本文關(guān)鍵詞： 智能配電網(wǎng) 風(fēng)險(xiǎn)評(píng)估 最優(yōu)變權(quán) 風(fēng)險(xiǎn)值 多時(shí)段　出處：《山東大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來(lái),針對(duì)智能電網(wǎng)的研究和建設(shè)成為世界各國(guó)的關(guān)注焦點(diǎn),隨著電網(wǎng)智能化進(jìn)程的快速推進(jìn),智能電網(wǎng)的發(fā)展在取得顯著成效的同時(shí),也面臨著極大的挑戰(zhàn)。由于智能配電網(wǎng)直接面向電力用戶,且積極支持分布式電源等新能源電力設(shè)施的并入,這不但加深了配電網(wǎng)調(diào)度與運(yùn)行管理的復(fù)雜程度,而且給配電網(wǎng)的施工和運(yùn)行維護(hù)帶來(lái)了更多的限制因素,因此導(dǎo)致配電網(wǎng)面臨的風(fēng)險(xiǎn)類型更為繁雜、受到的風(fēng)險(xiǎn)影響更為突出。為了可靠地保證智能配電網(wǎng)的正常運(yùn)營(yíng),須實(shí)時(shí)掌握其面臨的風(fēng)險(xiǎn)類型及嚴(yán)重程度,故應(yīng)對(duì)網(wǎng)絡(luò)的風(fēng)險(xiǎn)狀態(tài)進(jìn)行實(shí)時(shí)評(píng)估。首先,本文在剖析智能配電網(wǎng)特征的基礎(chǔ)上,選取目前影響網(wǎng)絡(luò)正常運(yùn)行的關(guān)鍵風(fēng)險(xiǎn)因素,按照風(fēng)險(xiǎn)評(píng)估的基本準(zhǔn)則和一般流程,建立多層級(jí)網(wǎng)絡(luò)風(fēng)險(xiǎn)評(píng)估體系;根據(jù)實(shí)際工程的需要,將網(wǎng)絡(luò)風(fēng)險(xiǎn)狀態(tài)劃分為5個(gè)等級(jí),并量化為具體的風(fēng)險(xiǎn)值區(qū)間,為直觀地評(píng)判網(wǎng)絡(luò)的運(yùn)營(yíng)情況提供了極大的便利。然后,本文提出了一套針對(duì)網(wǎng)絡(luò)風(fēng)險(xiǎn)評(píng)估體系的最優(yōu)變權(quán)分配方案。考慮到傳統(tǒng)的主觀賦權(quán)方法在分配權(quán)重時(shí)常常忽略專家主觀意識(shí)對(duì)處理過(guò)程的影響,本文基于相似度聚類分析定義了專家權(quán)威關(guān)聯(lián)系數(shù)這一新概念,將其與基于模糊層次分析法確定的權(quán)重相融合,以使確定的主觀權(quán)重更為有效;為了充分計(jì)及評(píng)估指標(biāo)參數(shù)的時(shí)序特征,本文利用熵值改進(jìn)傳統(tǒng)CRITIC賦權(quán)法,根據(jù)各微觀指標(biāo)在網(wǎng)絡(luò)發(fā)展的歷史階段、當(dāng)前階段和未來(lái)階段的評(píng)估值,確定指標(biāo)的客觀權(quán)重;將兩類權(quán)重系數(shù)整合為指標(biāo)的最優(yōu)變權(quán),將傳統(tǒng)的靜態(tài)評(píng)估轉(zhuǎn)化為結(jié)合趨勢(shì)分析的動(dòng)態(tài)評(píng)估,為全面掌握網(wǎng)絡(luò)的運(yùn)行狀況提供了更為可靠的理論依據(jù)。其次,本文具體闡述了各微觀指標(biāo)風(fēng)險(xiǎn)值的計(jì)算過(guò)程。針對(duì)定性評(píng)估指標(biāo),本文基于集值統(tǒng)計(jì)專家估價(jià)法對(duì)其進(jìn)行合理的評(píng)判,考慮到評(píng)估過(guò)程中存在各種模糊性、隨機(jī)性和專家心理波動(dòng)等不確定因素,引入專家評(píng)估信任因子度量評(píng)判結(jié)果的信賴度;針對(duì)定量評(píng)估指標(biāo),本文建立了嶺形模糊隸屬度函數(shù)作為其評(píng)估函數(shù),并通過(guò)算例證明了本文構(gòu)建的評(píng)估函數(shù)相比于傳統(tǒng)評(píng)估函數(shù)在性能上具備明顯的優(yōu)越性。最后,本文以某10KV智能配電網(wǎng)示范工程作為研究對(duì)象,詳細(xì)地闡述了網(wǎng)絡(luò)整體風(fēng)險(xiǎn)值的求取過(guò)程。通過(guò)將計(jì)算結(jié)果和網(wǎng)絡(luò)實(shí)際運(yùn)行狀態(tài)作對(duì)比可以證明本文提出的智能配電網(wǎng)風(fēng)險(xiǎn)評(píng)估方法能夠有效地識(shí)別網(wǎng)絡(luò)發(fā)展過(guò)程中面臨的主要風(fēng)險(xiǎn)類型,并可對(duì)網(wǎng)絡(luò)面臨的風(fēng)險(xiǎn)嚴(yán)重程度作出較為準(zhǔn)確的量化,對(duì)于全面推進(jìn)智能配電網(wǎng)的趨優(yōu)發(fā)展具有重要的指導(dǎo)意義。
[Abstract]:In recent years, the research and construction of smart grid has become the focus of attention all over the world. With the rapid progress of intelligent power grid, the development of smart grid has achieved remarkable results at the same time. Because smart distribution network is directly oriented to power users and actively supports the integration of new energy power facilities such as distributed generation, this not only deepens the complexity of distribution network dispatching and operation management. Moreover, it brings more restrictive factors to the construction and operation and maintenance of distribution network, which leads to more complicated risk types faced by distribution network. In order to ensure the normal operation of smart distribution network, it is necessary to grasp the risk types and severity in real time, so the risk state of the network should be evaluated in real time. On the basis of analyzing the characteristics of smart distribution network, this paper selects the key risk factors that affect the normal operation of the network, and establishes a multi-level network risk assessment system according to the basic criteria and general process of risk assessment. According to the need of the actual project, the network risk state is divided into five grades, and quantified into the specific risk value interval, which provides a great convenience for the intuitive evaluation of the network operation. Then. In this paper, an optimal variable weight allocation scheme for network risk assessment system is proposed, considering that the traditional subjective weighting method often ignores the influence of the subjective consciousness of experts on the processing process. In this paper, a new concept of expert authoritative correlation coefficient is defined based on similarity clustering analysis, which is combined with the weight determined by fuzzy analytic hierarchy process (FAHP) to make the subjective weight more effective. In order to fully take into account the time series characteristics of the evaluation index parameters, this paper uses entropy to improve the traditional CRITIC weighting method, according to the microcosmic indicators in the historical stage of network development, the current stage and the future stage of the evaluation value. To determine the objective weight of the index; Integrating the two kinds of weight coefficients as the optimal variable weight of the index and transforming the traditional static evaluation into the dynamic evaluation combined with trend analysis provides a more reliable theoretical basis for the overall operation of the network. Secondly. In this paper, the calculation process of the risk value of each micro index is elaborated. Aiming at the qualitative evaluation index, this paper makes a reasonable evaluation on it based on the set value statistical expert valuation method, considering the various fuzziness in the process of evaluation. The uncertainty factors such as randomness and psychological fluctuation of experts are introduced to evaluate the reliability of the evaluation results of the trust factor. For the quantitative evaluation index, this paper establishes the ridge fuzzy membership function as its evaluation function. An example shows that the evaluation function constructed in this paper has obvious superiority compared with the traditional evaluation function. Finally, this paper takes a 10KV smart distribution network demonstration project as the research object. The calculation process of the whole network risk value is expounded in detail. By comparing the calculation result with the actual network running state, it can be proved that the intelligent distribution network risk assessment method proposed in this paper can effectively identify the developed network. The main types of risk in the process. Furthermore, it can accurately quantify the severity of the risks faced by the network, which has an important guiding significance for the overall development of intelligent distribution network.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM76

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本文編號(hào)：1443169