本文選題：接地網(wǎng) + 故障診斷��； 參考：《湖南大學(xué)》2014年碩士論文

【摘要】：發(fā)、變電站接地網(wǎng)是電力系統(tǒng)中不可或缺的重要組成部分，接地系統(tǒng)的工作狀態(tài)直接影響到工作人員的安全、電力系統(tǒng)的穩(wěn)定運行和電氣設(shè)備的正常工作。我國主要采用碳鋼作為接地體，由于長年埋于地下，接地網(wǎng)導(dǎo)體的腐蝕造成接地網(wǎng)電氣性能參數(shù)惡化，嚴(yán)重時直接危及電力系統(tǒng)安全運行，因此，研究接地網(wǎng)故障診斷具有重要意義。鑒于此，本文對接地網(wǎng)模型的構(gòu)建、基于極限學(xué)習(xí)機的接地網(wǎng)故障定位方法以及應(yīng)用概率神經(jīng)網(wǎng)絡(luò)的接地網(wǎng)腐蝕程度的識別等進行了較深入的研究。 本文采用純電阻線性網(wǎng)絡(luò)的接地網(wǎng)穩(wěn)態(tài)模型，提出了基于RBF神經(jīng)網(wǎng)絡(luò)的接地網(wǎng)故障診斷方法。以支路斷裂時的可及節(jié)點電壓為訓(xùn)練樣本對網(wǎng)絡(luò)進行訓(xùn)練，只需要把待診斷接地網(wǎng)的可及節(jié)點電壓送入訓(xùn)練好的網(wǎng)絡(luò)機進行診斷，根據(jù)輸出結(jié)果即可定位斷裂支路。仿真結(jié)果表明，此方法在定位接地網(wǎng)斷裂支路上是可行的。在此基礎(chǔ)上，將極限學(xué)習(xí)機引入接地網(wǎng)故障診斷。對診斷結(jié)果進行分析可知此方法解決了采用RBF神經(jīng)網(wǎng)絡(luò)的單故障診斷結(jié)果誤差較大的問題，且受故障位置與激勵位置的影響較小，是一種診斷準(zhǔn)確率高且穩(wěn)定性強的方法。同時，考慮到雙故障時訓(xùn)練樣本的龐大與輸入時的誤差，對極限學(xué)習(xí)機進行了改進，即通過觀察識別率，排序最有可能的幾條故障支路，并引入白噪聲擾動。診斷結(jié)果表明，改進后的極限學(xué)習(xí)機不需要獲取龐大雙故障訓(xùn)練樣本，，僅利用單故障訓(xùn)練樣本就能較為準(zhǔn)確地定位雙斷點故障，大大提高了診斷效率，并且對輸入誤差具有較大的相容度。 針對接地網(wǎng)故障模式較多分類困難的問題，提出了結(jié)合主元分析(PCA)與概率神經(jīng)網(wǎng)絡(luò)（PNN）的接地網(wǎng)故障診斷方法。把不同故障模式下的接地網(wǎng)可及節(jié)點電壓分別先后送入PCA與PNN進行網(wǎng)絡(luò)訓(xùn)練，根據(jù)PNN的輸出結(jié)果來識別接地網(wǎng)的故障模式。其結(jié)果與利用BP神經(jīng)網(wǎng)絡(luò)進行接地網(wǎng)的腐蝕程度識別的結(jié)果相比較，表明此方法具有更高的故障識別率，更少的收斂步數(shù)與更短的訓(xùn)練時間，是一種快速、準(zhǔn)確的接地網(wǎng)故障識別方法。
[Abstract]:Substation grounding network is an indispensable part of power system. The working state of grounding system directly affects the safety of staff, the stable operation of power system and the normal operation of electrical equipment. Carbon steel is mainly used as grounding material in China. The corrosion of grounding grid conductors results in the deterioration of electrical performance parameters of grounding grid, which directly endangers the safe operation of power system. It is of great significance to study the fault diagnosis of grounding grid. In view of this, the construction of grounding grid model, the fault location method of grounding grid based on ultimate learning machine and the identification of corrosion degree of grounding grid based on probabilistic neural network are deeply studied in this paper. In this paper, a fault diagnosis method of grounding grid based on RBF neural network is presented, which is based on the steady-state model of pure resistive linear network. When the reachable node voltage is used as the training sample to train the network, only the reachable node voltage of the grounding network to be diagnosed is sent to the trained network machine for diagnosis, and the broken branch can be located according to the output results. The simulation results show that this method is feasible in locating the fault branch of grounding grid. On this basis, the ultimate learning machine is introduced into fault diagnosis of grounding grid. The analysis of diagnosis results shows that this method solves the problem of large error in single fault diagnosis using RBF neural network, and is less affected by fault location and excitation position. It is a method with high diagnostic accuracy and strong stability. At the same time, considering the large number of training samples and the error of input, the paper improves the extreme learning machine, that is, by observing the recognition rate, ranking the most likely fault branches, and introducing white noise disturbance. The diagnosis results show that the improved extreme learning machine does not need to obtain a large number of double fault training samples, and the single fault training sample can be used to locate the double breakpoint fault accurately, which greatly improves the diagnosis efficiency. And the input error has a large degree of compatibility. In order to solve the problem that the fault pattern of grounding grid is more difficult to classify, a fault diagnosis method of grounding grid based on principal component analysis (PCA) and probabilistic neural network (PNN) is proposed. The voltages of reachable nodes in different fault modes are fed into PCA and PNN respectively for network training, and the fault modes of grounding grids are identified according to the output results of PNN. The results show that the method has higher fault identification rate, less convergence steps and shorter training time, and it is a kind of fast method, compared with the result of using BP neural network to identify the corrosion degree of grounding grid. Accurate fault identification method of grounding grid.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM862

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