基于WAMS/SCADA混合量测的电力系统状态估计研究
摘要
电力系统状态估计能够提供电网当前的运行状态,是保证电力系统正常运行的基础。传统的基于监控和数据采集系统(SCADA)获取量测的状态估计还存在精度低且同步性差的缺陷,广域量测系统(WAMS)的出现为改善传统状态估计带来了新的机遇和挑战。从技术和经济等因素考虑,WAMS和SCADA必然在当前、甚至相当长一段时间内并存,因此,可以应用二者相结合的数据来改进传统状态估计存在的问题。本论文针对WAMS/SCADA混合量测状态估计中的某些重要问题,如静态状态估计算法、数据兼容性、错误参数辨识与修正、WAMS量测配置、动态状态估计算法等进行了深入研究,主要工作如下：
提出了两种应用WAMS/SCADA混合量测以提高静态状态估计精度的算法。一种是将WAMS量测约束条件与网络中固有的零注入节点约束条件引入状态估计方程,优化求解双重约束条件下的最佳估计值。另一种是在保留原有SCADA量测的同时,通过虚拟量测对PMU观测范围进行拓展,提高量测冗余度。然后,分析和比较了两种算法在不同工作条件下的适应性。
建立了关于WAMS/SCADA混合量测状态估计数据兼容性问题的数学模型,分析了影响混合量测数据兼容的主要因素。通过对WAMS和SCADA量测的相关度分析,实现了WAMS和SCADA数据的同步,考虑同步性引起的误差重新量化了量测权值,达到使混合量测数据在状态估计中兼容的目的。
提出了一种以WAMS/SCADA混合量测为基础的状态估计错误参数辨识和修正的方法。将WAMS量测对应的功率残差和零注入节点功率残差,与SCADA量测残差相结合,通过优化理论构造参数误差与各残差的灵敏度关系矩阵,进而实现状态估计中错误参数的辨识和修正。
提出了一种WAMS/SCADA混合量测中的PMU配置方案,根据电气距离建立了网络中各个节点彼此的耦合关系,应用模糊c-均值聚类方法对节点进行分群,辨识具有相似动态特性的节点,通过在各节点群中配置PMU,达到捕捉系统典型动态信息的目的。
最后,提出了一种基于WAMS/SCADA混合量测的电力系统动态状态估计算法。通过在SCADA量测的基础上引入WAMS量测,提高动态状态估计的精度；同时运用无迹卡尔曼滤波方法,避免了线性化误差的引入和雅克比矩阵的计算,相比传统扩展卡尔曼滤波方法能够更准确地估计动态系统中的状态变量。
State estimation provides the real-time operational state of power system, and it's a prerequisite to ensure the normal operation of the system. The conventional state estimation which based on SCADA has the disadvantages of low accuracy and poor synchronization, and the emergence and development of WAMS brings new opportunities for improving the performance of state estimation. From the economy and technique views to consider, WAMS and SCADA exists at the same time is the inexorable trend. However, WAMS and SCADA based on different technical platforms, so how to combine the WAMS data with the SCADA data is a difficult problem. Aiming at some basic questions of WAMS/SCADA hybrid state estimation, this paper makes in-depth and careful studies on static state estimation algorithm, dynamic state estimation algorithm, optimal PMU placement, compatibility problem, identification and correction of the parameters errors. The main achievements of this paper are as follows:
This paper proposes two kinds of static state estimation algorithms which based on WAMS/SCADA hybrid data. The first algorithm constructs a state estimation objective function which combined with the constraints of WAMS and zero-injection nodes. The second algorithm expands the observation range of PMU by introducing pseudo measurements. Then, analyzing and comparing the adaptability of two algorithms under different working conditions.
This paper establishs mathematical model about the compatible problem of WAMS/SCADA hybrid data, and analyzes the main factors which effect data compatibility. This solution implements the synchronization of WAMS and SCADA data through the correlation analysis, and then re-quantified the measure weights by considering the delay distribution of measurement data, to make hybrid data have compatibility.
On the basis of WAMS/SCADA hybrid measurements, a new method to identify and correct the parameters errors is proposed in this paper. Combining the residuals power and zero-injection node power flows acquired by WAMS measurements with the residuals of SCADA measurements, using the optimization theory to construct the sensibility matrix with respect to the residuals and parameters errors. And then, the identification and correction of parameters errors can be achieved.
This paper presents a new method for an optimal placement of PMU in hybrid measurement system. This method establishs the coupling relationship of network nodes based on electrical distance, and uses the Fuzzy c-means clustering technique to group the network nodes, a class of nodes which have similar dynamic characteristics can be replaced by grouping node. Then, PMU which placed in the grouping node can capture the typical dynamic information.
The final study of this paper is about dynamic state estimation algorithm which based on WAMS/SCADA hybrid measurements. This algorithm introduces the WAMS data to improve the accuracy of state estimation, and uses Unscented Kalman Filter (UKF) to avoid the error caused by the linearization process and the complicated calculation of Jacobian matrix, so it can estimate the state variables of dynamic system more accurately.
提出了两种应用WAMS/SCADA混合量测以提高静态状态估计精度的算法。一种是将WAMS量测约束条件与网络中固有的零注入节点约束条件引入状态估计方程,优化求解双重约束条件下的最佳估计值。另一种是在保留原有SCADA量测的同时,通过虚拟量测对PMU观测范围进行拓展,提高量测冗余度。然后,分析和比较了两种算法在不同工作条件下的适应性。
建立了关于WAMS/SCADA混合量测状态估计数据兼容性问题的数学模型,分析了影响混合量测数据兼容的主要因素。通过对WAMS和SCADA量测的相关度分析,实现了WAMS和SCADA数据的同步,考虑同步性引起的误差重新量化了量测权值,达到使混合量测数据在状态估计中兼容的目的。
提出了一种以WAMS/SCADA混合量测为基础的状态估计错误参数辨识和修正的方法。将WAMS量测对应的功率残差和零注入节点功率残差,与SCADA量测残差相结合,通过优化理论构造参数误差与各残差的灵敏度关系矩阵,进而实现状态估计中错误参数的辨识和修正。
提出了一种WAMS/SCADA混合量测中的PMU配置方案,根据电气距离建立了网络中各个节点彼此的耦合关系,应用模糊c-均值聚类方法对节点进行分群,辨识具有相似动态特性的节点,通过在各节点群中配置PMU,达到捕捉系统典型动态信息的目的。
最后,提出了一种基于WAMS/SCADA混合量测的电力系统动态状态估计算法。通过在SCADA量测的基础上引入WAMS量测,提高动态状态估计的精度；同时运用无迹卡尔曼滤波方法,避免了线性化误差的引入和雅克比矩阵的计算,相比传统扩展卡尔曼滤波方法能够更准确地估计动态系统中的状态变量。
State estimation provides the real-time operational state of power system, and it's a prerequisite to ensure the normal operation of the system. The conventional state estimation which based on SCADA has the disadvantages of low accuracy and poor synchronization, and the emergence and development of WAMS brings new opportunities for improving the performance of state estimation. From the economy and technique views to consider, WAMS and SCADA exists at the same time is the inexorable trend. However, WAMS and SCADA based on different technical platforms, so how to combine the WAMS data with the SCADA data is a difficult problem. Aiming at some basic questions of WAMS/SCADA hybrid state estimation, this paper makes in-depth and careful studies on static state estimation algorithm, dynamic state estimation algorithm, optimal PMU placement, compatibility problem, identification and correction of the parameters errors. The main achievements of this paper are as follows:
This paper proposes two kinds of static state estimation algorithms which based on WAMS/SCADA hybrid data. The first algorithm constructs a state estimation objective function which combined with the constraints of WAMS and zero-injection nodes. The second algorithm expands the observation range of PMU by introducing pseudo measurements. Then, analyzing and comparing the adaptability of two algorithms under different working conditions.
This paper establishs mathematical model about the compatible problem of WAMS/SCADA hybrid data, and analyzes the main factors which effect data compatibility. This solution implements the synchronization of WAMS and SCADA data through the correlation analysis, and then re-quantified the measure weights by considering the delay distribution of measurement data, to make hybrid data have compatibility.
On the basis of WAMS/SCADA hybrid measurements, a new method to identify and correct the parameters errors is proposed in this paper. Combining the residuals power and zero-injection node power flows acquired by WAMS measurements with the residuals of SCADA measurements, using the optimization theory to construct the sensibility matrix with respect to the residuals and parameters errors. And then, the identification and correction of parameters errors can be achieved.
This paper presents a new method for an optimal placement of PMU in hybrid measurement system. This method establishs the coupling relationship of network nodes based on electrical distance, and uses the Fuzzy c-means clustering technique to group the network nodes, a class of nodes which have similar dynamic characteristics can be replaced by grouping node. Then, PMU which placed in the grouping node can capture the typical dynamic information.
The final study of this paper is about dynamic state estimation algorithm which based on WAMS/SCADA hybrid measurements. This algorithm introduces the WAMS data to improve the accuracy of state estimation, and uses Unscented Kalman Filter (UKF) to avoid the error caused by the linearization process and the complicated calculation of Jacobian matrix, so it can estimate the state variables of dynamic system more accurately.
引文
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