發(fā)布時間：2018-03-06 15:04

  本文選題：過電壓　切入點：ATP-EMTP　出處：《西安理工大學》2017年碩士論文　論文類型：學位論文

【摘要】：電力系統(tǒng)出現(xiàn)擾動或故障時產(chǎn)生的過電壓是危害電網(wǎng)安全運行的重要因素之一,過電壓可毀壞電氣設備,導致供電中斷甚至大范圍停電等嚴重事故,不同類型的過電壓有著不同的產(chǎn)生原理,其防范措施也各不相同。因此,如何準確、迅速的識別出不同類型的過電壓,對及時排除故障、保障電力系統(tǒng)穩(wěn)定安全運行有著重大意義。本文根據(jù)某變電站現(xiàn)場實際接線及相關電氣設備具體參數(shù),對線路、變壓器、電源、負荷、避雷器等主要設備在ATP-EMTP仿真軟件中進行模型選擇及參數(shù)計算,完成設備參數(shù)設置并搭建該變電站過電壓仿真模型,對電容器組合閘、空載線路合閘、空載變壓器合閘、單相接地、直擊雷以及感應雷過電壓等6種常見的典型過電壓波形進行了仿真計算,結合各自過電壓產(chǎn)生原理,分析并總結了每一過電壓波形特點。根據(jù)仿真計算結果,進行了變電站過電壓特征量提取的研究。為了對不同等級過電壓波形進行比較,對其進行了歸一化處理。依據(jù)不同過電壓持續(xù)時間特點,對仿真得到的波形區(qū)間劃分為3個時間段,在此基礎上,根據(jù)不同波形間的差異選取不同區(qū)間,采用時域分析法提取了 6個相對明顯的特征量,對直觀上不易區(qū)分的波形,采取小波分析法提取了8個具有較好差異性的特征量,并利用仿真數(shù)據(jù)驗證了該14個特征量差異的有效性。最后,綜合仿真得出的6種變電站過電壓波形特征,構建了基于支持向量機的過電壓分層識別結構并設計識別流程,可由上到下對所提取的特征量在每層分類器中進行選擇。為驗證該識別系統(tǒng)的有效性,根據(jù)仿真得到480條過電壓數(shù)據(jù),240條用于訓練,240條用于測試。結果表明,本文所采用的支持向量機的識別準確率接近88%,識別時間近乎2.5s,相比傳統(tǒng)識別方法準確率提升約12%,時間縮短約3s,該方法準確性高,速度快,能夠有效對不同類型過電壓進行識別,為今后防止過電壓的工程應用中提供技術支持。
[Abstract]:Power system disturbance or fault overvoltage produced is one of the important factors endangering the safety of power grid, overvoltage can destroy the electrical equipment, causing power outages or blackout accident, overvoltage of different type has different principle, the prevention measures are also different. Therefore, how to accurately quickly, identify different types of overvoltage, timely troubleshooting, is of great significance to ensure the safe and stable operation of power system. According to the actual substation wiring and electrical equipment related to specific parameters of the circuit, transformer, power supply, load, main equipment of lightning arrester for model selection and the parameter in the ATP-EMTP simulation software in the calculation, complete set of device parameters and build the substation overvoltage simulation model of capacitor combination switch, no-load circuit switch, transformer no-load switch, single-phase grounding, Direct lightning and lightning over-voltage to 6 kinds of typical voltage waveform is simulated, with their respective overvoltage principle, analyzes and summarizes each waveform characteristics. Based on the simulation results, the research on substation overvoltage feature extraction. In order to compare the different grade voltage waveform and the normalized. According to different voltage duration characteristics of waveform interval division by simulation for the 3 time, on this basis, according to the difference between the different waveform interval, time-domain extraction of 6 relatively obvious characteristic analysis method is used to visually indistinguishable take the 8 waveform extraction has better difference characteristics of wavelet analysis, and using the simulation data to verify the validity of the 14 characteristics of volume differences. Finally, 6 comprehensive results Substation overvoltage waveform characteristics, construct the over-voltage layeredidentification structure design and process identification based on support vector machine, from top to bottom on the features extracted in each layer of the classifier selection. To verify the effectiveness of the recognition system, according to the simulated 480 overvoltage data, 240 for training 240, for testing. The results show that the support vector machine recognition the recognition accuracy rate close to 88%, almost 2.5s time, compared with the traditional identification methods to enhance the accuracy of about 12%, shorten the time of about 3S, the method is of high accuracy, fast speed, can effectively identify the different types of overvoltage, to prevent future provide technical support for engineering application of voltage.

【學位授予單位】：西安理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM63

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本文編號：1575328