本文選題：配電網(wǎng) + 無(wú)功優(yōu)化�。� 參考：《濟(jì)南大學(xué)》2017年碩士論文

【摘要】：隨著當(dāng)代技術(shù)的不斷革新以及經(jīng)濟(jì)的高速增長(zhǎng),人們的生活水平得到了極大的提高,隨之而來(lái)的是社會(huì)各行各業(yè)對(duì)電能的需求量日益增加,同時(shí)對(duì)供電質(zhì)量和安全性的要求也在不斷提高。電壓作為衡量電能質(zhì)量的重要指標(biāo)之一,直接影響著電力系統(tǒng)安全穩(wěn)定的運(yùn)行,而且隨著用電負(fù)荷的飛速增長(zhǎng),導(dǎo)致很多地區(qū)的電壓不滿足國(guó)家標(biāo)準(zhǔn)值,甚至?xí)霈F(xiàn)用電設(shè)備無(wú)法正常工作的情況。影響電壓質(zhì)量的主要原因是系統(tǒng)提供的無(wú)功功率不足或無(wú)功功率分布不合理。因此,在配電網(wǎng)中合理的配置無(wú)功補(bǔ)償裝置及時(shí)就地補(bǔ)充無(wú)功功率,保障網(wǎng)絡(luò)中無(wú)功功率的平衡,提高電壓質(zhì)量從而保證電力系統(tǒng)安全的運(yùn)行,同時(shí)提高電網(wǎng)運(yùn)行的經(jīng)濟(jì)性顯得很重要。本文在分析了傳統(tǒng)無(wú)功補(bǔ)償點(diǎn)選擇方法存在不足的基礎(chǔ)上,提出把聚類算法和功率矩方法結(jié)合來(lái)選擇無(wú)功補(bǔ)償。首先根據(jù)網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)建立關(guān)聯(lián)矩陣,然后對(duì)高維矩陣進(jìn)行降維處理以便于在聚類操作時(shí)減少計(jì)算量節(jié)省計(jì)算時(shí)間,聚類過(guò)程是將整個(gè)網(wǎng)絡(luò)根據(jù)電氣距離來(lái)劃分成幾個(gè)子區(qū)域,接著在每個(gè)子區(qū)域中采用功率矩公式計(jì)算負(fù)荷中心以確定補(bǔ)償點(diǎn)的位置。這樣選擇補(bǔ)償點(diǎn)可以有效避免傳統(tǒng)方法選擇補(bǔ)償點(diǎn)過(guò)于集中的缺陷,能夠更好地實(shí)現(xiàn)無(wú)功分散補(bǔ)償就地平衡。補(bǔ)償點(diǎn)位置確定后如何求解相應(yīng)補(bǔ)償容量,本文在對(duì)比分析傳統(tǒng)數(shù)學(xué)優(yōu)化方法和現(xiàn)代人工智能優(yōu)化算法的優(yōu)缺點(diǎn)后,選擇了粒子群優(yōu)化算法,并基于粒子群算法本身存在的計(jì)算后期易陷入局部極值的缺點(diǎn)提出改進(jìn)策略,主要對(duì)粒子群算法中的慣性權(quán)重和學(xué)習(xí)因子這兩個(gè)參數(shù)進(jìn)行了研究分析,以及在算法迭代計(jì)算后期加入了遺傳算法中的交叉操作過(guò)程,依據(jù)粒子的適應(yīng)度值大小排序并將種群分成兩部分,好的部分繼續(xù)按原計(jì)劃繼續(xù)搜尋,不好的部分內(nèi)粒子之間進(jìn)行交叉操作產(chǎn)生新粒子,增加了粒子種群在迭代計(jì)算后期的多樣性,改善了算法性能。針對(duì)部分較為落后的農(nóng)村長(zhǎng)線路,在僅通過(guò)補(bǔ)償無(wú)功而不能有效解決線路末端低壓?jiǎn)栴}的情況下,考慮在線路上安裝自動(dòng)調(diào)壓器來(lái)提升線路電壓,通過(guò)同時(shí)配置無(wú)功補(bǔ)償器和自動(dòng)調(diào)壓器,在提升電壓質(zhì)量、降低線路損耗以及增加投資收益等多方面效果顯著。以IEEE-33節(jié)點(diǎn)系統(tǒng)和66節(jié)點(diǎn)實(shí)際農(nóng)村配電線路為例進(jìn)行編程仿真,與其他方法的計(jì)算結(jié)果對(duì)比分析,驗(yàn)證了本文方法的正確性和有效性。
[Abstract]:With the continuous innovation of modern technology and rapid economic growth, people's living standards have been greatly improved, followed by the increasing demand for electricity in various sectors of society. At the same time, the quality of power supply and security requirements are also constantly improving. Voltage, as one of the important indexes to measure power quality, directly affects the safe and stable operation of power system, and with the rapid increase of power load, the voltage in many areas does not meet the national standard. Even electrical equipment will not work properly. The main reason of affecting voltage quality is that the reactive power provided by the system is insufficient or the distribution of reactive power is unreasonable. Therefore, the reasonable allocation of reactive power compensator in distribution network can ensure the balance of reactive power in the network, improve the voltage quality and ensure the safe operation of power system. At the same time, it is very important to improve the economy of power grid operation. Based on the analysis of the shortcomings of the traditional reactive power compensation point selection method, this paper proposes a new method to select reactive power compensation by combining the clustering algorithm with the power moment method. First of all, the correlation matrix is established according to the network topology, and then the high-dimensional matrix is reduced to reduce the amount of computation and save the computing time. The whole network is divided into several sub-regions according to the electrical distance in the clustering process. Then the power moment formula is used to calculate the load center in each sub-region to determine the position of the compensation point. In this way, the traditional method can avoid the defect that the compensation points are too centralized, and the local balance of reactive power dispersion compensation can be better realized. After determining the position of compensation points, how to solve the corresponding compensation capacity, after comparing and analyzing the advantages and disadvantages of traditional mathematical optimization method and modern artificial intelligence optimization algorithm, the particle swarm optimization algorithm is selected. Based on the shortcomings of particle swarm optimization (PSO), which is easy to fall into local extremum in the later stage of computation, an improved strategy is put forward. The inertia weight and learning factor of PSO are studied and analyzed. In addition, the cross-operation process of genetic algorithm is added in the late iterative computation of the algorithm. The population is sorted according to the fitness value of the particle and the population is divided into two parts. The good part continues to search according to the original plan. The poor cross operation between the particles in some parts produces new particles, which increases the diversity of particle population in the late iterative computation and improves the performance of the algorithm. In view of some backward rural long lines, considering the installation of automatic voltage regulator to raise the line voltage, the problem of low voltage at the end of the line can not be effectively solved by compensating reactive power. By configuring reactive power compensator and automatic voltage regulator at the same time, it can improve voltage quality, reduce line loss and increase investment income. Taking IEEE-33 node system and 66-bus actual rural distribution line as examples, the correctness and validity of this method are verified by comparing and analyzing the calculation results of other methods.
【學(xué)位授予單位】：濟(jì)南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM714.3

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本文編號(hào)：1980780