【摘要】：小電流接地系統(tǒng)是指中低壓配電網(wǎng)絡(luò)中，，采用中性點(diǎn)經(jīng)消弧線圈接地、經(jīng)大電阻接地或者中性點(diǎn)不接地等運(yùn)行方式的系統(tǒng)。通常情況下，小電流系統(tǒng)發(fā)生故障后，故障電流十分微弱，不容易被檢測(cè)到，這對(duì)故障選線問(wèn)題提出了巨大挑戰(zhàn)。目前小電流接地故障選線方法主要分為三類：一是利用故障穩(wěn)態(tài)特征分量進(jìn)行選線，二是利用故障暫態(tài)特征分量進(jìn)行選線，三是注入特殊信號(hào)進(jìn)行選線。這些方法在實(shí)際應(yīng)用中均取得了一些成效，但從選線的準(zhǔn)確率和穩(wěn)定性上來(lái)說(shuō)還遠(yuǎn)遠(yuǎn)不夠理想。 概率神經(jīng)網(wǎng)絡(luò)是一種可用于模式分類的神經(jīng)網(wǎng)絡(luò)，在機(jī)械、材料、環(huán)境工程乃至經(jīng)濟(jì)領(lǐng)域有著較好應(yīng)用，而在配電網(wǎng)故障選線上很少有人嘗試。經(jīng)過(guò)反復(fù)研究和探索，尋找到配電線路零序電流小波能量、有功分量和五次諧波分量三種故障特征量作為選線的依據(jù)。對(duì)故障模式進(jìn)行了合理定義，重點(diǎn)突破了概率神經(jīng)網(wǎng)絡(luò)中多種故障特征量有效融合的問(wèn)題，提出一種基于概率神經(jīng)網(wǎng)絡(luò)的模式識(shí)別選線方法。 通過(guò)對(duì)小電流接地系統(tǒng)模型進(jìn)行大量Matlab仿真試驗(yàn)，研究了故障位置、接地電阻、故障合閘角、中性點(diǎn)接地方式、配電線路結(jié)構(gòu)和噪聲干擾對(duì)故障特征量的影響，收集了大量故障數(shù)據(jù)樣本。同時(shí)對(duì)電弧高阻接地、混合線纜配電網(wǎng)、噪聲干擾下的接地故障以及中性點(diǎn)不同接地方式系統(tǒng)的概率神經(jīng)網(wǎng)絡(luò)模式識(shí)別選線進(jìn)行了廣泛試驗(yàn)，驗(yàn)證了該方法具有較好的通用性和抗干擾能力。將該方法與概率神經(jīng)網(wǎng)絡(luò)單一特征量選線和BP神經(jīng)網(wǎng)絡(luò)故障選線方法進(jìn)行了比較，證明了該方法具有準(zhǔn)確率高，操作簡(jiǎn)單快速，故障知識(shí)豐富，易于拓展知識(shí)庫(kù)等特點(diǎn)。最后提出了一套本方法的故障選線裝置設(shè)計(jì)方案。
[Abstract]:Low current grounding system is a system in which neutral point is grounded by arc-suppression coil grounding through large resistance or neutral point is not grounded in medium and low voltage distribution network. Usually, the fault current is very weak after the failure of the small current system, and it is not easy to detect, which poses a great challenge to the fault line selection problem. At present, there are three kinds of fault line selection methods for low current grounding fault: one is to select the line by using the steady-state characteristic component of the fault, the other is to select the line by using the transient characteristic component of the fault, and the third is to inject special signals to select the line. These methods have achieved some results in practical application, but they are far from ideal in terms of accuracy and stability of line selection. Probabilistic neural network (PNN) is a kind of neural network which can be used for pattern classification. It has good applications in mechanical, material, environmental engineering and even economic fields, but it is seldom tried in fault line selection of distribution network. Through repeated research and exploration, three fault characteristic quantities of zero-sequence current wavelet energy, active power component and fifth harmonic component of distribution line are found as the basis of line selection. In this paper, the fault mode is defined reasonably, and the problem of effective fusion of multiple fault features in probabilistic neural network is broken through, and a method of pattern recognition and line selection based on probabilistic neural network is proposed. Through a large number of Matlab simulation tests on the small current grounding system model, the effects of fault location, grounding resistance, fault closing angle, neutral grounding mode, distribution line structure and noise interference on the fault characteristic quantity are studied. A large number of fault data samples were collected. At the same time, a wide range of experiments have been carried out on arc high resistance grounding, hybrid cable distribution network, grounding fault under noise interference and probabilistic neural network pattern recognition of different neutral grounding mode systems. It is proved that this method has good generality and anti-interference ability. The method is compared with the probabilistic neural network single feature selection method and the BP neural network fault line selection method. It is proved that this method has the advantages of high accuracy, simple and fast operation, rich fault knowledge and easy to expand the knowledge base. Finally, a design scheme of fault line selection device based on this method is put forward.
【學(xué)位授予單位】：南昌大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM862

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