本文選題：機(jī)器學(xué)習(xí) + 故障恢復(fù)　； 參考：《湘潭大學(xué)》2017年碩士論文

【摘要】：智能電網(wǎng)正在日益成為電網(wǎng)技術(shù)的發(fā)展趨勢(shì),智能配電網(wǎng)屬于智能電網(wǎng)當(dāng)中重要的一部分。智能配電網(wǎng)通常具有較為完備的故障診斷以及自愈功能,以此來(lái)提高電網(wǎng)的穩(wěn)定性以及可靠性,同時(shí)智能配電網(wǎng)也支持接入分布式電源。具有優(yōu)秀自愈能力的配電網(wǎng)能夠最大程度減小配電網(wǎng)故障對(duì)于用戶產(chǎn)生的影響,從而提高用戶的用電體驗(yàn)以及配電網(wǎng)的穩(wěn)定性。配電網(wǎng)的故障恢復(fù)是指在配電網(wǎng)的某處發(fā)生故障時(shí),通過(guò)各個(gè)支路的開(kāi)關(guān)以及聯(lián)絡(luò)開(kāi)關(guān)進(jìn)行通斷操作從而改變配電網(wǎng)的結(jié)構(gòu),將受到故障影響的失電區(qū)域負(fù)載轉(zhuǎn)移到其他饋線或者電源進(jìn)行供電,從而使失電負(fù)載恢復(fù)供電。根據(jù)配電網(wǎng)故障信息找到最佳的故障恢復(fù)路徑是故障恢復(fù)的主要任務(wù),這是一個(gè)典型的多目標(biāo)非線性問(wèn)題。本文的主要內(nèi)容和創(chuàng)新點(diǎn)如下:(1)介紹配電網(wǎng)的構(gòu)造以及種類(lèi),對(duì)配電網(wǎng)的故障恢復(fù)技術(shù)研究現(xiàn)狀和存在的技術(shù)難點(diǎn)進(jìn)行了總結(jié)和分析,介紹了當(dāng)前配電網(wǎng)故障恢復(fù)的相關(guān)理論和一些常用的方案。(2)闡述機(jī)器學(xué)習(xí)理論,以及相關(guān)技術(shù)在配電網(wǎng)故障恢復(fù)上的發(fā)展,并提出一種基于回聲狀態(tài)網(wǎng)絡(luò)(ESN)的配電網(wǎng)故障恢復(fù)算法,通過(guò)回聲狀態(tài)網(wǎng)絡(luò)優(yōu)秀的動(dòng)態(tài)特性對(duì)配電網(wǎng)的故障信息進(jìn)行學(xué)習(xí),同時(shí)設(shè)計(jì)樹(shù)形遍歷法結(jié)合ESN的輸出對(duì)配電網(wǎng)的結(jié)構(gòu)進(jìn)行改變,這種方式能使配電網(wǎng)在更改結(jié)構(gòu)的過(guò)程中始終保持輻射狀運(yùn)行條件,因此無(wú)需考慮學(xué)習(xí)過(guò)程中由于學(xué)習(xí)誤差而對(duì)配電網(wǎng)結(jié)構(gòu)約束造成的影響。(3)在基于回聲狀態(tài)網(wǎng)絡(luò)的故障恢復(fù)方案基礎(chǔ)上結(jié)合了非支配目標(biāo)排序遺傳算法-2(NSGA-II),通過(guò)NSGA-II優(yōu)秀的全局搜索能力與多目標(biāo)優(yōu)化能力使配電網(wǎng)系統(tǒng)進(jìn)行非監(jiān)督學(xué)習(xí),這種學(xué)習(xí)方案由于不需要進(jìn)行訓(xùn)練樣本的收集,因此一定程度上減少了時(shí)間成本。(4)以標(biāo)準(zhǔn)16節(jié)點(diǎn)配電網(wǎng)為實(shí)驗(yàn)場(chǎng)景,將本文中研究的基于機(jī)器學(xué)習(xí)技術(shù)的故障恢復(fù)學(xué)習(xí)方案應(yīng)用于配電網(wǎng)的故障恢復(fù)學(xué)習(xí)當(dāng)中,使配電網(wǎng)系統(tǒng)能夠通過(guò)學(xué)習(xí)不同故障狀態(tài)下的配電網(wǎng)故障恢復(fù)方案,從而使系統(tǒng)能夠?qū)Σ煌墓收闲畔⑦M(jìn)行快速反應(yīng)。
[Abstract]:Smart grid is increasingly becoming the development trend of grid technology. Smart distribution network is an important part of smart grid. Smart distribution network usually has more complete fault diagnosis and self-healing functions to improve the stability and reliability of the network, and smart distribution network also supports access to distributed generation. The distribution network with excellent self-healing ability can minimize the influence of distribution network fault on users to improve the user's experience of power consumption and the stability of distribution network. The fault recovery of distribution network is to change the structure of distribution network by switching on and off each branch switch and contact switch when there is a fault somewhere in the distribution network. The load of the power loss area affected by the fault is transferred to other feeders or power sources for power supply, so that the power loss load can be restored. The main task of fault recovery is to find the best fault recovery path according to the fault information of distribution network, which is a typical multi-objective nonlinear problem. The main contents and innovations of this paper are as follows: (1) introducing the structure and types of distribution network, summarizing and analyzing the present situation and technical difficulties of fault recovery technology in distribution network. This paper introduces the related theories of distribution network fault recovery and some commonly used schemes. It describes the theory of machine learning and the development of related technology in distribution network fault recovery. A fault recovery algorithm based on echo state network (ESNN) is proposed. The excellent dynamic characteristics of echo state network are used to learn the fault information of distribution network. At the same time, the tree traversal method combined with the output of ESN is designed to change the structure of the distribution network, which can make the distribution network always keep the radiation operation condition in the process of changing the structure. Therefore, it is unnecessary to consider the influence of learning errors on the distribution network structure constraints in the learning process. (3) based on the fault recovery scheme based on echo state network, the non-dominant target ranking genetic algorithm -2n NSGA-IIG is combined, and NSGA-II is used to optimize the performance. The global search ability and multi-objective optimization ability of the show enable the distribution network system to carry out unsupervised learning. Because the training sample collection is not needed, this learning scheme reduces the time cost to some extent.) the standard 16-node distribution network is used as the experimental scenario. In this paper, the machine learning technology based fault recovery learning scheme is applied to the distribution network fault recovery learning, so that the distribution network system can learn the distribution network fault recovery scheme under different fault states. So that the system can respond to different fault information quickly.
【學(xué)位授予單位】：湘潭大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TM732

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