本文選題：配電網(wǎng) + 故障診斷��； 參考：《青島大學(xué)》2017年碩士論文

【摘要】：配電網(wǎng)故障診斷是電力系統(tǒng)研究的重要問題,配電網(wǎng)故障的快速診斷對減少配電網(wǎng)斷電區(qū)域和縮短配電網(wǎng)斷電時間具有重大意義,同時也起到了改善供電質(zhì)量和提高配電網(wǎng)運行效率的作用。隨著配電網(wǎng)技術(shù)的不斷進步,其智能化程度越來越高,配電網(wǎng)正趨向越來越自動化的方向發(fā)展。在配電網(wǎng)發(fā)生故障時電網(wǎng)監(jiān)視系統(tǒng)和數(shù)據(jù)采集系統(tǒng)(SCADA)會將各種信息傳到管理中心來幫助我們進行分析,但故障處理人員需要從大量信息中快速判斷和處理故障,這在故障判定時間上提出了很高的要求,因此在配電網(wǎng)監(jiān)控系統(tǒng)中加入故障診斷模塊有重要的實際價值。本文對目前常用的配電網(wǎng)故障診斷的人工智能方法進行了整理,總結(jié)了配電網(wǎng)故障診斷各種方法的工作原理及優(yōu)缺點。并構(gòu)建了基于因果網(wǎng)絡(luò)的配電網(wǎng)故障診斷模型,根據(jù)配電網(wǎng)繼電保護配置,充分利用因果網(wǎng)絡(luò)快速的逆向推理能力,運用故障發(fā)生時配電網(wǎng)系統(tǒng)中產(chǎn)生的各種告警信息,將配電網(wǎng)各設(shè)備的關(guān)聯(lián)抽象成三類基本因果關(guān)系,運用因果網(wǎng)絡(luò)做邏輯逆向推理,實現(xiàn)配電網(wǎng)故障診斷。通過實際算例的分析驗證了該模型能取得較為理想的診斷效果,證明該研究具有一定的實用價值。隨著配電網(wǎng)的發(fā)展,大量的分布式電源(DG)的接入勢必會對常規(guī)配電網(wǎng)的運行、控制以及保護配置造成影響,從而對配電網(wǎng)的故障診斷提出新的要求,本文對含DG的配電網(wǎng)系統(tǒng)做了簡要介紹,介紹了分布式發(fā)電的基本知識以及目前主要的分布式發(fā)電技術(shù)及其運行方式。在這種運行方式下為了適應(yīng)DG接入對保護配置帶來的影響,引入虛擬節(jié)點的概念構(gòu)造了含DG的配電網(wǎng)因果網(wǎng)絡(luò)診斷模型。此外當配電網(wǎng)出現(xiàn)故障時,若故障信息受到干擾而丟失或畸變,因果網(wǎng)絡(luò)糾錯能力弱的短板就會突顯出來。為了降低誤判率,在原來的基礎(chǔ)上首先對各種信息進行預(yù)處理,提出了基于信息預(yù)處理的改進因果網(wǎng)絡(luò)對含DG的配電網(wǎng)故障診斷方法,實現(xiàn)了在保護和斷路器存在誤動或者拒動等情況下對含DG的智能配電網(wǎng)的故障診斷。并用實際算例驗證了改進因果網(wǎng)絡(luò)在含DG配電網(wǎng)故障診斷中的優(yōu)越性,證明了該模型的實用性。
[Abstract]:Distribution network fault diagnosis is an important problem in the research of power system. The rapid diagnosis of distribution network fault is of great significance to reduce the distribution network power failure area and shorten the distribution network power failure time. At the same time, it also plays the role of improving the quality of power supply and improving the efficiency of distribution network operation. With the development of distribution network technology, its intelligence is becoming higher and higher, and the distribution network is becoming more and more automatic. Power network monitoring system and data acquisition system (SCADA) will transmit all kinds of information to the management center to help us analyze when the distribution network fails, but the fault handler needs to quickly judge and deal with the fault from a large amount of information. Therefore, it is of great practical value to add fault diagnosis module to the monitoring system of distribution network. In this paper, the common artificial intelligence methods of distribution network fault diagnosis are summarized, and the working principles, advantages and disadvantages of the methods are summarized. The fault diagnosis model of distribution network based on causality network is constructed. According to the configuration of relay protection in distribution network, the rapid reverse reasoning ability of causal network is fully utilized, and all kinds of alarm information generated in distribution network system are used when the fault occurs. The connection of each equipment in distribution network is abstracted into three kinds of basic causality, and the fault diagnosis of distribution network is realized by using causality network to do logic reverse reasoning. Through the analysis of practical examples, it is proved that the model can obtain more ideal diagnostic effect, and that the research has certain practical value. With the development of the distribution network, the access of a large number of distributed generation (DG) will inevitably affect the operation, control and protection configuration of the conventional distribution network, thus putting forward new requirements for the fault diagnosis of the distribution network. This paper briefly introduces the distribution network system with DG, introduces the basic knowledge of distributed generation, the main distributed generation technology and its operation mode. In order to adapt to the influence of DG access on protection configuration, a causal network diagnosis model with DG is constructed by introducing the concept of virtual node. In addition, when the fault occurs in the distribution network, if the fault information is lost or distorted due to interference, the weak error correction ability of the causal network will be highlighted. In order to reduce the error rate, the paper first preprocesses all kinds of information on the basis of the original information, and proposes an improved causality network based on information preprocessing for fault diagnosis of distribution network with DG. The fault diagnosis of the intelligent distribution network with DG is realized under the condition of the protection and the maloperation or the failure of the circuit breaker. The advantages of improved causality network in fault diagnosis of distribution network with DG are verified by a practical example, and the practicability of the model is proved.
【學(xué)位授予單位】：青島大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM711

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