光伏并网电力系统的状态估计

发布时间：2024-05-27 19:05

　　光伏电网通过元件耦合并入传统电网导致网络节点数量增加,因此需要更多的量测设备来监测系统。然而,实际上不可能为整个电网都配备实时测量功率或光伏参数的设备。在满足冗余要求的前提下,状态估计可以利用现有的量测值来确定电力系统的状态,从而为光伏并网系统的状态估计提供了研究思路。为此,本文分析了用WLS和FDSE进行电网状态估计的方法,并对光伏并网系统的状态估计进行了研究。首先,分析了用于电网状态估计的WLS算法,在IEEE14节点系统中进行验证,算例分析表明该算法在理想的量测值条件下,计算结果是令人满意的,但是该算法无法解决量测中的大误差(噪声)和异常值问题。其次,分析了用于电网状态估计的FDSE算法,同样在IEEE14节点系统中进行验证,与WLS算法一样,FDSE算法不能有效地处理量测中的大误差和异常值问题。一般来说,WLS比FDSE具有更好的计算精度,但需要花费一定的计算时间。由于本文在小型电网中进行研究,节点数较少,对计算要求没有太大的限制,所以算法的选择仅基于准确性。因此,本文提出了一种用于光伏并网系统状态估计的WLS算法,并在IEEE30节点系统中进行验证。算例分析表明该算法的计算结...

【文章页数】：89 页

【学位级别】：硕士

【文章目录】：
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



本文编号：3982981