【摘要】：同步相量測(cè)量單元(phasor measurement unit,PMU)作為一種信息測(cè)量裝置,已經(jīng)廣泛應(yīng)用于電力系統(tǒng)運(yùn)行的各個(gè)環(huán)節(jié)當(dāng)中,當(dāng)電力系統(tǒng)處于機(jī)電暫態(tài)過(guò)程中時(shí),PMU能夠直接測(cè)量系統(tǒng)運(yùn)行狀態(tài)的相量信息。然而,由于這些信息是利用傳感器進(jìn)行測(cè)量的,且需要通過(guò)一定的方式進(jìn)行信息傳輸,所以最終使用的數(shù)據(jù)不可避免存在隨機(jī)誤差和壞數(shù)據(jù)。在電力系統(tǒng)安全監(jiān)控方面,為了得到更準(zhǔn)確的控制方案或者結(jié)果,在應(yīng)用前需要對(duì)實(shí)際量測(cè)數(shù)據(jù)進(jìn)行濾波處理。文中提出了基于粒子濾波(Particle filtering,PF)算法的電力系統(tǒng)機(jī)電暫態(tài)狀態(tài)估計(jì)方法,主要內(nèi)容包括以下幾個(gè)方面:首先,對(duì)粒子濾波算法進(jìn)行了深入研究,以基本PF算法為基礎(chǔ),提出了基于序貫重要性重采樣(sequential importance resampling,SIR)的PF算法,為了驗(yàn)證本文提出算法的優(yōu)越性,同時(shí)研究了傳統(tǒng)解決非線性狀態(tài)估計(jì)問(wèn)題的擴(kuò)展卡爾曼濾波算法(extended Kalman filter,EKF),從理論上對(duì)兩種算法進(jìn)行了對(duì)比研究分析。其次,將提出的基于SIR的粒子濾波算法應(yīng)用于電力系統(tǒng)實(shí)際狀態(tài)估計(jì)中。首先對(duì)發(fā)電機(jī)在機(jī)電暫態(tài)過(guò)程中的運(yùn)行狀態(tài)進(jìn)行了狀態(tài)估計(jì),建立了相應(yīng)的發(fā)電機(jī)四階狀態(tài)空間模型,包括系統(tǒng)方程和觀測(cè)方程;在發(fā)電機(jī)四階模型的基礎(chǔ)上,對(duì)暫態(tài)過(guò)程中狀態(tài)方程的噪聲誤差進(jìn)行了分析;為了能夠科學(xué)合理的定量評(píng)價(jià)估計(jì)的效果,提出了基于Copula理論的觀測(cè)路徑相關(guān)性評(píng)價(jià)指標(biāo);最后將提出的方法應(yīng)用于CEPRI7節(jié)點(diǎn)系統(tǒng)的機(jī)電暫態(tài)狀態(tài)估計(jì)當(dāng)中,從多個(gè)角度,定性定量的對(duì)估計(jì)結(jié)果進(jìn)行了評(píng)價(jià)。結(jié)果表明基于PF的估計(jì)結(jié)果與實(shí)際結(jié)果相關(guān)性較高、與真實(shí)值的均方根誤差小,估計(jì)效果優(yōu)于EKF的估計(jì)結(jié)果,有效減小了誤差數(shù)據(jù)的影響。最后,提出了對(duì)全系統(tǒng)進(jìn)行機(jī)電暫態(tài)狀態(tài)估計(jì)的方法。在進(jìn)行發(fā)電機(jī)暫態(tài)狀態(tài)估計(jì)的基礎(chǔ)上,提出了機(jī)網(wǎng)接口的直接解法,將對(duì)發(fā)電機(jī)節(jié)點(diǎn)的機(jī)電暫態(tài)狀態(tài)估計(jì)結(jié)果用全系統(tǒng)節(jié)點(diǎn)電壓相量誤差方差表示;建立了考慮發(fā)電機(jī)暫態(tài)過(guò)程狀態(tài)估計(jì)的全系統(tǒng)動(dòng)態(tài)狀態(tài)估計(jì)模型,通過(guò)引入發(fā)電機(jī)狀態(tài)估計(jì)約束提高對(duì)全系統(tǒng)暫態(tài)狀態(tài)估計(jì)的精度。通過(guò)對(duì)仿真算例的計(jì)算分析,可以得出本文提出的電力系統(tǒng)暫態(tài)過(guò)程全系統(tǒng)狀態(tài)估計(jì)方法,能夠有效的濾除實(shí)際PMU測(cè)量過(guò)程中可能出現(xiàn)的隨機(jī)誤差,獲得更加準(zhǔn)確的節(jié)點(diǎn)電壓相量值。
[Abstract]:As a kind of information measuring device, synchronous phasor measurement unit (phasor measurement unit,PMU) has been widely used in every link of power system operation, when the power system is in the electromechanical transient process, PMU can measure the phasor information of the running state of the system directly. However, because this information is measured by sensors and needs to be transmitted in a certain way, the end-used data will inevitably have random errors and bad data. In the aspect of power system security monitoring, in order to obtain more accurate control scheme or result, it is necessary to filter the actual measurement data before application. This paper presents a power system electromechanical transient state estimation method based on particle filter (Particle filtering,PF) algorithm. The main contents are as follows: firstly, the particle filter algorithm is deeply studied, which is based on the basic PF algorithm. In this paper, a PF algorithm based on sequential importance resampling (sequential importance resampling,SIR) is proposed. In order to verify the superiority of the proposed algorithm, the traditional extended Kalman filter (extended Kalman filter,EKF) algorithm, which is used to solve the nonlinear state estimation problem, is studied. In this paper, the comparison and analysis of the two algorithms are carried out theoretically. Secondly, the proposed particle filter algorithm based on SIR is applied to the actual state estimation of power system. Firstly, the operating state of the generator in the electromechanical transient process is estimated, and the fourth-order state space model of the generator is established, including the system equation and the observation equation. On the basis of the fourth-order model of generator, the noise error of state equation in transient process is analyzed, in order to evaluate the effect of quantitative estimation scientifically and reasonably, the correlation evaluation index of observation path based on Copula theory is put forward. Finally, the proposed method is applied to the electromechanical transient state estimation of the CEPRI7 node system, and the results are evaluated qualitatively and quantitatively from several angles. The results show that the estimation results based on PF have a high correlation with the actual results, and the root mean square error between the real values and the estimation results is small. The estimation effect is better than that of EKF, and the influence of the error data is reduced effectively. Finally, a method of electromechanical transient state estimation for the whole system is proposed. Based on the estimation of generator transient state, a direct solution of machine-network interface is proposed. The result of electromechanical transient state estimation of generator node is expressed by the variance of voltage phasor error of the whole system node. The whole system dynamic state estimation model considering generator transient process state estimation is established, and the accuracy of the whole system transient state estimation is improved by introducing the constraint of generator state estimation. Through the calculation and analysis of the simulation example, we can get the whole system state estimation method of power system transient process proposed in this paper, which can effectively filter out the random errors that may occur in the actual PMU measurement process. More accurate node voltage phasor values are obtained.
【學(xué)位授予單位】：東北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TM732

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