【摘要】：電力系統(tǒng)狀態(tài)估計(jì)在現(xiàn)代能量管理系統(tǒng)(EMS)中扮演著至關(guān)重要的作用,是調(diào)度人員進(jìn)行正確決策的基礎(chǔ)。但量測數(shù)據(jù)常因量測裝置內(nèi)在誤差、傳輸噪聲等原因受到污染,干擾狀態(tài)估計(jì)結(jié)果,誤導(dǎo)調(diào)度人員。因此,提高狀態(tài)估計(jì)算法的魯棒性以及抑制不良數(shù)據(jù)的能力,對保證電力系統(tǒng)穩(wěn)定運(yùn)行有重要意義。隨著量測裝置技術(shù)的發(fā)展,同步相量量測裝置(PMU)在電力系統(tǒng)中推廣應(yīng)用,為狀態(tài)估計(jì)提供高精度、高同步的量測。同時(shí),PMU量測與SCADA量測來源不同,相互獨(dú)立,從而互為備用,可以有效抑制SCADA量測中的不良數(shù)據(jù),進(jìn)一步提高算法的魯棒性。因此,本文主要是提出一種魯棒性更好的預(yù)測輔助狀態(tài)估計(jì)算法,并探究PMU量測對該算法估計(jì)精度和魯棒性的影響。本文主要內(nèi)容如下:1.簡單介紹了幾種應(yīng)用在狀態(tài)估計(jì)中的算法,包括加權(quán)最小二乘法、卡爾曼濾波和擴(kuò)展卡爾曼濾波。2.基于SCADA量測,在擴(kuò)展卡爾曼濾波(EKF)的基礎(chǔ)上改進(jìn),提出了廣義最大似然類型一擴(kuò)展卡爾曼濾波算法(GM-EKF)�；舅悸�:首先,利用EKF的狀態(tài)方程與量測方程構(gòu)建線性回歸框架。然后,利用投影統(tǒng)計(jì)算法(PS)辨識(shí)異常值并構(gòu)建等價(jià)權(quán)函數(shù)。接著,評(píng)價(jià)函數(shù)選擇Huber函數(shù),構(gòu)建類似WLS形式的目標(biāo)函數(shù)并利用IRLS求解。為驗(yàn)證算法的有效性和魯棒性,將GM-EKF算法在IEEE標(biāo)準(zhǔn)測試系統(tǒng)中仿真,并與相關(guān)算法進(jìn)行結(jié)果比較。3.基于SCADA/PMU混合量測,探究PMU量測對于GM-EKF算法估計(jì)精度和魯棒性的影響。基本思路:針對PMU量測與SCADA量測不同融合方式,一種是狀態(tài)變量為極坐標(biāo),直接添加PMU量測,形成非線性的魯棒預(yù)測輔助狀態(tài)估計(jì)算法。另一種是首先處理收集到的SCADA量測,將處理的狀態(tài)估計(jì)值與PMU量測作為新的量測,在直角坐標(biāo)系下,形成線性的魯棒預(yù)測輔助狀態(tài)估計(jì)算法。將算法在IEEE標(biāo)準(zhǔn)測試系統(tǒng)中仿真,分析仿真結(jié)果。
[Abstract]:Power system state estimation plays an important role in modern energy management system (EMS) and is the basis for dispatcher to make correct decision. However, the measurement data are often contaminated by the inherent errors of the measuring device and transmission noise, and the estimation results of the interference state are misled by the dispatcher. Therefore, it is important to improve the robustness of the state estimation algorithm and the ability to suppress bad data to ensure the stable operation of power system. With the development of measuring device technology, synchronous phasor device (PMU) is widely used in power system, which provides high precision and high synchronization measurement for state estimation. At the same time, the sources of PMU measurement and SCADA measurement are different and independent, which can effectively suppress the bad data in SCADA measurement and further improve the robustness of the algorithm. Therefore, this paper mainly proposes a more robust predictor-aided state estimation algorithm, and explores the effect of PMU measurements on the estimation accuracy and robustness of the algorithm. The main contents of this paper are as follows: 1. This paper briefly introduces several algorithms used in state estimation, including weighted least square method, Kalman filter and extended Kalman filter. Based on SCADA measurement and the improvement of extended Kalman filter (EKF), an extended Kalman filter algorithm (GM-EKF), a generalized maximum likelihood type, is proposed. Basic ideas: firstly, the linear regression framework is constructed by using EKF's equation of state and measurement equation. Then, the outliers are identified by projection statistic algorithm (PS) and the equivalent weight function is constructed. Then, the evaluation function selects the Huber function, constructs the objective function similar to WLS and solves it by IRLS. In order to verify the effectiveness and robustness of the algorithm, the GM-EKF algorithm is simulated in the IEEE standard test system, and the results are compared with the related algorithms. 3. Based on SCADA/PMU mixed measurement, the effect of PMU measurement on estimation accuracy and robustness of GM-EKF algorithm is investigated. The basic idea: according to the different fusion methods of PMU measurement and SCADA measurement, one is that the state variable is polar coordinate and PMU measurement is added directly to form a nonlinear robust predictive auxiliary state estimation algorithm. The other is to process the collected SCADA measurements first and take the state estimators and PMU measurements as new measurements to form a linear robust predictive auxiliary state estimation algorithm in rectangular coordinates. The algorithm is simulated in IEEE standard test system and the simulation results are analyzed.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM73

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 朱杰;張葛祥;王濤;趙俊博;;電力系統(tǒng)狀態(tài)估計(jì)欺詐性數(shù)據(jù)攻擊及防御綜述[J];電網(wǎng)技術(shù);2016年08期

2 朱杰;張葛祥;;基于歷史數(shù)據(jù)庫的電力系統(tǒng)狀態(tài)估計(jì)欺詐性數(shù)據(jù)防御[J];電網(wǎng)技術(shù);2016年06期

3 丁宏恩;高宗和;蘇大威;趙家慶;常乃超;龔成明;;混合量測狀態(tài)估計(jì)相角參考點(diǎn)壞數(shù)據(jù)問題的處理方法[J];電力系統(tǒng)自動(dòng)化;2014年09期

4 趙俊博;張葛祥;黃彥全;;含新能源電力系統(tǒng)狀態(tài)估計(jì)研究現(xiàn)狀和展望[J];電力自動(dòng)化設(shè)備;2014年05期

5 林桂華;安天瑜;周蘇荃;張艷軍;孫明一;;計(jì)及PMU量測信息的量測量變換狀態(tài)估計(jì)[J];電網(wǎng)技術(shù);2009年17期

6 程濤;黃彥全;申鐵;;遺傳算法在PMU優(yōu)化配置中的應(yīng)用[J];電力系統(tǒng)及其自動(dòng)化學(xué)報(bào);2009年01期

7 盧志剛;張宗偉;;基于量測量替換與標(biāo)準(zhǔn)化殘差檢測的不良數(shù)據(jù)辨識(shí)[J];電力系統(tǒng)自動(dòng)化;2007年13期

8 衛(wèi)志農(nóng);李陽林;鄭玉平;;基于混合量測的電力系統(tǒng)線性動(dòng)態(tài)狀態(tài)估計(jì)算法[J];電力系統(tǒng)自動(dòng)化;2007年06期

9 秦曉輝;畢天姝;楊奇遜;;計(jì)及PMU的混合非線性狀態(tài)估計(jì)新方法[J];電力系統(tǒng)自動(dòng)化;2007年04期

10 黃彥全;肖建;李云飛;邵明;黃慶;;基于量測數(shù)據(jù)相關(guān)性的電力系統(tǒng)不良數(shù)據(jù)檢測和辨識(shí)新方法[J];電網(wǎng)技術(shù);2006年02期

相關(guān)博士學(xué)位論文 前1條

1 王英濤;基于WAMS的電力系統(tǒng)動(dòng)態(tài)監(jiān)測及分析研究[D];中國電力科學(xué)研究院;2006年

相關(guān)碩士學(xué)位論文 前5條

1 楊霽;基于狀態(tài)預(yù)測的電力系統(tǒng)狀態(tài)估計(jì)及不良數(shù)據(jù)檢測與辨識(shí)[D];西南交通大學(xué);2015年

2 李波;混合量測下電力系統(tǒng)動(dòng)態(tài)狀態(tài)估計(jì)研究[D];上海交通大學(xué);2013年

3 林桂華;基于PMU的電力系統(tǒng)狀態(tài)估計(jì)研究[D];哈爾濱工業(yè)大學(xué);2009年

4 李陽林;基于WAMS/SCADA混合量測的電力系統(tǒng)動(dòng)態(tài)狀態(tài)估計(jì)算法研究[D];河海大學(xué);2007年

5 孫國強(qiáng);基于相量測量的電力系統(tǒng)狀態(tài)估計(jì)研究[D];河海大學(xué);2005年

，

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