輸電線路雷擊仿真與識(shí)別方法研究
發(fā)布時(shí)間：2018-08-21 20:46
【摘要】：高壓輸電系統(tǒng)是電力系統(tǒng)中承擔(dān)著電能傳輸任務(wù)的重要組成部分，雷擊是造成輸電線路絕緣事故的主要原因之一，對電力系統(tǒng)的危害極大。為了防止雷害故障的頻繁發(fā)生，在現(xiàn)場要求采取的措施一定要有針對性，要針對不同的雷害故障，采取不同的措施。因此不論是進(jìn)行故障處理還是電力系統(tǒng)的防雷改進(jìn)，都必須首先分析清楚雷擊的類型，，否則必然是勞而無功，雷害事故依舊頻繁發(fā)生。因此有必要對雷擊類型的識(shí)別進(jìn)行研究。本文對雷擊220kV輸電線路進(jìn)行了仿真分析，獲取不同類型雷擊的過電壓波形，提取特征量，結(jié)合智能分類方法實(shí)現(xiàn)對雷擊的分類識(shí)別。 采用PSCAD/EMTDC暫態(tài)仿真軟件進(jìn)行220kV輸電線路雷擊仿真�？紤]了桿塔的分段波阻抗模型，對輸電線路雷擊進(jìn)行了建模分析。得到普通短路故障、感應(yīng)雷擊、直擊雷干擾、反擊故障和繞擊故障5種擾動(dòng)下的仿真波形。對仿真得到的電壓波形進(jìn)行了初步的分析，并結(jié)合過電壓產(chǎn)生機(jī)理，分析了不同類型雷擊電壓信號(hào)的時(shí)域波形差異。 通過時(shí)域、頻域、時(shí)頻聯(lián)合分析方法對過電壓信號(hào)進(jìn)行分析，得到了全面反映信號(hào)特點(diǎn)的特征量�；跁r(shí)域波形提取了三相波形相似度W、電壓積分特征值ω和波頭波動(dòng)性B三個(gè)特征量；基于希爾伯特-黃變換和Hilbert時(shí)頻譜提取了反映頻域和時(shí)頻關(guān)聯(lián)的特征量Q1、Q2、Q3、Q4；基于teager能量算子提取了反映波頭變化特性和陡度的特征量Tg和Tm。 考慮到不同擾動(dòng)的特點(diǎn)和各個(gè)特征量的物理意義，為每類擾動(dòng)的識(shí)別選擇相應(yīng)的特征量，以提高識(shí)別的準(zhǔn)確率。雷擊與普通短路故障的差異主要體現(xiàn)在波頭波動(dòng)性B和高低頻能量比值Q2上，雷擊的波頭波動(dòng)性大，高頻分量含量高；感應(yīng)雷擊同一時(shí)刻出現(xiàn)在輸電線路的三相導(dǎo)線中，所以三相波形相似度W和Hilbert能譜矩陣相似度Q3較高，可區(qū)分感應(yīng)雷擊和直擊雷擊。雷擊干擾的電壓信號(hào)附加分量具有周期正負(fù)交變性，故電壓積分特征值ω可用于識(shí)別雷擊干擾。繞擊和反擊的差異體現(xiàn)在波頭特征和高頻能量分布特征，通過計(jì)算反映波頭陡度的Teager能量值，Teager能量值的最大值Tm及反映最大值是否出現(xiàn)在第一個(gè)極值點(diǎn)的特征量Tg，結(jié)合高頻能量分布特征量Q4可有效的區(qū)分反擊與繞擊。 本文研究了相關(guān)向量機(jī)的基本概念、算法及核函數(shù)的選擇。利用多個(gè)二分類相關(guān)向量機(jī)建立了輸電線路雷擊類型的識(shí)別模型。將提取的特征量輸入分類器，為每個(gè)RVM選擇相應(yīng)的特征向量。采用仿真得到的數(shù)據(jù)對識(shí)別模型進(jìn)行訓(xùn)練和測試。最終實(shí)現(xiàn)對輸電線路雷擊類型的智能識(shí)別。大量的仿真分析驗(yàn)證了所建識(shí)別模型的有效性。
[Abstract]:High voltage transmission system is an important part of power transmission task in power system. Lightning strike is one of the main causes of transmission line insulation accidents, which is very harmful to power system. In order to prevent the frequent occurrence of lightning damage faults, the measures required in the field must be targeted, and different measures should be taken according to different lightning damage faults. Therefore, it is necessary to first analyze the type of lightning strike for fault treatment or lightning protection improvement of power system, otherwise, it is inevitable that there is no work and lightning accidents still occur frequently. Therefore, it is necessary to study the recognition of lightning strike type. In this paper, the 220kV transmission lines are simulated and analyzed, the overvoltage waveforms of different types of lightning strikes are obtained, the characteristic quantities are extracted, and the classification and recognition of lightning strikes are realized with intelligent classification method. PSCAD/EMTDC transient simulation software is used to simulate lightning stroke of 220kV transmission line. Considering the sectional wave impedance model of the tower, the lightning stroke of transmission line is modeled and analyzed. The simulation waveforms of five kinds of disturbances, such as common short circuit fault, inductive lightning strike, direct strike lightning disturbance, counterattack fault and wound failure, are obtained. The voltage waveform obtained by simulation is analyzed preliminarily, and the time-domain waveform difference of different types of lightning voltage signals is analyzed in combination with the mechanism of over-voltage generation. The overvoltage signal is analyzed by time-domain, frequency-domain and time-frequency joint analysis method, and the characteristic quantity reflecting the characteristics of the signal is obtained. Based on the time domain waveform, three characteristic variables, namely, the similarity of three-phase waveform W, the voltage integral eigenvalue 蠅 and the wave head volatility B, are extracted, and the characteristic quantity Q _ 1 / Q _ 2Q _ 3Q _ 3 / Q _ 4, which reflects frequency domain and time-frequency correlation, is extracted based on Hilbert-Huang transform and Hilbert time-frequency spectrum. Based on the teager energy operator, the characteristic quantities TG and TM, which reflect the variation characteristics and steepness of the wave head, are extracted. Considering the characteristics of different disturbances and the physical meaning of each characteristic quantity, the corresponding feature variables are selected for the recognition of each kind of disturbance, so as to improve the accuracy of recognition. The difference between lightning stroke and common short-circuit fault is mainly reflected in wave head fluctuation B and ratio Q _ 2 of high and low frequency energy, the wave head of lightning stroke is highly volatile and the content of high frequency component is high, and the inductive lightning strike appears in the three-phase conductor of transmission line at the same time. Therefore, the similarity of three-phase waveform W and Hilbert energy spectrum matrix Q _ 3 is high, which can distinguish induced lightning stroke from direct lightning stroke. The additional components of the voltage signal of lightning disturbance have positive and negative period alternation, so the voltage integral eigenvalue 蠅 can be used to identify the lightning disturbance. The difference between wound strike and counterattack is reflected in the characteristics of wave head and high frequency energy distribution. By calculating the maximum value T _ m of Teager energy value and whether or not the maximum value appears in the first extreme point, the characteristic quantity Q4 of high frequency energy distribution can be used to distinguish the striking back from the round strike. The maximum value of Teager energy and the maximum value of Teager energy reflect the steepness of the wave head and whether the maximum energy value appears in the first extreme point is calculated. In this paper, the basic concept, algorithm and kernel function selection of correlation vector machine are studied. The identification model of lightning strike type of transmission line is established by using multiple binary correlation vector machines. The extracted features are input into the classifier and the corresponding feature vectors are selected for each RVM. The recognition model is trained and tested by the simulation data. Finally, the intelligent identification of lightning strike type of transmission line is realized. A large number of simulation results show the effectiveness of the proposed model.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM863
本文編號(hào)：2196305
[Abstract]:High voltage transmission system is an important part of power transmission task in power system. Lightning strike is one of the main causes of transmission line insulation accidents, which is very harmful to power system. In order to prevent the frequent occurrence of lightning damage faults, the measures required in the field must be targeted, and different measures should be taken according to different lightning damage faults. Therefore, it is necessary to first analyze the type of lightning strike for fault treatment or lightning protection improvement of power system, otherwise, it is inevitable that there is no work and lightning accidents still occur frequently. Therefore, it is necessary to study the recognition of lightning strike type. In this paper, the 220kV transmission lines are simulated and analyzed, the overvoltage waveforms of different types of lightning strikes are obtained, the characteristic quantities are extracted, and the classification and recognition of lightning strikes are realized with intelligent classification method. PSCAD/EMTDC transient simulation software is used to simulate lightning stroke of 220kV transmission line. Considering the sectional wave impedance model of the tower, the lightning stroke of transmission line is modeled and analyzed. The simulation waveforms of five kinds of disturbances, such as common short circuit fault, inductive lightning strike, direct strike lightning disturbance, counterattack fault and wound failure, are obtained. The voltage waveform obtained by simulation is analyzed preliminarily, and the time-domain waveform difference of different types of lightning voltage signals is analyzed in combination with the mechanism of over-voltage generation. The overvoltage signal is analyzed by time-domain, frequency-domain and time-frequency joint analysis method, and the characteristic quantity reflecting the characteristics of the signal is obtained. Based on the time domain waveform, three characteristic variables, namely, the similarity of three-phase waveform W, the voltage integral eigenvalue 蠅 and the wave head volatility B, are extracted, and the characteristic quantity Q _ 1 / Q _ 2Q _ 3Q _ 3 / Q _ 4, which reflects frequency domain and time-frequency correlation, is extracted based on Hilbert-Huang transform and Hilbert time-frequency spectrum. Based on the teager energy operator, the characteristic quantities TG and TM, which reflect the variation characteristics and steepness of the wave head, are extracted. Considering the characteristics of different disturbances and the physical meaning of each characteristic quantity, the corresponding feature variables are selected for the recognition of each kind of disturbance, so as to improve the accuracy of recognition. The difference between lightning stroke and common short-circuit fault is mainly reflected in wave head fluctuation B and ratio Q _ 2 of high and low frequency energy, the wave head of lightning stroke is highly volatile and the content of high frequency component is high, and the inductive lightning strike appears in the three-phase conductor of transmission line at the same time. Therefore, the similarity of three-phase waveform W and Hilbert energy spectrum matrix Q _ 3 is high, which can distinguish induced lightning stroke from direct lightning stroke. The additional components of the voltage signal of lightning disturbance have positive and negative period alternation, so the voltage integral eigenvalue 蠅 can be used to identify the lightning disturbance. The difference between wound strike and counterattack is reflected in the characteristics of wave head and high frequency energy distribution. By calculating the maximum value T _ m of Teager energy value and whether or not the maximum value appears in the first extreme point, the characteristic quantity Q4 of high frequency energy distribution can be used to distinguish the striking back from the round strike. The maximum value of Teager energy and the maximum value of Teager energy reflect the steepness of the wave head and whether the maximum energy value appears in the first extreme point is calculated. In this paper, the basic concept, algorithm and kernel function selection of correlation vector machine are studied. The identification model of lightning strike type of transmission line is established by using multiple binary correlation vector machines. The extracted features are input into the classifier and the corresponding feature vectors are selected for each RVM. The recognition model is trained and tested by the simulation data. Finally, the intelligent identification of lightning strike type of transmission line is realized. A large number of simulation results show the effectiveness of the proposed model.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM863
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