本文選題：狀態(tài)估計 + 量測裝置配置��； 參考：《華北電力大學(北京)》2017年碩士論文

【摘要】：狀態(tài)估計作為電力系統(tǒng)潮流計算、短路電流計算和穩(wěn)定性分析的基礎,是對從現場獲取來的數據的第一次處理。隨著現代科技的發(fā)展,現場采集數據的設備種類越來越多,設備精度越來越高,同時,配電網相對于輸電網結構更加復雜,大量的分布式電源、環(huán)網甚至微網,出現在配電網中。因此,本文在新型量測設備PMU和傳統(tǒng)量測設備在含環(huán)配電網中的混合配置方案上進行了深入研究。含環(huán)配電網中的量測裝置優(yōu)化配置,實際上就是用花費較少的量測系統(tǒng)來獲得較高精度的狀態(tài)估計結果,同時必須保證系統(tǒng)的可觀性。本文以新興的和聲搜索算法為框架,以PMU和傳統(tǒng)量測裝置的成本較低和量測精度較高為目標,得到多目標pareto解集前沿,用獲取得到的量測進行狀態(tài)估計,以狀態(tài)估計的估計精度來衡量目標函數中的量測精度。文章在仿真過程中,采用的是IEEE33關閉聯絡開關來實現含環(huán)配電網,并在關閉9節(jié)點和15節(jié)點之間、8節(jié)點和21節(jié)點之間的聯絡開關的系統(tǒng)上進行了仿真實驗,得到了理想的配置方案。狀態(tài)估計有著成熟的算法框架,在靜態(tài)狀態(tài)估計中使用較多的是最小二乘狀態(tài)估計,但是由于量測量和狀態(tài)量之間是非線性關系,最小二乘狀態(tài)估計需要借助高斯牛頓反復迭代,計算成本較高。隨著神經網絡這種專門解決非線性問題的技術的出現,使用神經網絡訓練一個網絡用于計算狀態(tài)估計已經成為一種可能。本文就是利用稀疏自編碼器(SAE)和前饋(BP)神經網絡結合,同時使用粒子群算法(PSO)調整網絡參數,最終在IEEE14的基礎上訓練得到一個計算狀態(tài)估計的網絡,和傳統(tǒng)的最小二乘狀態(tài)估計相比,本文使用提出的神經網絡訓練得到的網絡計算狀態(tài)估計不僅可以節(jié)省每一次的計算時間,還可以得到更高的狀態(tài)估計精度。
[Abstract]:As the basis of power flow calculation, short-circuit current calculation and stability analysis, state estimation is the first processing of the data obtained from the field. With the development of modern science and technology, there are more and more kinds of equipment to collect data on the spot, and the precision of the equipment is more and more high. At the same time, the distribution network is more complicated than the transmission network structure, and a large number of distributed generation, ring network and even microgrid, Appear in the distribution network. Therefore, the hybrid configuration scheme of new measuring equipment PMU and traditional measuring equipment in distribution network with ring is studied in this paper. In fact, the optimal configuration of measuring devices in the distribution network with ring is to obtain a high precision state estimation result with a less cost measurement system, and at the same time, the observability of the system must be guaranteed. In this paper, the new harmonic search algorithm is used as the framework, the cost of PMU and the traditional measuring device is low and the measurement precision is high, the frontier of multi-objective pareto solution set is obtained, and the obtained measurements are used to estimate the state. The measurement accuracy in the objective function is measured by the estimation accuracy of state estimation. In the course of simulation, the IEEE33 switch is used to realize the distribution network with ring, and the simulation experiment is carried out on the system of closing the connection switch between 9 and 15 nodes, 8 nodes and 21 nodes. An ideal configuration scheme is obtained. State estimation has a mature algorithm framework. The least square state estimation is used more frequently in static state estimation, but because of the nonlinear relationship between the measurement and the state quantity, The least square state estimation needs to be iterated repeatedly by means of Gao Si Newton, and the calculation cost is high. With the emergence of neural network, which is a special technique to solve nonlinear problems, it is possible to use neural network to train a network to calculate state estimation. In this paper, we combine sparse self-encoder (SAE) with feedforward (BP) neural network, and use particle swarm optimization algorithm (PSO) to adjust the network parameters. Finally, we train a computational state estimation network on the basis of IEEE14. Compared with the traditional least square state estimation, the proposed neural network training can not only save the time of each calculation, but also obtain higher state estimation accuracy.
【學位授予單位】：華北電力大學(北京)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM930

【參考文獻】

相關期刊論文 前9條

1 張啟飛;蘇小林;閻曉霞;;基于暫態(tài)可觀性的向量測量單元配置方法[J];山西電力;2015年01期

2 袁淑瑛;王中杰;;N-1故障時基于差分進化算法的PMU優(yōu)化配置[J];系統(tǒng)仿真技術;2015年01期

3 徐巖;郅靜;;基于改進自適應遺傳算法的PMU優(yōu)化配置[J];電力系統(tǒng)保護與控制;2015年02期

4 王巖;陳文浩;陳筱韻;蔡潤慶;;基于和聲搜索算法的配電網多目標無功優(yōu)化[J];南方電網技術;2014年05期

5 朱彩蘭;;一類離散時間神經網絡模型的狀態(tài)估計[J];遼寧工業(yè)大學學報(自然科學版);2014年05期

6 盛四清;范林濤;李興;檀曉林;;基于帕累托最優(yōu)的配電網多目標規(guī)劃[J];電力系統(tǒng)自動化;2014年15期

7 張作鵬;陳剛;白茂金;;基于動態(tài)規(guī)劃算法的PMU優(yōu)化配置[J];電網技術;2007年S1期

8 李清政;鐘建偉;;基于神經網絡法的配電網狀態(tài)估計[J];河北建筑科技學院學報;2006年03期

9 李光熹，，熊曼麗;神經網絡法電力系統(tǒng)狀態(tài)估計[J];武漢水利電力大學學報;1995年01期

相關博士學位論文 前1條

1 李強;基于PMU量測的電力系統(tǒng)狀態(tài)估計研究[D];中國電力科學研究院;2006年

相關碩士學位論文 前1條

1 汪隆臻;基于PMU的狀態(tài)估計研究[D];華北電力大學（北京）;2008年

本文編號：1903473