【摘要】：隨著電網(wǎng)建設(shè)的快速發(fā)展,電網(wǎng)系統(tǒng)的規(guī)模與復(fù)雜程度也日益增大,各信息系統(tǒng)已經(jīng)積累了大量輸變電設(shè)備狀態(tài)信息數(shù)據(jù)。這給運(yùn)檢培訓(xùn)帶來新的問題與挑戰(zhàn)。而傳統(tǒng)的人工教學(xué)培訓(xùn)具有成本昂貴、培訓(xùn)效果不高的特點(diǎn)。因此推動運(yùn)檢培訓(xùn)向標(biāo)準(zhǔn)化、高效化、網(wǎng)絡(luò)化、形象化發(fā)展已經(jīng)是未來的趨勢。 隨著電網(wǎng)設(shè)備運(yùn)行狀態(tài)檢測技術(shù)的發(fā)展,運(yùn)檢決策人員所掌握的設(shè)備狀態(tài)信息量迅速增長,另一方面,信息的組成從以文字和數(shù)字等結(jié)構(gòu)化數(shù)據(jù)為主發(fā)展到圖像和視頻等非結(jié)構(gòu)化數(shù)據(jù)占有數(shù)據(jù)量的絕大部分。隨著機(jī)器人和無人機(jī)等自動檢測平臺在輸變電巡檢中的應(yīng)用,非結(jié)構(gòu)化數(shù)據(jù)的分析需求將大大增加,這些數(shù)據(jù)的特征和規(guī)模導(dǎo)致依靠專家的人工分析已經(jīng)無法及時處理,制約了上述先進(jìn)檢測手段在運(yùn)檢決策中發(fā)揮作用。 本文通過研究基于輸變電設(shè)備在線監(jiān)測、帶電檢測等多源異構(gòu)數(shù)據(jù)的檢測、狀態(tài)評價(jià)與檢修技能的培訓(xùn)與考核技術(shù),實(shí)現(xiàn)培訓(xùn)系統(tǒng)與狀態(tài)評價(jià)系統(tǒng)的數(shù)據(jù)共享和知識共享,提高培訓(xùn)水平和效率。本文針對運(yùn)檢多源異構(gòu)檢測數(shù)據(jù),研究故障與檢測數(shù)據(jù)間關(guān)聯(lián)性,提取故障特征信息,并通過對變壓器、斷路器典型故障案例庫建模與分析,紅外影像庫和超聲超高頻檢測圖譜庫的建模與分析,建立典型故障庫和紅外檢測圖譜庫在線培訓(xùn)系統(tǒng),供學(xué)員學(xué)習(xí)。最后基于多源異構(gòu)數(shù)據(jù)、故障案例庫建立基于大數(shù)據(jù)的輸變電運(yùn)檢培訓(xùn)系統(tǒng),并開發(fā)手機(jī)應(yīng)用,方便學(xué)員學(xué)習(xí)。 總體研究表明,本文所研究的基于大數(shù)據(jù)的運(yùn)檢培訓(xùn)技術(shù),為運(yùn)檢培訓(xùn)提供了一種新方法,可有效提高設(shè)備故障診斷和檢修決策水平,能夠產(chǎn)生顯著的經(jīng)濟(jì)和安全效益。
[Abstract]:With the rapid development of power grid construction, the scale and complexity of power grid system is increasing day by day. The information system has accumulated a large number of information data of transmission and transformation equipment. This brings new problems and challenges to operation and inspection training. The traditional manual teaching and training has the characteristics of high cost and low training effect. Therefore, it is the future trend to promote the standardization, high efficiency, network and visualization of operation and inspection training. With the development of power network equipment operating state detection technology, the amount of equipment state information grasped by operation inspection decision makers increases rapidly, on the other hand, From structured data such as text and number to unstructured data such as image and video, the information consists of most of the data. With the application of automatic detection platform such as robot and UAV in power transmission and transformation inspection, the demand for analysis of unstructured data will be greatly increased. The characteristics and scale of these data make it impossible to deal with them in time depending on the manual analysis of experts. It restricts the above advanced detection means to play a role in the operation and inspection decision. In this paper, the data sharing and knowledge sharing between the training system and the condition evaluation system are realized through the research of the multi-source heterogeneous data detection, such as on-line monitoring of transmission and transformation equipment, live detection and so on, as well as the training and examination technology of condition evaluation and maintenance skills. Improve training level and efficiency. In this paper, we study the relationship between fault and detection data, extract fault characteristic information, and model and analyze the typical fault database of transformer and circuit breaker. The modeling and analysis of infrared image library and ultrasonic ultra-high frequency test atlas library are carried out, and the online training system of typical fault database and infrared detection atlas library is established for students to learn. Finally, based on the multi-source and heterogeneous data, the fault case database is used to establish the training system of transmission and substation operation based on big data, and the mobile phone application is developed to facilitate the students to learn. The overall research shows that the training technology based on big data provides a new method for operation and inspection training, which can effectively improve the level of equipment fault diagnosis and maintenance decision, and can produce significant economic and safety benefits.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP311.13;TM76

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本文編號：2314651