基于WAMS的电力系统状态估计若干问题研究
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摘要
目前,世界各国正在兴起建设智能电网的浪潮,我国也提出了建设以特高压电网为骨干网架、各级电网协调发展的坚强智能电网,这对现代高级调度中心的自动化水平要求越来越高。电力系统状态估计是现代电力调度中心能量管理系统(EMS)的重要组成部分,是电力系统智能化实时分析和控制功能的基础。
传统状态估计主要是基于监控和数据采集(SCADA)系统提供的遥测量。随着90年代基于相量测量单元(PMU)的广域测量系统(WAMS)在电力系统中的广泛应用,为电力系统状态估计理论的发展带来了新的契机,PMU量测已成为电力系统重要的数据源之一。在现阶段,把WAMS和EMS系统集成,是当前电力系统分析和控制功能的必然要求。
本论文基于WAMS技术,结合当前已有的状态估计理论研究成果,主要研究内容如下：
1.提出了采用等效电流量测变换法将非线性静态状态估计器变换为线性估计器,并将PMU状态向量量测变换为相应支路电流等效修正量测计入线性估计器中进行线性估计以提高状态估计器的收敛速度和滤波精度。
2.提出了一种新的PMU配置方法。在传统SCADA量测系统的基础上,部分安装PMU形成混合非线性量测模型,利用母线出线数和母线注入功率相结合的原则配置PMU,有利于提高系统可观测性、状态估计精度和状态方程的数值稳定性。
3.提出了基于WAMS的电力系统自适应动态状态估计算法,在线辨识和修正状态估计模型参数以改善算法在正常情况、存在坏数据、负荷突变／发电机输出功率突变、网络拓扑结构错误各种情况下的滤波效果。
4.在传统扩展卡尔曼滤波(EKF)算法基础上,提出了两种在线辨识和修正模型误差协方差矩阵的方法,并在这两种状态估计模型中引入量测函数的非线性项,这不仅可以在线辨识和估计系统中未知或不精确的噪声统计特性,而且由于计及了量测函数的非线性项,非线性系统在线性化过程中所产生的线性化误差完全得到补偿,同时引入PMU测量的电压幅值和相角量测,大大改善了系统在正常情况、存在坏数据、负荷突变、网络拓扑错误各种情况下的预测和滤波精度。
5.由于WAMS/SCADA系统中不可避免地存在粗量测误差和系统运行状态突变等各种异常情况,基于动态状态估计器的预测功能,采用上述自适应动态状态估计器并利用标准化新息和加权新息来检测、辨识和排除WAMS/SCADA系统中存在多个坏数据、负荷突变、网络拓扑结构错误以及这3种异常同时发生的情况。
本文对所提算法均编制了相应的程序,并进行了仿真试验,仿真结果验证了本文所提各种算法的可行性和有效性。
At present, smart grid construction is getting attention all around the world. In China, strong smart grid based on ultra-high voltage (UHV) power grid is under construction. It needs even much higher automation level of modern dispatching centers. During this process, power system state estimation is an important constituent of modern electric dispatching centers, and it is the basis of realizing the power system real-time analysis and control.
The remote measurements for traditional state estimator are gathered by supervisory control and data acquisition (SCADA) system. In 1990s, with the widely application in power systems of wide area measurement system (WAMS), which is based on phasor measurement unit (PMU), it brings a new chance for the development of power system state estimation. At present, PMU measurements have become one of the most important data sources of power system. Therefore, it is an inevitable trend to integrate WAMS and energy management system (EMS) for power system analysis and control.
According to WAMS and nowdays research of state estimation, the main contents of this thesis are as follows:
1. The method of equivalent current measurement transformation is proposed to transform nonlinear static state estimator into a linear estimator; PMU measurements of state variables is transformed into equivalent correction measurements of the branch current correspondingly. They are incorporated into the linear estimator to do linear estimation for fast convergence and high filtering precision.
2. A new method for selection of suitable PMU placement is presented. Based on traditional SCADA measurement system, a hybrid non-linear measurement model is built by installing PMU partially. And the PMU placements are selected by using the theory of outlets number and bus injections, which perfectly improves the system observability, state estimation precision and numerical stability of the state equations.
3. An adaptive dynamic state estimation algorithm based on WAMS for power systems is introduced. According to the new algorithm, estimation model parameters are on-line identified and modified to improve the filtering performance under different scenarios such as normal operation, bad measurements, sudden load change/drastic generation variation and topology errors.
4. Based on traditional extended Kalman filter (EKF) algorithm, two methods of modifying the model error covariance matrix are presented; the measurement function nonlinearities are incorporated in the two dynamic state estimation models. That is, when the filter is conducted, the unknown or imprecise noise statistic characteristic is identified and modified on-line simultaneously. Additionally, since the nonlinearities of the measurement function are integrated into the state estimation models, linearized error yielding from the linearization of nonlinear power system is fully compensated. Besides, the voltage magnitudes and phase angles measured by PMU are introduced in the observed measurements. All of these greatly improve the forecasting and filtering performances of the dynamic state estimation under different situations that include normal operation, sudden load change, bad measurements and topology error conditions.
5. As WAMS/SCADA system inevitably contains various anomalies such as gross measurement errors and sudden changes of system status, a new method for detection, identification and elimination of anomalies during power system adaptive dynamic state estimation is presented. In the new method, according to the capability of forecasting the system state of the dynamic state estimator, normalized innovation and weighted innovations are used to diagnose, recognize and process the anomalies such as multi bad data, sudden load changes, network topology errors and the simultaneous occurrences of the three anomalies on the system states.
Based on all of the above proposed methods, software is developed and tested with some examples. The simulation results show the feasibility and validity of all the presented methods.
传统状态估计主要是基于监控和数据采集(SCADA)系统提供的遥测量。随着90年代基于相量测量单元(PMU)的广域测量系统(WAMS)在电力系统中的广泛应用,为电力系统状态估计理论的发展带来了新的契机,PMU量测已成为电力系统重要的数据源之一。在现阶段,把WAMS和EMS系统集成,是当前电力系统分析和控制功能的必然要求。
本论文基于WAMS技术,结合当前已有的状态估计理论研究成果,主要研究内容如下：
1.提出了采用等效电流量测变换法将非线性静态状态估计器变换为线性估计器,并将PMU状态向量量测变换为相应支路电流等效修正量测计入线性估计器中进行线性估计以提高状态估计器的收敛速度和滤波精度。
2.提出了一种新的PMU配置方法。在传统SCADA量测系统的基础上,部分安装PMU形成混合非线性量测模型,利用母线出线数和母线注入功率相结合的原则配置PMU,有利于提高系统可观测性、状态估计精度和状态方程的数值稳定性。
3.提出了基于WAMS的电力系统自适应动态状态估计算法,在线辨识和修正状态估计模型参数以改善算法在正常情况、存在坏数据、负荷突变／发电机输出功率突变、网络拓扑结构错误各种情况下的滤波效果。
4.在传统扩展卡尔曼滤波(EKF)算法基础上,提出了两种在线辨识和修正模型误差协方差矩阵的方法,并在这两种状态估计模型中引入量测函数的非线性项,这不仅可以在线辨识和估计系统中未知或不精确的噪声统计特性,而且由于计及了量测函数的非线性项,非线性系统在线性化过程中所产生的线性化误差完全得到补偿,同时引入PMU测量的电压幅值和相角量测,大大改善了系统在正常情况、存在坏数据、负荷突变、网络拓扑错误各种情况下的预测和滤波精度。
5.由于WAMS/SCADA系统中不可避免地存在粗量测误差和系统运行状态突变等各种异常情况,基于动态状态估计器的预测功能,采用上述自适应动态状态估计器并利用标准化新息和加权新息来检测、辨识和排除WAMS/SCADA系统中存在多个坏数据、负荷突变、网络拓扑结构错误以及这3种异常同时发生的情况。
本文对所提算法均编制了相应的程序,并进行了仿真试验,仿真结果验证了本文所提各种算法的可行性和有效性。
At present, smart grid construction is getting attention all around the world. In China, strong smart grid based on ultra-high voltage (UHV) power grid is under construction. It needs even much higher automation level of modern dispatching centers. During this process, power system state estimation is an important constituent of modern electric dispatching centers, and it is the basis of realizing the power system real-time analysis and control.
The remote measurements for traditional state estimator are gathered by supervisory control and data acquisition (SCADA) system. In 1990s, with the widely application in power systems of wide area measurement system (WAMS), which is based on phasor measurement unit (PMU), it brings a new chance for the development of power system state estimation. At present, PMU measurements have become one of the most important data sources of power system. Therefore, it is an inevitable trend to integrate WAMS and energy management system (EMS) for power system analysis and control.
According to WAMS and nowdays research of state estimation, the main contents of this thesis are as follows:
1. The method of equivalent current measurement transformation is proposed to transform nonlinear static state estimator into a linear estimator; PMU measurements of state variables is transformed into equivalent correction measurements of the branch current correspondingly. They are incorporated into the linear estimator to do linear estimation for fast convergence and high filtering precision.
2. A new method for selection of suitable PMU placement is presented. Based on traditional SCADA measurement system, a hybrid non-linear measurement model is built by installing PMU partially. And the PMU placements are selected by using the theory of outlets number and bus injections, which perfectly improves the system observability, state estimation precision and numerical stability of the state equations.
3. An adaptive dynamic state estimation algorithm based on WAMS for power systems is introduced. According to the new algorithm, estimation model parameters are on-line identified and modified to improve the filtering performance under different scenarios such as normal operation, bad measurements, sudden load change/drastic generation variation and topology errors.
4. Based on traditional extended Kalman filter (EKF) algorithm, two methods of modifying the model error covariance matrix are presented; the measurement function nonlinearities are incorporated in the two dynamic state estimation models. That is, when the filter is conducted, the unknown or imprecise noise statistic characteristic is identified and modified on-line simultaneously. Additionally, since the nonlinearities of the measurement function are integrated into the state estimation models, linearized error yielding from the linearization of nonlinear power system is fully compensated. Besides, the voltage magnitudes and phase angles measured by PMU are introduced in the observed measurements. All of these greatly improve the forecasting and filtering performances of the dynamic state estimation under different situations that include normal operation, sudden load change, bad measurements and topology error conditions.
5. As WAMS/SCADA system inevitably contains various anomalies such as gross measurement errors and sudden changes of system status, a new method for detection, identification and elimination of anomalies during power system adaptive dynamic state estimation is presented. In the new method, according to the capability of forecasting the system state of the dynamic state estimator, normalized innovation and weighted innovations are used to diagnose, recognize and process the anomalies such as multi bad data, sudden load changes, network topology errors and the simultaneous occurrences of the three anomalies on the system states.
Based on all of the above proposed methods, software is developed and tested with some examples. The simulation results show the feasibility and validity of all the presented methods.
引文
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