收斂性能。
[Abstract]:As one of the green, pollution-free and renewable clean resources, the development of wind power in China brings infinite prospects to the power industry. Because of the characteristics of wind power, the output of wind power has randomness and volatility. The stability of the power system will be impacted by large-scale wind power grid connection. For the power system, the balance of reactive power affects the voltage quality of the system. Reactive power optimization of power system is a very complex problem, multivariable, multi-constraint, large-scale wind power connected to the power network. It brings new challenges to reactive power optimization, wind speed is difficult to predict, output power data is difficult to obtain; The traditional algorithm for calculating power flow is no longer suitable for wind power nodes. All kinds of algorithms used in optimization problems have some shortcomings and need to be improved. In order to solve the problem that wind speed is difficult to predict, this paper establishes a method to meet the precision requirements. The wind speed is approximately represented by Weibull distribution, and the probability model of wind power scene is used. The output power of wind field is calculated. Newton-Raphson method is used to calculate the power flow of the system, and the particularity of the connected node of wind power is considered. The algorithm only needs to modify the corresponding Jacobian matrix elements for grid-connected nodes. The process of reactive power optimization is a process of solving the optimal power flow. It is very important to select the appropriate algorithm to obtain the optimal solution. In this paper, the widely accepted particle swarm optimization algorithm is chosen as the optimization algorithm, and the minimum active power network loss of the system is taken as the objective function. At the same time, the penalty items of the node voltage overrun and generator reactive power are added. Aiming at the disadvantage of particle swarm optimization, chaotic particle swarm optimization algorithm is introduced. Based on chaotic particle swarm optimization algorithm, chaotic particle swarm optimization algorithm is introduced. In this paper, a new algorithm, Frog-chaotic Particle Swarm Optimization, is proposed. As a new heuristic evolutionary algorithm, F-CPSOL has good computational performance and global optimization ability. In order to overcome the shortcomings of particle swarm optimization (PSO), it is easy to fall into local optimization. F-CPSO is improved on the basis of chaotic particle swarm optimization algorithm: the initial population is grouped according to the size of the particle fitness function, and the population is optimized synchronously within the population and the whole world; Adding the learning factor to the population optimal particle in the velocity update formula; In each iteration, a new particle is randomly generated to replace the worst particle, increasing the diversity of the population. In this paper, IEEE-30 node system is taken as an example, wind power node is added to IEEE-30 node system, and MATLAB language is used to program simulation. Compared with the standard particle swarm optimization (SPSO), the improved particle swarm optimization algorithm can effectively reduce the active power loss of the system and increase the qualified rate of the system through the analysis of simulation results. Compared with the standard particle swarm optimization algorithm, the improved particle swarm optimization algorithm has better global convergence performance.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TM614;TM714.3

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