發(fā)布時(shí)間：2018-10-09 08:38

【摘要】：在智能電網(wǎng)的環(huán)境下,潮流計(jì)算作為其基本的分析工具有著非常重要的地位。為了將電力系統(tǒng)經(jīng)濟(jì)性,安全性以及電能質(zhì)量三方面要求與潮流計(jì)算完美的結(jié)合起來,人們提出了最優(yōu)潮流計(jì)算。最優(yōu)潮流深受電力系統(tǒng)規(guī)劃設(shè)計(jì)人員和運(yùn)行調(diào)度人員的青睞,在電力系統(tǒng)規(guī)劃、運(yùn)行、分析和控制中起著重要的作用。本文首先針對(duì)牛頓法潮流計(jì)算的初值敏感性問題,提出了最優(yōu)初值選取方法。解決了由于初值選取不當(dāng)造成的潮流計(jì)算不收斂問題。其次,針對(duì)內(nèi)點(diǎn)法,簡(jiǎn)化梯度法等方法在最優(yōu)潮流計(jì)算中出現(xiàn)的初值敏感問題以及計(jì)算量過大等問題,提出了基于Fisher函數(shù)的廣義梯度投影最優(yōu)潮流算法。最后,為了使最優(yōu)潮流獲得更快的收斂速度,更方便的處理離散變量,運(yùn)用雙Hopfield神經(jīng)網(wǎng)絡(luò)來求解最優(yōu)潮流。文中的主要工作如下：(1)提出了牛頓法潮流計(jì)算的收斂性判據(jù),來判斷初值能否使潮流方程得到收斂解。若初值可行,根據(jù)所提出的最大迭代次數(shù)估計(jì)判據(jù),對(duì)潮流計(jì)算的迭代次數(shù)進(jìn)行初步估計(jì)。通過以上兩個(gè)判據(jù)可以對(duì)所選初值能否使潮流方程收斂進(jìn)行一個(gè)初步判斷,從而避免了初值任意選取造成的冗余計(jì)算。針對(duì)于牛頓法的初值敏感性問題,結(jié)合所提出的兩個(gè)判據(jù),利用遺傳算法提出了最優(yōu)初值的選取方法。IEEE標(biāo)準(zhǔn)節(jié)點(diǎn)系統(tǒng)和通遼電網(wǎng)的實(shí)例仿真結(jié)果可以證明所提出的最優(yōu)初值選取方法的有效性。(2)提出了基于Fisher函數(shù)的廣義梯度投影最優(yōu)潮流算法。該方法與簡(jiǎn)化梯度法相比,不必每次迭代都求解潮流方程,大大減少了計(jì)算量。并運(yùn)用動(dòng)態(tài)選取罰函數(shù)技術(shù),避免了由于罰函數(shù)選取不當(dāng)導(dǎo)致的病態(tài)條件數(shù)。與內(nèi)點(diǎn)法相比,擴(kuò)大了初值的選取范圍,避免了初值選取不當(dāng)導(dǎo)致收斂過程的不穩(wěn)定或使尋優(yōu)進(jìn)展緩慢。IEEE標(biāo)準(zhǔn)節(jié)點(diǎn)系統(tǒng)的仿真結(jié)果驗(yàn)證了該算法的有效性,并將簡(jiǎn)化梯度法,內(nèi)點(diǎn)法以及廣義梯度投影法最優(yōu)潮流計(jì)算分別運(yùn)用到通遼電網(wǎng)實(shí)例計(jì)算中,作出對(duì)比分析,仿真結(jié)果驗(yàn)證了基于Fisher函數(shù)的廣義的梯度投影法的快速收斂性和穩(wěn)定性。(3)運(yùn)用雙Hopfield神經(jīng)網(wǎng)絡(luò)來進(jìn)行最優(yōu)潮流計(jì)算。雙Hopfield神經(jīng)網(wǎng)絡(luò)分為兩個(gè)部分：首先,利用一個(gè)網(wǎng)絡(luò)來優(yōu)化罰函數(shù)項(xiàng),使解落在可行解子空間中。另一個(gè)網(wǎng)絡(luò)用來優(yōu)化目標(biāo)函數(shù),朝著目標(biāo)函數(shù)的可行下降方向進(jìn)行求解。兩個(gè)網(wǎng)絡(luò)是相互獨(dú)立的,交替運(yùn)行。雙Hopfield神經(jīng)網(wǎng)絡(luò)避免了Hopfield網(wǎng)絡(luò)求解最優(yōu)問題時(shí)既需滿足約束條件又需得到高質(zhì)量的解之間的矛盾,并且該算法極大的提高了計(jì)算速度。IEEE標(biāo)準(zhǔn)節(jié)點(diǎn)系統(tǒng)的仿真結(jié)果驗(yàn)證了雙Hopfield神經(jīng)網(wǎng)絡(luò)方法與Hopfield神經(jīng)網(wǎng)絡(luò)相比取得了更好的優(yōu)化效果。
[Abstract]:In the environment of smart grid, power flow calculation as its basic analysis tool has a very important position. In order to combine the three requirements of power system economy, security and power quality with the power flow calculation perfectly, people put forward the optimal power flow calculation. The optimal power flow is favored by the power system planners and dispatchers, and plays an important role in power system planning, operation, analysis and control. In this paper, an optimal initial value selection method is proposed to solve the sensitivity problem of Newtonian power flow calculation. The problem of non-convergence of power flow calculation caused by improper selection of initial value is solved. Secondly, a generalized gradient projection optimal power flow algorithm based on Fisher function is proposed to solve the initial value sensitivity problem and the excessive amount of calculation in the optimal power flow calculation by the interior point method and the simplified gradient method. Finally, in order to get faster convergence speed of optimal power flow and more convenient to deal with discrete variables, a double Hopfield neural network is used to solve the optimal power flow. The main work of this paper is as follows: (1) the convergence criterion of Newtonian power flow calculation is proposed to determine whether the initial value can converge the power flow equation. If the initial value is feasible, the iterative number of power flow calculation is estimated preliminarily according to the proposed criterion of maximum iterative degree estimation. Through the above two criteria, we can make a preliminary judgment on whether the selected initial value can make the power flow equation converge, thus avoiding the redundant calculation caused by the random selection of the initial value. In view of the sensitivity of the initial value of Newton's method, combined with the proposed two criteria, The simulation results of IEEE standard node system and Tongliao power network show that the proposed method is effective. (2) the generalized ladder based on Fisher function is proposed. Degree projection optimal power flow algorithm. Compared with the simplified gradient method, the proposed method does not have to solve the power flow equation every iteration, thus greatly reducing the computational complexity. The technique of dynamic selection of penalty function is used to avoid the ill-conditioned condition caused by improper selection of penalty function. Compared with the interior point method, the range of initial value selection is enlarged, and the instability of convergence process caused by improper initial value selection or the slow progress of optimization are avoided. The simulation results of IEEE standard node system verify the effectiveness of the algorithm and simplify the gradient method. The interior point method and the generalized gradient projection method are used to calculate the optimal power flow in Tongliao power network. The simulation results verify the fast convergence and stability of the generalized gradient projection method based on Fisher function. (3) double Hopfield neural network is used to calculate the optimal power flow. The dual Hopfield neural network is divided into two parts: first, a network is used to optimize the penalty function term, so that the solution falls in the feasible solution subspace. Another network is used to optimize the objective function and solve the problem in the direction of feasible descent of the objective function. The two networks are independent of each other and run alternately. The double Hopfield neural network avoids the contradiction between satisfying the constraint condition and obtaining the high quality solution when the Hopfield network is used to solve the optimal problem. The algorithm greatly improves the computation speed. The simulation results of IEEE-based standard node system verify that the dual Hopfield neural network method has better optimization effect than that of Hopfield neural network.
【學(xué)位授予單位】：東北大學(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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