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

【摘要】：狀態(tài)估計(jì)作為電力系統(tǒng)潮流計(jì)算、短路電流計(jì)算和穩(wěn)定性分析的基礎(chǔ),是對(duì)從現(xiàn)場(chǎng)獲取來的數(shù)據(jù)的第一次處理。隨著現(xiàn)代科技的發(fā)展,現(xiàn)場(chǎng)采集數(shù)據(jù)的設(shè)備種類越來越多,設(shè)備精度越來越高,同時(shí),配電網(wǎng)相對(duì)于輸電網(wǎng)結(jié)構(gòu)更加復(fù)雜,大量的分布式電源、環(huán)網(wǎng)甚至微網(wǎng),出現(xiàn)在配電網(wǎng)中。因此,本文在新型量測(cè)設(shè)備PMU和傳統(tǒng)量測(cè)設(shè)備在含環(huán)配電網(wǎng)中的混合配置方案上進(jìn)行了深入研究。含環(huán)配電網(wǎng)中的量測(cè)裝置優(yōu)化配置,實(shí)際上就是用花費(fèi)較少的量測(cè)系統(tǒng)來獲得較高精度的狀態(tài)估計(jì)結(jié)果,同時(shí)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(cè)裝置的成本較低和量測(cè)精度較高為目標(biāo),得到多目標(biāo)pareto解集前沿,用獲取得到的量測(cè)進(jìn)行狀態(tài)估計(jì),以狀態(tài)估計(jì)的估計(jì)精度來衡量目標(biāo)函數(shù)中的量測(cè)精度。文章在仿真過程中,采用的是IEEE33關(guān)閉聯(lián)絡(luò)開關(guān)來實(shí)現(xiàn)含環(huán)配電網(wǎng),并在關(guān)閉9節(jié)點(diǎn)和15節(jié)點(diǎn)之間、8節(jié)點(diǎn)和21節(jié)點(diǎn)之間的聯(lián)絡(luò)開關(guān)的系統(tǒng)上進(jìn)行了仿真實(shí)驗(yàn),得到了理想的配置方案。狀態(tài)估計(jì)有著成熟的算法框架,在靜態(tài)狀態(tài)估計(jì)中使用較多的是最小二乘狀態(tài)估計(jì),但是由于量測(cè)量和狀態(tài)量之間是非線性關(guān)系,最小二乘狀態(tài)估計(jì)需要借助高斯牛頓反復(fù)迭代,計(jì)算成本較高。隨著神經(jīng)網(wǎng)絡(luò)這種專門解決非線性問題的技術(shù)的出現(xiàn),使用神經(jīng)網(wǎng)絡(luò)訓(xùn)練一個(gè)網(wǎng)絡(luò)用于計(jì)算狀態(tài)估計(jì)已經(jīng)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(jīng)網(wǎng)絡(luò)結(jié)合,同時(shí)使用粒子群算法(PSO)調(diào)整網(wǎng)絡(luò)參數(shù),最終在IEEE14的基礎(chǔ)上訓(xùn)練得到一個(gè)計(jì)算狀態(tài)估計(jì)的網(wǎng)絡(luò),和傳統(tǒng)的最小二乘狀態(tài)估計(jì)相比,本文使用提出的神經(jīng)網(wǎng)絡(luò)訓(xùn)練得到的網(wǎng)絡(luò)計(jì)算狀態(tài)估計(jì)不僅可以節(jié)省每一次的計(jì)算時(shí)間,還可以得到更高的狀態(tài)估計(jì)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(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM930

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