本文選題：配電網(wǎng)　切入點(diǎn)：線損計(jì)算　出處：《湖南大學(xué)》2014年碩士論文

【摘要】：配電網(wǎng)線損計(jì)算是電力系統(tǒng)降損節(jié)能的重要技術(shù)手段，是線損管理科學(xué)化、規(guī)范化、制度化的實(shí)現(xiàn)基礎(chǔ)。準(zhǔn)確簡(jiǎn)便的線損計(jì)算有助于制定合理的降損措施，提高供電能力，增加電力企業(yè)經(jīng)濟(jì)效益。 配電網(wǎng)結(jié)構(gòu)復(fù)雜、分支線路多，往往缺乏準(zhǔn)確、完整的線路和負(fù)荷資料，導(dǎo)致常規(guī)線損計(jì)算方法往往難以實(shí)施。針對(duì)這一問題，本文利用神經(jīng)網(wǎng)絡(luò)特有的非線性擬合特性，研究易于操作、可行性高，且滿足工程計(jì)算精度的配電網(wǎng)線損計(jì)算方法。 首先，，論文針對(duì)中壓和低壓配電網(wǎng)的特點(diǎn)，分析了中壓和低壓配電網(wǎng)現(xiàn)有線損計(jì)算方法，指出現(xiàn)有線損計(jì)算方法的適用條件與不足。 其次，針對(duì)中壓配電網(wǎng)配電線路結(jié)構(gòu)復(fù)雜，運(yùn)行數(shù)據(jù)不全，常規(guī)線損計(jì)算方法難以實(shí)施的問題，將徑向基函數(shù)（Radial Basis Function，RBF）神經(jīng)網(wǎng)絡(luò)應(yīng)用到中壓配電網(wǎng)線損計(jì)算中，利用它的擬合特性，映射配電線路線損與特征參量之間復(fù)雜的非線性關(guān)系，記憶配電線路在結(jié)構(gòu)參數(shù)和運(yùn)行參數(shù)變化時(shí)線損的變化規(guī)律，建立了基于RBF神經(jīng)網(wǎng)絡(luò)的中壓配電網(wǎng)線損計(jì)算模型。 然后，對(duì)自適應(yīng)二次變異差分進(jìn)化（Adaptive Second Mutation DifferentialEvolution，ASMDE）算法進(jìn)行了改進(jìn)，采用了重構(gòu)交叉概率因子思想和近似最優(yōu)保存策略。利用改進(jìn)的ASMDE算法對(duì)RBF神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)參數(shù)進(jìn)行整體優(yōu)化，克服了常規(guī)網(wǎng)絡(luò)訓(xùn)練算法隱含層與輸出層結(jié)構(gòu)參數(shù)分開確定，輸出層易陷入局部極小等缺點(diǎn)。實(shí)例仿真驗(yàn)證了所提中壓配電網(wǎng)線損計(jì)算模型與算法的可行性和先進(jìn)性。 最后，研究低壓配電網(wǎng)的線損計(jì)算方法。低壓配電網(wǎng)供電方式復(fù)雜多樣，分支線路眾多，沿線用電負(fù)荷沒有嚴(yán)格的規(guī)律，自動(dòng)化程度不高，線路參數(shù)和負(fù)荷資料嚴(yán)重缺乏�；谏鲜鰡栴}，將BP神經(jīng)網(wǎng)絡(luò)用于低壓配電網(wǎng)的線損計(jì)算與分析中，并對(duì)基于BP神經(jīng)網(wǎng)絡(luò)的線損計(jì)算模型的輸入?yún)?shù)進(jìn)行了詳細(xì)地分析，找出了引起配電臺(tái)區(qū)線損變化的主要參量，將其作為BP神經(jīng)網(wǎng)絡(luò)模型的輸入?yún)?shù)。利用Matlab神經(jīng)網(wǎng)絡(luò)工具箱完成了網(wǎng)絡(luò)的訓(xùn)練，通過實(shí)例對(duì)所建低壓臺(tái)區(qū)線損計(jì)算模型進(jìn)行了仿真，結(jié)果驗(yàn)證了所建模型的準(zhǔn)確性和實(shí)用性。
[Abstract]:Line loss calculation of distribution network is an important technical means to reduce loss and save energy in power system. It is the scientific, standardized and institutionalized realization foundation of line loss management.Accurate and simple line loss calculation is helpful to make reasonable loss reduction measures, improve power supply capacity and increase economic benefits of power enterprises.The structure of distribution network is complex and there are many branch lines, which often lack accurate and complete data of line and load, so it is difficult to carry out the conventional line loss calculation method.In order to solve this problem, this paper makes use of the special nonlinear fitting characteristic of neural network to study the distribution network line loss calculation method which is easy to operate, high feasibility and meets the engineering calculation accuracy.Firstly, according to the characteristics of medium voltage and low voltage distribution networks, this paper analyzes the existing line loss calculation methods of medium voltage and low voltage distribution networks, and points out the applicable conditions and shortcomings of the existing line loss calculation methods.Secondly, aiming at the problems that the distribution line structure is complex, the operation data is not complete and the conventional line loss calculation method is difficult to carry out, the radial basis function (RBF) Basis function neural network is applied to the line loss calculation of the medium voltage distribution network.By using its fitting characteristic, the complex nonlinear relationship between line loss and characteristic parameters of distribution line is mapped, and the variation law of line loss of distribution line when the structure parameter and operation parameter change are memorized.The line loss calculation model of medium voltage distribution network based on RBF neural network is established.Then, adaptive Second Mutation differential evolution (ASMDE) algorithm of adaptive quadratic mutation differential evolution is improved, and the idea of reconstructing crossover probability factor and approximate optimal preservation strategy are adopted.The improved ASMDE algorithm is used to optimize the structural parameters of the RBF neural network, which overcomes the shortcomings of the conventional network training algorithm, such as the structural parameters of the hidden layer and the output layer are determined separately and the output layer is prone to fall into local minima.The simulation results show that the proposed model and algorithm are feasible and advanced.Finally, the line loss calculation method of low voltage distribution network is studied.The power supply mode of low voltage distribution network is complex and diverse, the branch lines are numerous, there are no strict rules of power load along the line, the degree of automation is not high, and the data of line parameters and load are seriously lacking.Based on the above problems, the BP neural network is applied to the line loss calculation and analysis of the low voltage distribution network, and the input parameters of the line loss calculation model based on the BP neural network are analyzed in detail.The main parameters which cause the line loss change in the distribution station area are found out and used as the input parameters of the BP neural network model.The Matlab neural network toolbox is used to train the network. The model of line loss calculation in low voltage station is simulated by an example, and the accuracy and practicability of the model are verified.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM744

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