光伏電網(wǎng)通過元件耦合并入傳統(tǒng)電網(wǎng)導(dǎo)致網(wǎng)絡(luò)節(jié)點數(shù)量增加,因此需要更多的量測設(shè)備來監(jiān)測系統(tǒng)。然而,實際上不可能為整個電網(wǎng)都配備實時測量功率或光伏參數(shù)的設(shè)備。在滿足冗余要求的前提下,狀態(tài)估計可以利用現(xiàn)有的量測值來確定電力系統(tǒng)的狀態(tài),從而為光伏并網(wǎng)系統(tǒng)的狀態(tài)估計提供了研究思路。為此,本文分析了用WLS和FDSE進行電網(wǎng)狀態(tài)估計的方法,并對光伏并網(wǎng)系統(tǒng)的狀態(tài)估計進行了研究。首先,分析了用于電網(wǎng)狀態(tài)估計的WLS算法,在IEEE14節(jié)點系統(tǒng)中進行驗證,算例分析表明該算法在理想的量測值條件下,計算結(jié)果是令人滿意的,但是該算法無法解決量測中的大誤差(噪聲)和異常值問題。其次,分析了用于電網(wǎng)狀態(tài)估計的FDSE算法,同樣在IEEE14節(jié)點系統(tǒng)中進行驗證,與WLS算法一樣,FDSE算法不能有效地處理量測中的大誤差和異常值問題。一般來說,WLS比FDSE具有更好的計算精度,但需要花費一定的計算時間。由于本文在小型電網(wǎng)中進行研究,節(jié)點數(shù)較少,對計算要求沒有太大的限制,所以算法的選擇僅基于準確性。因此,本文提出了一種用于光伏并網(wǎng)系統(tǒng)狀態(tài)估計的WLS算法,并在IEEE30節(jié)點系統(tǒng)中進行驗證。算例分析表明該算法的計算結(jié)...

【文章頁數(shù)】：89 頁

【學(xué)位級別】：碩士

【文章目錄】：
摘要
Abstract
Chapter 1 : Introduction
    1.1 Background
    1.2 Motivation and Research questions
    1.3 Research objectives
    1.4 Thesis outline
Chapter 2: State estimation of power networks using the Weighted Least Squares method
    2.1 Introduction
        2.1.1 Background
        2.1.2 Literature review
    2.2 The maximum likelihood estimation method
    2.3 Measurement model and assumptions
    2.4 Weighted least squares state estimation
        2.4.1 Measurement function
        2.4.2 Measurement Jacobian
    2.5 Observability Analysis
    2.6 Bad Data Detection and Identification
    2.7 State Estimation Accuracy
    2.8 Algorithm of the simulation model developed in MATLAB
    2.9 Test results
        2.9.1 SE with perfect measurements
        2.9.2 SE for measurements having Gaussian noise
        2.9.3 Bad Data Analysis
    2.10 Summary
Chapter 3: Fast Decoupled State Estimation method of power networks
    3.1 Introduction
        3.1.1 Background
        3.1.2 Literature review
    3.2 Fast Decoupled State Estimation Model
        3.2.1 Measurement function
        3.2.2 Measurement Jacobian
        3.2.3 Gain Matrix
    3.3 Bad Data Detection and Identification
    3.4 Algorithm of the simulation model developed in MATLAB
    3.5 Test results
        3.5.1 SE with perfect measurements
        3.5.2 SE for measurements having Gaussian noise
        3.5.3 Bad Data Analysis
    3.6 Summary
Chapter 4: State Estimation of Photovoltaic Grid-Integrated Power System
    4.1 Introduction
        4.1.1 Background
        4.1.2 Literature review
    4.2 Extended State Estimation Algorithm
        4.2.1 Steady-State Model of Grid-Connected Photovoltaic Generation System
        4.2.2 Power Flow Analysis of a Grid-Integrated Photovoltaic System
    4.3 Weighted Least Squares Algorithm for Integrated Power System
        4.3.1 Measurement function
        4.3.2 Measurement Jacobian
    4.4 Bad Data Detection and Identification
    4.5 Implementation of the Algorithm in MATLAB
    4.6 Case Study Description
    4.7 Test Results
        4.7.1 Measurements of the grid without PV
        4.7.2 Measurements of the grid with PV
        4.7.3 Bad Data Analysis
    4.8 Summary
Chapter 5: Conclusions and Future work
    5.1 Introduction
    5.2 Conclusions
        5.2.1 State estimation of power networks using weighted least square method
        5.2.2 State estimation of power networks using Fast Decoupled State Estimation method
        5.2.3 State estimation of photovoltaic-grid integrated power system
    5.3 Future Work
Reference
Acknowledgement
Appendix
    A: IEEE 14 Bus System Parameters
        Table A.1: Line Data
        Table A.2: Bus Data
        Table A.3: Transformer Tap-Setting Data
        Table A.4: Shunt Capacitor Data
    B: IEEE 30 Bus System Parameters
        Table B.1: Line Data
        Table B.2: Bus Data
        Table B.3: Transformer Tap-Setting Data
        Table B.4: Shunt Capacitor Data
    C: Model Parameters of the PV power station sample under STC
        Table C.1: Model Parameters of PV Arrays
        Table C.2: Model Parameters of AC part
        Table C.3: Operating Parameters of PCC and PV Generation System



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