本文選題：狀態(tài)估計(jì) + 無跡卡爾曼濾波　； 參考：《華北電力大學(xué)》2014年碩士論文

【摘要】：電力系統(tǒng)狀態(tài)估計(jì)是電力系統(tǒng)狀態(tài)監(jiān)測與控制的核心。狀態(tài)估計(jì)的目的在于根據(jù)測量和網(wǎng)絡(luò)模型獲得電力系統(tǒng)準(zhǔn)確的實(shí)時(shí)運(yùn)行狀態(tài)，以便進(jìn)行電力系統(tǒng)分析、預(yù)測或控制等，提高系統(tǒng)的安全與經(jīng)濟(jì)運(yùn)行水平。目前實(shí)際電力系統(tǒng)中常用的方法是基于最小二乘法的靜態(tài)狀態(tài)估計(jì)，靜態(tài)狀態(tài)估計(jì)的缺陷是根據(jù)某一時(shí)間斷面獲得的系統(tǒng)狀態(tài)，表征的是系統(tǒng)的穩(wěn)態(tài)運(yùn)行狀況�；诳柭鼮V波原理的動(dòng)態(tài)狀態(tài)估計(jì)方法是為了進(jìn)一步提高實(shí)時(shí)性而提出的，，具有重要的研究價(jià)值。 本文介紹了卡爾曼濾波的基本原理及幾種基于卡爾曼濾波原理的濾波方法，其中無跡卡爾曼濾波作為一種非線性濾波方法，并沒有對非線性系統(tǒng)進(jìn)行線性化，而是通過無跡變換這一方法對非線性系統(tǒng)的均值和方差信息進(jìn)行傳遞，該方法與擴(kuò)展卡爾曼濾波方法相比具有更好的數(shù)值穩(wěn)定性。本文基于無跡卡爾曼濾波方法，結(jié)合相量測量單元在電力系統(tǒng)中的應(yīng)用，對電力系統(tǒng)動(dòng)態(tài)狀態(tài)估計(jì)計(jì)算方法進(jìn)行研究。系統(tǒng)模型中預(yù)測模型不可能完全精確，參數(shù)估計(jì)會(huì)有誤差，系統(tǒng)噪聲的統(tǒng)計(jì)特性亦不可知，因此引入漸消記憶指數(shù)加權(quán)的噪聲統(tǒng)計(jì)估值器，既可以估計(jì)時(shí)變系統(tǒng)噪聲，亦可將模型誤差歸入噪聲中進(jìn)行估計(jì)。引入噪聲統(tǒng)計(jì)估值器后，即得到了自適應(yīng)無跡卡爾曼濾波方法。 對于擴(kuò)展卡爾曼濾波、無跡卡爾曼濾波、Cubature卡爾曼濾波和自適應(yīng)無跡卡爾曼濾波方法，均在MATLAB中編程實(shí)現(xiàn)，并結(jié)合IEEE14、IEEE30、IEEE57和IEEE118測試系統(tǒng)，對各種方法動(dòng)態(tài)狀態(tài)估計(jì)性能進(jìn)行比較，自適應(yīng)無跡卡爾曼濾波取得了較好的估計(jì)效果，與原有的算法進(jìn)行比較，驗(yàn)證了算法的有效性。
[Abstract]:Power system state estimation is the core of power system state monitoring and control. The purpose of state estimation is to obtain accurate real-time operation state of power system according to measurement and network model, so as to analyze, predict or control power system, and improve the security and economic operation level of power system. At present, the commonly used method in power system is static state estimation based on least square method. The defect of static state estimation is the system state obtained according to a certain time section, which represents the steady state of the system. The dynamic state estimation method based on Kalman filtering principle is proposed to further improve the real-time performance, which has important research value. This paper introduces the basic principle of Kalman filter and several filtering methods based on Kalman filter principle. As a nonlinear filtering method, unscented Kalman filter does not linearize the nonlinear system. The unscented transformation is used to transmit the mean and variance information of nonlinear systems. The method has better numerical stability than the extended Kalman filtering method. Based on the unscented Kalman filtering method and the application of phasor measurement unit in power system, the calculation method of power system dynamic state estimation is studied in this paper. The prediction model in the system model cannot be completely accurate, the parameter estimation will have errors, and the statistical characteristics of the system noise will not be known. Therefore, a noise statistical estimator weighted by fading memory exponents can be used to estimate the time-varying system noise. The model error can also be classified into noise for estimation. The adaptive unscented Kalman filter is obtained by introducing the noise statistical estimator. For extended Kalman filter, unscented Kalman filter cuboid Kalman filter and adaptive unscented Kalman filter, they are all programmed in MATLAB. The dynamic state estimation performance of various methods is compared with IEEE 14, IEEE30, IEEE57 and IEEE118 test system. The adaptive unscented Kalman filter has achieved a good estimation effect, and compared with the original algorithm, the validity of the algorithm is verified.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM732;TN713

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前3條

1 羅仁義;計(jì)及節(jié)點(diǎn)時(shí)空關(guān)聯(lián)性的電力系統(tǒng)預(yù)測輔助狀態(tài)估計(jì)[D];西南交通大學(xué);2017年

2 呂思穎;基于卡爾曼濾波的輸電線路繼電保護(hù)算法研究[D];廣西大學(xué);2016年

3 郭忠明;控壓鉆井井筒壓力控制參數(shù)設(shè)計(jì)及實(shí)時(shí)計(jì)算研究[D];西南石油大學(xué);2016年

本文編號(hào)：1990837