智能電網(wǎng)暫態(tài)故障檢測(cè)和電流過載預(yù)防控制研究
發(fā)布時(shí)間：2018-12-14 13:11
【摘要】：隨著我國(guó)經(jīng)濟(jì)的高速發(fā)展，電力能源需求不斷增大，電力系統(tǒng)變得越來越龐大和復(fù)雜。由于新的電力設(shè)備不斷被接入電網(wǎng)中，外部干擾下電網(wǎng)的安全隱患增大。電力系統(tǒng)網(wǎng)絡(luò)是一非線性、規(guī)模大、強(qiáng)耦合、動(dòng)態(tài)的復(fù)雜系統(tǒng)，傳統(tǒng)電力網(wǎng)絡(luò)監(jiān)控系統(tǒng)的測(cè)量、計(jì)算、控制，通信缺乏廣泛的協(xié)作，其靈活性和效率還有待提高。智能電網(wǎng)的出現(xiàn)為上述問題的解決提供了新的機(jī)遇。面向智能電網(wǎng)，研究新的理論和方法提高電力系統(tǒng)的可靠性和安全性具有重要的意義。 本文圍繞提高電力系統(tǒng)暫態(tài)安全性，對(duì)其故障檢測(cè)方法，預(yù)防控制策略展開研究。根據(jù)電力系統(tǒng)的傳輸和動(dòng)態(tài)特性，構(gòu)建集成統(tǒng)一潮流控制器(Unified PowerFlow Controller, UPFC)的電力系統(tǒng)暫態(tài)數(shù)學(xué)模型。提出了基于極點(diǎn)配置局部遞歸全局前饋((Locally Recurrent Global Forward, LRGF)動(dòng)態(tài)神經(jīng)網(wǎng)絡(luò)建模方法，并分別討論了基于小波提升和基于在線自適應(yīng)主元分解的電網(wǎng)暫態(tài)故障檢測(cè)。最后，針對(duì)電力系統(tǒng)傳輸線路電流過載和暫態(tài)非穩(wěn)定情況，提出了采用UPFC作為控制手段，基于障礙函數(shù)和能量函數(shù)的一種預(yù)防控制策略。仿真結(jié)果驗(yàn)證了提出方法的有效性。 ①針對(duì)電網(wǎng)暫態(tài)過程基于數(shù)據(jù)的建模，提出了一種基于極點(diǎn)配置LRGF神經(jīng)網(wǎng)絡(luò)。由于對(duì)于動(dòng)態(tài)神經(jīng)元的極點(diǎn)存在于實(shí)軸上和一對(duì)共軛復(fù)數(shù)極點(diǎn)兩種情況，為了避免參數(shù)到穩(wěn)定區(qū)域投影的復(fù)雜性，提出的神經(jīng)網(wǎng)絡(luò)將隱層神經(jīng)元內(nèi)動(dòng)態(tài)濾波器的極點(diǎn)被劃分為依據(jù)極點(diǎn)的情況將神經(jīng)元分成實(shí)極點(diǎn)和復(fù)極點(diǎn)兩部分，通過函數(shù)權(quán)值的方法將這兩種情況極點(diǎn)的動(dòng)態(tài)部分加權(quán)輸出，同時(shí)針對(duì)這種新的神經(jīng)網(wǎng)絡(luò)特別的采用了求導(dǎo)梯度下降的學(xué)習(xí)算法，通過極點(diǎn)投影和權(quán)值調(diào)節(jié)學(xué)習(xí)計(jì)算實(shí)現(xiàn)對(duì)電網(wǎng)暫態(tài)特性建模。 ②針對(duì)電網(wǎng)暫態(tài)故障檢測(cè)中殘差信號(hào)分析，提出了一種基于小波提升和自適應(yīng)閾值的檢測(cè)方法。根據(jù)殘差信號(hào)和小波函數(shù)最優(yōu)設(shè)計(jì)原理自適應(yīng)地設(shè)計(jì)小波預(yù)測(cè)算子和更新算子。通過小波提升方法，將極點(diǎn)配置LRGF動(dòng)態(tài)神經(jīng)網(wǎng)絡(luò)輸出與電力系統(tǒng)輸出作差得到的殘差信號(hào)分解為細(xì)節(jié)信號(hào)和逼近信號(hào)提取故障特征。通過自適應(yīng)閾值檢測(cè)細(xì)節(jié)信號(hào)和逼近信號(hào)，以及容忍時(shí)間方法檢測(cè)緩變和突變故障。仿真結(jié)果驗(yàn)證了此方法在電網(wǎng)暫態(tài)故障檢測(cè)中的有效性。 ③針對(duì)電網(wǎng)在線暫態(tài)故障檢測(cè)中殘差信號(hào)分析中數(shù)據(jù)處理問題，，提出了一種基于在線自適應(yīng)主元分解算法。提出的在線自適應(yīng)主成份分解算法通過以殘差信號(hào)為輸入的主元向量迭代，快速計(jì)算主元特征向量，建立主元模型。通過主元變換降低被檢測(cè)信號(hào)維度，得到殘差信號(hào)的主元得分。根據(jù)主元得分計(jì)算T2統(tǒng)計(jì)變量和Q統(tǒng)計(jì)變量。通過T2統(tǒng)計(jì)量反應(yīng)系統(tǒng)PCA模型內(nèi)部變化，Q統(tǒng)計(jì)量反應(yīng)PCA模型與信號(hào)偏差的原理，檢測(cè)系統(tǒng)故障。仿真實(shí)例驗(yàn)證了算法的有效性。 ④為應(yīng)對(duì)電網(wǎng)暫態(tài)過程中電網(wǎng)傳輸線路電流過載，在統(tǒng)一潮流控制器(UPFC)下，提出了基于障礙函數(shù)和能量函數(shù)的暫態(tài)電流過載預(yù)防控制方法。通過能量函數(shù)方法分析電力系統(tǒng)在故障后的暫態(tài)穩(wěn)定性。根據(jù)穩(wěn)定分析結(jié)果，實(shí)施預(yù)防控制策略。與基于仿真法和人工智能法的預(yù)防控制不同，文中通過構(gòu)建了一種由電網(wǎng)能量函數(shù)和障礙函數(shù)組成的控制李亞普諾夫函數(shù)，得到控制率�？刂破魍ㄟ^障礙函數(shù)約束邊界數(shù)值無(wú)窮大的特性，以及統(tǒng)一潮流控制器的作用，阻止電網(wǎng)傳輸線路暫態(tài)電流過載。采用最近非穩(wěn)定平衡點(diǎn)UEP的方法分析控制系統(tǒng)的穩(wěn)定性，并通過優(yōu)化算法調(diào)節(jié)障礙函數(shù)重塑系統(tǒng)穩(wěn)定區(qū)域。通過對(duì)3節(jié)點(diǎn)電力系統(tǒng)和162節(jié)點(diǎn)電力系統(tǒng)的仿真結(jié)果證明了本文提出的預(yù)防控制方法的有效性。
[Abstract]:With the high-speed development of our country's economy, the power demand of electric power is increasing, and the power system becomes more and more bulky and complex. the potential safety hazard of the power grid is increased due to the fact that the new power equipment is constantly being accessed into the power grid. The network of power system is a non-linear, large-scale, strong-coupled and dynamic complex system. The measurement, calculation, control and communication of the traditional power network monitoring system lack extensive cooperation, and its flexibility and efficiency are still to be improved. The emergence of the smart grid provides a new opportunity for solving the above problems. It is of great significance to study the new theory and method to improve the reliability and safety of the power system. In this paper, the transient security of power system is improved, the fault detection method and the prevention control strategy are developed. The power system transient mathematical model of the integrated power flow controller (UPFC) is built according to the transmission and dynamic characteristics of the power system. In this paper, a local recursive global forward (LRGF) dynamic neural network modeling method based on pole assignment is proposed, and the power grid transient fault detection based on wavelet lifting and on-line self-adaptive main element decomposition is discussed. Finally, aiming at the current overload and transient non-stability of the transmission line of the power system, a control method using UPFC as a control means, an obstacle function and an energy function is proposed. The simulation results verify the effectiveness of the proposed method. Based on the data-based modeling of the transient process of the power grid, an LRGF God based on pole configuration is proposed. through the network, since the poles of the dynamic neuron are present on the real axis and the pair of common complex poles, in order to avoid the projection of the parameters to the stable region, The complexity of the neural network is that the pole of the dynamic filter in the hidden layer neuron is divided into two parts of the real pole and the complex pole according to the case of the pole, and the dynamic part of the two cases is added by the method of the function weight value. The power output, in addition to the new neural network, adopts the learning algorithm of the gradient descent of the derivation gradient, and the power grid transient is realized through the pole projection and weight adjustment learning calculation. Based on the analysis of residual signal in power grid transient fault detection, a wavelet-based lifting and adaptive threshold is presented. The method for detecting the small wave is adaptively designed according to the residual signal and the design principle of the wavelet function. and the residual signal obtained by the difference between the output of the LGF dynamic neural network and the output of the power system is decomposed into a detail signal and an approximation signal by a small wave lifting method. Taking the fault features, detecting the detail signal and the approximation signal through the adaptive threshold, and detecting the slow change by the tolerance time method. and the simulation results verify that the method is in the power grid transient fault detection In order to solve the problem of data processing in the analysis of residual signal in the on-line transient fault detection of the power grid, an on-line self-adapting is proposed. An on-line adaptive main component decomposition algorithm is proposed. The main component eigenvector is calculated by using the residual signal as the input main element vector. and reducing the dimension of the detected signal by the main element transformation to obtain a residual error, The main element score of the signal is calculated according to the main element score. Statistical variable of quantity and Q. Through the analysis of the internal change of the PCA model of the reaction system of T2 statistic, the principle of the response of the quantity of Q statistics to the deviation of the signal and detecting system failure. The simulation example verifies The effectiveness of the algorithm is presented. In order to deal with the current overload of the transmission line during the transient state of the power grid, under the unified power flow controller (UPFC), the transient electricity based on the barrier function and the energy function is put forward. The invention relates to a flow overload prevention control method, Transient stability after failure. Based on the results of the stability analysis In contrast to the control of control based on the simulation method and the artificial intelligence method, the paper constructs a control Lyapunov function composed of the power function and the obstacle function of the power grid. The controller limits the characteristic of the boundary value infinite through the obstacle function, and the function of the unified power flow controller to prevent the transmission of the power grid. The transient current of the transmission line is overloaded. The stability of the control system is analyzed by the method of the last non-stable equilibrium point UEP, and the obstacle letter is adjusted by the optimization algorithm. In this paper, the simulation results of the three-node power system and the 162-node power system prove the pre-existing problems in this paper.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM76;TM73
本文編號(hào)：2378670
[Abstract]:With the high-speed development of our country's economy, the power demand of electric power is increasing, and the power system becomes more and more bulky and complex. the potential safety hazard of the power grid is increased due to the fact that the new power equipment is constantly being accessed into the power grid. The network of power system is a non-linear, large-scale, strong-coupled and dynamic complex system. The measurement, calculation, control and communication of the traditional power network monitoring system lack extensive cooperation, and its flexibility and efficiency are still to be improved. The emergence of the smart grid provides a new opportunity for solving the above problems. It is of great significance to study the new theory and method to improve the reliability and safety of the power system. In this paper, the transient security of power system is improved, the fault detection method and the prevention control strategy are developed. The power system transient mathematical model of the integrated power flow controller (UPFC) is built according to the transmission and dynamic characteristics of the power system. In this paper, a local recursive global forward (LRGF) dynamic neural network modeling method based on pole assignment is proposed, and the power grid transient fault detection based on wavelet lifting and on-line self-adaptive main element decomposition is discussed. Finally, aiming at the current overload and transient non-stability of the transmission line of the power system, a control method using UPFC as a control means, an obstacle function and an energy function is proposed. The simulation results verify the effectiveness of the proposed method. Based on the data-based modeling of the transient process of the power grid, an LRGF God based on pole configuration is proposed. through the network, since the poles of the dynamic neuron are present on the real axis and the pair of common complex poles, in order to avoid the projection of the parameters to the stable region, The complexity of the neural network is that the pole of the dynamic filter in the hidden layer neuron is divided into two parts of the real pole and the complex pole according to the case of the pole, and the dynamic part of the two cases is added by the method of the function weight value. The power output, in addition to the new neural network, adopts the learning algorithm of the gradient descent of the derivation gradient, and the power grid transient is realized through the pole projection and weight adjustment learning calculation. Based on the analysis of residual signal in power grid transient fault detection, a wavelet-based lifting and adaptive threshold is presented. The method for detecting the small wave is adaptively designed according to the residual signal and the design principle of the wavelet function. and the residual signal obtained by the difference between the output of the LGF dynamic neural network and the output of the power system is decomposed into a detail signal and an approximation signal by a small wave lifting method. Taking the fault features, detecting the detail signal and the approximation signal through the adaptive threshold, and detecting the slow change by the tolerance time method. and the simulation results verify that the method is in the power grid transient fault detection In order to solve the problem of data processing in the analysis of residual signal in the on-line transient fault detection of the power grid, an on-line self-adapting is proposed. An on-line adaptive main component decomposition algorithm is proposed. The main component eigenvector is calculated by using the residual signal as the input main element vector. and reducing the dimension of the detected signal by the main element transformation to obtain a residual error, The main element score of the signal is calculated according to the main element score. Statistical variable of quantity and Q. Through the analysis of the internal change of the PCA model of the reaction system of T2 statistic, the principle of the response of the quantity of Q statistics to the deviation of the signal and detecting system failure. The simulation example verifies The effectiveness of the algorithm is presented. In order to deal with the current overload of the transmission line during the transient state of the power grid, under the unified power flow controller (UPFC), the transient electricity based on the barrier function and the energy function is put forward. The invention relates to a flow overload prevention control method, Transient stability after failure. Based on the results of the stability analysis In contrast to the control of control based on the simulation method and the artificial intelligence method, the paper constructs a control Lyapunov function composed of the power function and the obstacle function of the power grid. The controller limits the characteristic of the boundary value infinite through the obstacle function, and the function of the unified power flow controller to prevent the transmission of the power grid. The transient current of the transmission line is overloaded. The stability of the control system is analyzed by the method of the last non-stable equilibrium point UEP, and the obstacle letter is adjusted by the optimization algorithm. In this paper, the simulation results of the three-node power system and the 162-node power system prove the pre-existing problems in this paper.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM76;TM73
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 屈微;劉賀平;張德政;;基于獨(dú)立分量分析特征提取的故障診斷系統(tǒng)[J];北京科技大學(xué)學(xué)報(bào);2006年07期
2 李冬輝;王樂英;李晟;;基于PCA的空調(diào)系統(tǒng)傳感器故障診斷[J];電工技術(shù)學(xué)報(bào);2008年06期
3 孔英會(huì);苑津莎;李新葉;趙振兵;;基于時(shí)序特征和參數(shù)估計(jì)的變壓器故障診斷方法[J];電工技術(shù)學(xué)報(bào);2008年12期
4 張佩瑤,馬孝江,王吉軍,朱泓;小波包信號(hào)提取算法及其在故障診斷中的應(yīng)用[J];大連理工大學(xué)學(xué)報(bào);1997年01期
5 趙冬梅,韓月,高曙;電網(wǎng)故障診斷的決策表約簡(jiǎn)新算法[J];電力系統(tǒng)自動(dòng)化;2004年04期
6 馬進(jìn),王景鋼,賀仁睦;電力系統(tǒng)動(dòng)態(tài)仿真的靈敏度分析[J];電力系統(tǒng)自動(dòng)化;2005年17期
7 何小飛;童曉陽(yáng);孫明蔚;;基于貝葉斯網(wǎng)絡(luò)和D-S證據(jù)理論的分布式電網(wǎng)故障診斷[J];電力系統(tǒng)自動(dòng)化;2011年10期
8 段獻(xiàn)忠,袁駿,何仰贊,張德泉;電力系統(tǒng)電壓穩(wěn)定靈敏度分析方法[J];電力系統(tǒng)自動(dòng)化;1997年04期
9 朱永利;王艷;耿蘭芹;蘇丹;;基于貝葉斯網(wǎng)絡(luò)的電網(wǎng)故障診斷[J];電力自動(dòng)化設(shè)備;2007年07期
10 曹一家;劉毅;高振興;郭創(chuàng)新;彭明偉;;一種大規(guī)模電網(wǎng)故障診斷的多智能體信息融合模型與方法[J];電力自動(dòng)化設(shè)備;2010年07期
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