本文關(guān)鍵詞：分布式動(dòng)態(tài)狀態(tài)估計(jì)算法及其在電力系統(tǒng)中的應(yīng)用　出處：《山東大學(xué)》2017年博士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 動(dòng)態(tài)狀態(tài)估計(jì) 分布式狀態(tài)估計(jì) 最大后驗(yàn)估計(jì) 擴(kuò)展卡爾曼濾波 相量量測(cè)單元 混合量測(cè) 參數(shù)辨識(shí)

【摘要】：電力系統(tǒng)狀態(tài)估計(jì)是能量管理系統(tǒng)的重要組成部分,其估計(jì)精度和可靠性直接影響著電力系統(tǒng)的調(diào)度、安全性分析和執(zhí)行操作任務(wù)的準(zhǔn)確性.動(dòng)態(tài)狀態(tài)估計(jì)不僅可以對(duì)系統(tǒng)狀態(tài)進(jìn)行更準(zhǔn)確地估計(jì),而且擁有靜態(tài)狀態(tài)估計(jì)不具備的預(yù)測(cè)功能,能夠?yàn)殡娏ο到y(tǒng)的經(jīng)濟(jì)調(diào)度和預(yù)防控制等在線功能提供先驗(yàn)信息和更多的操作時(shí)間.所以動(dòng)態(tài)狀態(tài)估計(jì)具有非常重要的應(yīng)用價(jià)值,并得到了廣泛研究.隨著電力系統(tǒng)規(guī)模的不斷擴(kuò)大,電網(wǎng)互聯(lián)程度的日益加強(qiáng)以及精度更高、更新速度更快的量測(cè)裝置(PMU)的迅速發(fā)展和廣泛應(yīng)用,傳統(tǒng)的集中式狀態(tài)估計(jì)方法很難滿足準(zhǔn)確性和實(shí)時(shí)性的需求,從而促進(jìn)了電力系統(tǒng)分布式狀態(tài)估計(jì)算法的發(fā)展.本文通過(guò)將電力系統(tǒng)劃分為若干個(gè)不重疊的子系統(tǒng),研究了電力系統(tǒng)的分布式動(dòng)態(tài)狀態(tài)估計(jì)問(wèn)題,提出的算法適用于大規(guī)模電力系統(tǒng),具有重要的理論意義和應(yīng)用價(jià)值.主要貢獻(xiàn)和創(chuàng)新點(diǎn)如下:(1)通過(guò)將電力系統(tǒng)分區(qū)并基于SCADA和PMU混合量測(cè),提出了分布式動(dòng)態(tài)狀態(tài)估計(jì)算法,使得各子系統(tǒng)可以平行而且相對(duì)獨(dú)立地計(jì)算,加快了整體的計(jì)算速度.(2)各子系統(tǒng)不需要全局拓?fù)淇捎^測(cè)性信息,只需極少的量測(cè)數(shù)據(jù)和鄰居傳遞的信息即可估計(jì)本地狀態(tài),降低了分布式動(dòng)態(tài)狀態(tài)估計(jì)算法的計(jì)算復(fù)雜度,并且該復(fù)雜度與網(wǎng)絡(luò)的大小無(wú)關(guān).所提出的算法沒(méi)有中央調(diào)度中心,所以避免了集中式算法在數(shù)據(jù)傳輸過(guò)程中的瓶頸問(wèn)題,而且便于實(shí)施和管理.(3)所提算法的估計(jì)精度比集中式算法稍差,但是優(yōu)于分布式靜態(tài)狀態(tài)估計(jì)算法.當(dāng)系統(tǒng)出現(xiàn)異常情況時(shí),所提算法良好的估計(jì)性能和魯棒性以及參數(shù)辨識(shí)的有效性得到證實(shí).(4)通過(guò)減弱限制條件,證明了算法得到的估計(jì)和預(yù)測(cè)誤差協(xié)方差矩陣是正定并且有上界的,保證了算法的可行性.按照章節(jié)順序,具體的研究?jī)?nèi)容和研究成果包括以下幾個(gè)方面:1.研究了離散時(shí)間電力系統(tǒng)線性化模型的分布式動(dòng)態(tài)狀態(tài)估計(jì)問(wèn)題.基于最大后驗(yàn)估計(jì)技術(shù),提出了一種分布式狀態(tài)估計(jì)算法,其中量測(cè)數(shù)據(jù)由SCADA和PMU混合量測(cè)系統(tǒng)提供.首先將電力系統(tǒng)劃分為若干個(gè)不重疊的區(qū)域,對(duì)應(yīng)的子系統(tǒng)利用先驗(yàn)信息、本地和邊界量測(cè)以及鄰居子系統(tǒng)傳遞的信息對(duì)本地狀態(tài)進(jìn)行估計(jì),而不是估計(jì)整個(gè)系統(tǒng)的狀態(tài).與集中式方法相比,該分布式算法有效地降低了每個(gè)子系統(tǒng)狀態(tài)的維數(shù)和計(jì)算復(fù)雜度.其次,當(dāng)劃分后的子系統(tǒng)組成的網(wǎng)絡(luò)不含有環(huán)時(shí),證明了各子系統(tǒng)在每個(gè)時(shí)刻的本地狀態(tài)估計(jì)經(jīng)過(guò)有限次迭代收斂于集中式方法在修正目標(biāo)函數(shù)下的估計(jì)值.最后,仿真結(jié)果驗(yàn)證了所提算法對(duì)于大規(guī)模電力系統(tǒng)狀態(tài)估計(jì)的有效性和可行性.2.對(duì)于非線性電力系統(tǒng),基于擴(kuò)展卡爾曼濾波技術(shù),給出了一種分布式動(dòng)態(tài)狀態(tài)估計(jì)算法.當(dāng)子系統(tǒng)的本地量測(cè)無(wú)法得到時(shí),證明了所提出的算法仍然是可行的,同時(shí)利用邊界量測(cè)和鄰居子系統(tǒng)傳遞的信息,各子系統(tǒng)能夠得到理想的本地狀態(tài)估計(jì).通過(guò)對(duì)模型參數(shù)進(jìn)行在線辨識(shí),提高了狀態(tài)預(yù)測(cè)的精度,并加強(qiáng)了算法的魯棒性.當(dāng)電力系統(tǒng)出現(xiàn)負(fù)荷突變、存在不良數(shù)據(jù)和拓?fù)浣Y(jié)構(gòu)改變等異常情況時(shí),詳細(xì)的仿真結(jié)果驗(yàn)證了所提算法的魯棒性、較為準(zhǔn)確的估計(jì)值和參數(shù)辨識(shí)對(duì)于狀態(tài)估計(jì)的有效作用.與其它算法的性能比較結(jié)果顯示了所提算法在應(yīng)用中的優(yōu)勢(shì).3.進(jìn)一步研究了非線性動(dòng)態(tài)系統(tǒng)的分布式狀態(tài)估計(jì)問(wèn)題.通過(guò)減弱算法所需的限制條件,分析了算法的有界性.基于數(shù)學(xué)歸納法,證明了各子系統(tǒng)的本地狀態(tài)估計(jì)和預(yù)測(cè)的誤差協(xié)方差矩陣正定.根據(jù)時(shí)變系統(tǒng)的能觀測(cè)性秩判據(jù),證明了誤差協(xié)方差矩陣有上界,保證了算法的可行性.利用新的參數(shù)辨識(shí)方法,有效抑制了負(fù)荷突變對(duì)于估計(jì)精度產(chǎn)生的不良影響.
[Abstract]:Power system state estimation is an important part of energy management system, its accuracy and reliability directly affects the power system scheduling, security analysis and implementation tasks. The accuracy of dynamic state estimation can more accurately estimate the system state, but also have a static state estimation prediction function does not have the power to. The system of economic dispatch and the prevention and control of online function provides a priori information and more operation time. So the dynamic state estimation has very important application value, and has been extensively studied. With the expansion of power system, power grid interconnection degree increasing and higher precision and faster update speed measuring device (PMU) the rapid development and extensive application. The method is very difficult to satisfy the real-time and accuracy requirements of the traditional centralized state estimation, and from To promote the development of the algorithm of distributed state estimation of power system. The power system is divided into several non overlapping sub system, distributed dynamic state estimation problem of power system, the proposed algorithm is suitable for large-scale power system, has important theoretical significance and application value. The main contributions and innovations are as follows: (1) the power system partition and SCADA and PMU mixed measurement based, we propose a distributed dynamic state estimation algorithm is that each subsystem can be parallel and independent calculation, accelerate the overall computation speed. (2) the system does not require global topological observability information with minimal amount the measured data and neighbor message can be estimated local state, reduce the distributed dynamic state estimation algorithm computational complexity, and the complexity and independent of the size of the network is proposed. No central control center, so to avoid the bottleneck problem of centralized algorithm in data transmission process, and is easy to implement and management. (3) the estimation accuracy is slightly worse than the centralized algorithm, but better than the distributed static state estimation algorithm. When the system appears abnormal, the proposed algorithm good estimation performance and the robustness and parameter identification is proved to be effective. (4) by weakening the restrictions, proved that the estimation algorithm is obtained and the prediction error covariance matrix is positive and bounded, ensure the feasibility of the algorithm. According to the order of chapters, the specific research contents and results are as follows: distributed dynamic state 1. on the linear model of discrete time estimation of power system. Based on the maximum a posteriori estimation technique, proposes a distributed state estimation algorithm, the measurement data Provided by the SCADA and PMU mixed measurement system. Firstly, the power system is divided into several non overlapping regions, the corresponding subsystem using the prior information, and the local boundary value estimation of local state measuring subsystem and neighbor information, rather than estimating the state of the whole system. Compared with the centralized method, the distributed the algorithm effectively reduces the dimensionality and computational complexity of each subsystem state. Secondly, when the subsystems of the partitioned network does not contain a ring, that each subsystem of the local state at each time estimation after finite iterations converge to the centralized estimation method in the correction value of the objective function. Finally, simulation the results show that the proposed algorithm for large scale power system state estimation, the validity and feasibility of.2. for nonlinear power system, extended Calman filter technology based on a given A distributed dynamic state estimation algorithm. The local sub system can not be measured, it is shown that the proposed algorithm is feasible, at the same time measurement and information transmission subsystem neighbors using the boundary, the local state of each subsystem can obtain the ideal estimation. Through the online identification of the model parameters, improve the state of the prediction accuracy and enhance the robustness of the algorithm. When the power system load mutation and the existence of bad data and topology change and other abnormal situations, detailed simulation results verify that the proposed robust estimation algorithm, more accurate values of parameter identification and state estimation for effective performance with other algorithms. The comparison results show that the advantages of.3. algorithm in the application of the further study of the distributed state estimation problem of nonlinear dynamic system. The limits required by weakening algorithm And the analysis of the algorithm is bounded. Based on mathematical induction, proved positive definite covariance matrix of each subsystem of the local state estimation and prediction. The observability rank criterion based on time-varying system, proved that the error covariance matrix is bounded, ensure the feasibility of the algorithm. By using the method of parameter identification of the new. To restrain load mutation to the adverse impact of estimation accuracy.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TM711;TM732

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