本文選題：負(fù)荷優(yōu)化分配 + 全局人工魚(yú)群算法��； 參考：《太原理工大學(xué)》2017年碩士論文

【摘要】：鋼鐵企業(yè)作為電力行業(yè)中最為典型的用電大戶,其電能的消耗在生產(chǎn)成本中占有很大比重。隨著經(jīng)濟(jì)下滑,鋼鐵企業(yè)負(fù)荷量嚴(yán)重縮水,因此導(dǎo)致企業(yè)配電變壓器運(yùn)行損耗劇增。本課題針對(duì)首鋼長(zhǎng)治鋼鐵有限公司中變壓器普遍處于非經(jīng)濟(jì)運(yùn)行狀態(tài)的問(wèn)題,通過(guò)結(jié)合該企業(yè)實(shí)際負(fù)荷情況構(gòu)建負(fù)荷預(yù)測(cè)模型,并對(duì)長(zhǎng)鋼站下屬鋼南、鋼西、鋼北三站的負(fù)荷進(jìn)行重新分配,之后根據(jù)負(fù)荷波動(dòng)的情況提出變壓器經(jīng)濟(jì)運(yùn)行控制策略,用以實(shí)現(xiàn)變壓器最優(yōu)運(yùn)行并達(dá)到降低該企業(yè)變壓器運(yùn)行損耗的目的。主要包括以下幾個(gè)方面:(1)本文總結(jié)和分析現(xiàn)有負(fù)荷預(yù)測(cè)方法,比較傳統(tǒng)最小二乘支持向量機(jī)與神經(jīng)網(wǎng)絡(luò)在負(fù)荷預(yù)測(cè)應(yīng)用中的優(yōu)劣,重點(diǎn)分析采用最小二乘支持向量機(jī)進(jìn)行負(fù)荷預(yù)測(cè)的不足以及現(xiàn)有最優(yōu)最小二乘支持向量機(jī)模型的特點(diǎn),提出將對(duì)于二維空間尋優(yōu)具有較高精度的全局人工魚(yú)群算法與最小二乘支持向量機(jī)相結(jié)合,構(gòu)建新的負(fù)荷預(yù)測(cè)模型,并結(jié)合該企業(yè)長(zhǎng)鋼站的負(fù)荷驗(yàn)證其預(yù)測(cè)模型的準(zhǔn)確性。最后對(duì)長(zhǎng)鋼站未來(lái)五年負(fù)荷進(jìn)行預(yù)測(cè),該預(yù)測(cè)值同時(shí)為后續(xù)負(fù)荷分配提供了新的思路。(2)本文考察了當(dāng)前首鋼長(zhǎng)治鋼鐵企業(yè)負(fù)荷現(xiàn)狀,對(duì)變壓器經(jīng)濟(jì)負(fù)載系數(shù)以及經(jīng)濟(jì)運(yùn)行區(qū)間進(jìn)行分析。考慮到由于負(fù)荷的地理因素在分配過(guò)程中將產(chǎn)生新架設(shè)線路的情況,這里通過(guò)考察各線路負(fù)荷情況,依據(jù)最新山西國(guó)家電網(wǎng)架空線以及桿塔選擇條件對(duì)每條線路的架空線類(lèi)型以及桿塔數(shù)量進(jìn)行選擇,并將其費(fèi)用計(jì)入負(fù)荷分配優(yōu)化過(guò)程中。為了同時(shí)滿足變壓器經(jīng)濟(jì)運(yùn)行和負(fù)荷分配費(fèi)用最低這兩個(gè)分配因素,提出采用多目標(biāo)NASG-II算法對(duì)鋼南、鋼北、鋼西站負(fù)荷進(jìn)行分配,得到Parteo前沿最優(yōu)解集并對(duì)該解集進(jìn)行深入分析,結(jié)果顯示負(fù)荷分配后該站變壓器的運(yùn)行損耗明顯減少。最后依據(jù)該站后五年負(fù)荷預(yù)測(cè)結(jié)果,為該企業(yè)制定一套切合實(shí)際的負(fù)荷分配方案。(3)當(dāng)前鋼鐵企業(yè)為了保證自身運(yùn)行的可靠性,企業(yè)中變電站長(zhǎng)期保持兩臺(tái)變壓器并列運(yùn)行狀態(tài)。然而鋼鐵企業(yè)負(fù)荷具有較大的波動(dòng)性,其運(yùn)行過(guò)程中難免出現(xiàn)非經(jīng)濟(jì)運(yùn)行的狀態(tài)。針對(duì)此問(wèn)題,本文通過(guò)分析變壓器經(jīng)濟(jì)運(yùn)行方式,得出變壓器在不同運(yùn)行方式下的節(jié)電效益,基于全局人工魚(yú)群算法確定變壓器最優(yōu)投切策略。考慮傳統(tǒng)全局人工魚(yú)群算法在尋優(yōu)過(guò)程中可行域是連續(xù)的,然而實(shí)際上本文所研究問(wèn)題的可行域是離散的,提出改進(jìn)的全局人工魚(yú)群算法用以實(shí)現(xiàn)在離散空間中確定變壓器最優(yōu)投切策略的目的。最后將該方法用于鋼東站變壓器運(yùn)行中,驗(yàn)證了所提方法的有效性。
[Abstract]:Iron and steel enterprises as the most typical power users in the power industry, its electricity consumption in the production costs account for a large proportion. With the economic downturn, the load of iron and steel enterprises shrinks severely, resulting in a sharp increase in the operation loss of distribution transformers. Aiming at the problem that transformers in Changzhi Iron and Steel Co., Ltd of Shougang are generally in non-economical running state, the paper constructs a load forecasting model combining with the actual load situation of this enterprise, and makes a load forecasting model for the south and west of Changgang Station. According to the load fluctuation, the economic operation control strategy of transformer is put forward in order to realize the optimal operation of transformer and reduce the operation loss of transformer in this enterprise. The main contents are as follows: (1) this paper summarizes and analyzes the existing load forecasting methods and compares the advantages and disadvantages of traditional least squares support vector machines and neural networks in load forecasting applications. The deficiency of least square support vector machine (LS-SVM) for load forecasting and the characteristics of the existing optimal LS-SVM model are analyzed. A new load forecasting model is constructed by combining the global artificial fish swarm algorithm with the least square support vector machine, which has high precision for two-dimensional spatial optimization, and the accuracy of the forecasting model is verified by the load of the enterprise's Changgang station. Finally, the load of Changzhi Iron and Steel Station in the next five years is forecasted, which also provides a new idea for the subsequent load distribution. (2) this paper reviews the current load situation of Changzhi Iron and Steel Enterprise in Shougang. The economic load coefficient and economic operation interval of transformer are analyzed. Taking into account the fact that the geographical factors of the load will result in new lines being erected during the distribution process, here through an examination of the load on each line, According to the latest selection conditions of overhead lines and towers of Shanxi State Grid, the types of overhead lines and the number of towers of each line are selected, and the cost is taken into account in the optimization process of load distribution. In order to satisfy the two distribution factors of transformer economic operation and lowest load distribution cost simultaneously, a multi-objective NASG-II algorithm is proposed to distribute the load of steel south, north and west stations. The optimal solution set of Parteo front is obtained and analyzed in depth. The results show that the operation loss of transformer in this station is obviously reduced after load distribution. Finally, according to the result of load forecasting in the later five years of the station, a suit of practical load distribution scheme is worked out for the enterprise. (3) in order to ensure the reliability of its own operation, the substation in the enterprise keeps two transformers running side by side for a long time. However, the load of iron and steel enterprises is fluctuating, and it is inevitable that non-economic operation occurs in the process of operation. In order to solve this problem, by analyzing the economic operation mode of transformer, this paper obtains the power saving benefit of transformer under different operation modes, and determines the optimal switching strategy of transformer based on global artificial fish swarm algorithm. Considering that the feasible region of traditional global artificial fish swarm algorithm is continuous in the process of optimization, however, in fact, the feasible region of the problem studied in this paper is discrete. An improved global artificial fish swarm algorithm is proposed to determine the optimal switching strategy of transformer in discrete space. Finally, the method is applied to the transformer operation of East Steel Station, and the validity of the proposed method is verified.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TM41

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