电力系统分布式动态状态估计研究
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摘要
同步相量测量单元(phasor measurement unit, PMU)能够对电力系统机电暂态过程中相量信息进行直接测量,为电力系统动态安全监控提供了新的技术手段。然而,由于传感器误差以及干扰的影响,PMU量测不可避免地存在随机误差和不良数据。如果不对PMU量测量进行处理而直接应用,则有可能无法准确监测电力系统动态过程,甚至导致控制系统做出错误的控制策略。针对PMU量测信息,本文系统地研究了机电暂态过程中分布式动态状态估计方法。论文主要研究成果如下：
(1)提出分布式动态状态估计框架。在发电厂和变电站分别进行发电机动态状态估计和变电站零阻抗特性状态估计,将估计结果上送至调度中心进行数据整合并实施全系统状态估计。提出了一种系统机电暂态过程中基于PMU的发电机动态状态估计新方法。该方法充分考虑系统机电暂态过程中调速器对发电机机械转矩的调节作用。建立了系统机电暂态过程中发电机动态状态估计模型；给出了系统噪声误差方差的具体计算方法；进一步提出基于比例对称采样无迹卡尔曼滤波的发电机动态状态估计算法。仿真结果表明提出的方法精度高于机械转矩恒定的方法。
(2)针对PMU量测中存在不良数据的问题,提出一种鲁棒性发电机动态状态估计算法。将时变多维观测噪声尺度因子引入到容积卡尔曼滤波中,根据量测新息对量测误差进行在线调整,使其更加逼近真实噪声。再利用调整后的误差计算滤波增益,使其能够在PMU量测存在不良数据的情况下对状态量预报值进行准确修正从而得到精确的发电机状态量估计值。针对时变多维观测噪声尺度因子为非对角阵而造成滤波增益求逆发生奇异的问题,提出解决方案。仿真结果表明,当PMU出现连续多点坏数据时,鲁棒动态状态估计仍然能够得到准确的估计结果。
(3)提出了一种系统机电暂态过程中,基于PMU的变电站状态估计新方法。该方法将变电站内断路器的零阻抗特性作为虚拟量测,进一步提升冗余度。同时,在系统故障后断路器状态未知的情况下建立状态估计模型,能够有效辨识断路器的实际状态。针对PMU量测存在不良数据的问题,给出了基于非二次准则状态估计的不良数据辨识方法,并对门槛值的选取方案进行了改进,能够有效辨识不良数据。
(4)提出了一种机电暂态过程中全系统状态估计方法。基于机网接口的直接解法,给出了发电机动态状态估计结果转化为网络节点电压相量伪量测的误差方差计算方法；提出了考虑发电机动态状态估计约束的全系统状态估计方法,通过发电机动态状态估计约束进一步提升机电暂态过程中系统状态量的估计精度。
Phasor measurement unit can measure the phasors in the electromechanical transient process, which provide a new opptunity of power system dynamic monitoring and control. However, the PMU measurements experience random errors and bad data unavoidablely due to the sensor errors and disturbances. If the PMU measurements are applied directly without any treatment, the power system dynamic process may not be monitored accurately, even the wrong control scheme is likely be conducted by control system. In this dissertation, a power system dynamic state estimator in the electromechanical transient process based on PMU is studied. The main work of this dissertation is summarized as follows:
(1) A framework of distributed dynamic state estimtimator is proposed. The dynamic state estimator for synchronous machines and the state estimator based on zero impedance characteristic are conducted in power plants and substations respectively. The estimation results are sent to the dispatch center where the power system state estimator is conducted. A novel approach for dynamic state estimator for synchronous machines in the electromachenical transient process based on PMU is proposed. The regulation effect of speed governors is considered. The model of dynamic state estimator for synchronous machines in the electromechanical transient process is built. The detailed calculation method of the system variance is given. The solving algorithm of the dynamic state estimator for synchronous machines based on the unscented Kalman filter using proportion symmetric sampling scheme is proposed. The simulation result shows that the precision of the proposed state estimator is higher than the method that the mechanical torque is assumed to be constant.
(2) The PMU measurements experience bad data unavoidedly in the electromachenical transient process. Aiming at this problem, the robust dynamic state estimator for synchronous machines is proposed. A time-varying multidimensional scalar is introduced into the cubature Kalman filter, which regulate the measurements error in real time according to the innovation. Then, the regulated error is used to calculate the Kalman filtering scalar, which can accurately rectify the prediction of states under the condition that the PMU measurements experience bad data. The accurate estimation value of generator states can be obtained. If the time-vary multidimensional scalar is a non-diagonal matrix, the Kalman filter scalar will be a singular matrix, whose inverse matrix cannot be obtained. Aiming at this problem, the solving method is proposed. The detailed process of dynamic state estimator based on the robust cubature Kalman filter is presented. The simulation result shows that the robust dynamic state estimor can obtaine the accurate estimation results when PMU experiences bad data.
(3) A novel approach for the substation state estimation based on PMU in the electromechanical transient process is proposed. In the approach, the redundancy of measurements is increased by taking the zero-impedance characteristic of circuit breakers as virtual measurements. At the same time, the state estimation model is built under the condition that the statues of the circuit breakers cannot be obtained in real time after a fault occurred in the system. By solving the model, the real status of circuit breakers can be obtained. The bad data identification method based on the non-quadratic rule state estimator is presented, and the method of choosing the threshold value is modified, which can identify the bad data effectively.
(4) A state estimation method for the power system in the electromechanical transient process is proposed. Based on the direct solving method of machine-network interfacing, the calculation method of the pseudo measurement error of network voltage phasor transformed from the dynamic state estimation results for synchronous machines is given. The power system state estimator in the electromechanical transient process is proposed, in which the constraint of dynamic state estimator for synchronous machines is used to increase the accuracy.
(1)提出分布式动态状态估计框架。在发电厂和变电站分别进行发电机动态状态估计和变电站零阻抗特性状态估计,将估计结果上送至调度中心进行数据整合并实施全系统状态估计。提出了一种系统机电暂态过程中基于PMU的发电机动态状态估计新方法。该方法充分考虑系统机电暂态过程中调速器对发电机机械转矩的调节作用。建立了系统机电暂态过程中发电机动态状态估计模型；给出了系统噪声误差方差的具体计算方法；进一步提出基于比例对称采样无迹卡尔曼滤波的发电机动态状态估计算法。仿真结果表明提出的方法精度高于机械转矩恒定的方法。
(2)针对PMU量测中存在不良数据的问题,提出一种鲁棒性发电机动态状态估计算法。将时变多维观测噪声尺度因子引入到容积卡尔曼滤波中,根据量测新息对量测误差进行在线调整,使其更加逼近真实噪声。再利用调整后的误差计算滤波增益,使其能够在PMU量测存在不良数据的情况下对状态量预报值进行准确修正从而得到精确的发电机状态量估计值。针对时变多维观测噪声尺度因子为非对角阵而造成滤波增益求逆发生奇异的问题,提出解决方案。仿真结果表明,当PMU出现连续多点坏数据时,鲁棒动态状态估计仍然能够得到准确的估计结果。
(3)提出了一种系统机电暂态过程中,基于PMU的变电站状态估计新方法。该方法将变电站内断路器的零阻抗特性作为虚拟量测,进一步提升冗余度。同时,在系统故障后断路器状态未知的情况下建立状态估计模型,能够有效辨识断路器的实际状态。针对PMU量测存在不良数据的问题,给出了基于非二次准则状态估计的不良数据辨识方法,并对门槛值的选取方案进行了改进,能够有效辨识不良数据。
(4)提出了一种机电暂态过程中全系统状态估计方法。基于机网接口的直接解法,给出了发电机动态状态估计结果转化为网络节点电压相量伪量测的误差方差计算方法；提出了考虑发电机动态状态估计约束的全系统状态估计方法,通过发电机动态状态估计约束进一步提升机电暂态过程中系统状态量的估计精度。
Phasor measurement unit can measure the phasors in the electromechanical transient process, which provide a new opptunity of power system dynamic monitoring and control. However, the PMU measurements experience random errors and bad data unavoidablely due to the sensor errors and disturbances. If the PMU measurements are applied directly without any treatment, the power system dynamic process may not be monitored accurately, even the wrong control scheme is likely be conducted by control system. In this dissertation, a power system dynamic state estimator in the electromechanical transient process based on PMU is studied. The main work of this dissertation is summarized as follows:
(1) A framework of distributed dynamic state estimtimator is proposed. The dynamic state estimator for synchronous machines and the state estimator based on zero impedance characteristic are conducted in power plants and substations respectively. The estimation results are sent to the dispatch center where the power system state estimator is conducted. A novel approach for dynamic state estimator for synchronous machines in the electromachenical transient process based on PMU is proposed. The regulation effect of speed governors is considered. The model of dynamic state estimator for synchronous machines in the electromechanical transient process is built. The detailed calculation method of the system variance is given. The solving algorithm of the dynamic state estimator for synchronous machines based on the unscented Kalman filter using proportion symmetric sampling scheme is proposed. The simulation result shows that the precision of the proposed state estimator is higher than the method that the mechanical torque is assumed to be constant.
(2) The PMU measurements experience bad data unavoidedly in the electromachenical transient process. Aiming at this problem, the robust dynamic state estimator for synchronous machines is proposed. A time-varying multidimensional scalar is introduced into the cubature Kalman filter, which regulate the measurements error in real time according to the innovation. Then, the regulated error is used to calculate the Kalman filtering scalar, which can accurately rectify the prediction of states under the condition that the PMU measurements experience bad data. The accurate estimation value of generator states can be obtained. If the time-vary multidimensional scalar is a non-diagonal matrix, the Kalman filter scalar will be a singular matrix, whose inverse matrix cannot be obtained. Aiming at this problem, the solving method is proposed. The detailed process of dynamic state estimator based on the robust cubature Kalman filter is presented. The simulation result shows that the robust dynamic state estimor can obtaine the accurate estimation results when PMU experiences bad data.
(3) A novel approach for the substation state estimation based on PMU in the electromechanical transient process is proposed. In the approach, the redundancy of measurements is increased by taking the zero-impedance characteristic of circuit breakers as virtual measurements. At the same time, the state estimation model is built under the condition that the statues of the circuit breakers cannot be obtained in real time after a fault occurred in the system. By solving the model, the real status of circuit breakers can be obtained. The bad data identification method based on the non-quadratic rule state estimator is presented, and the method of choosing the threshold value is modified, which can identify the bad data effectively.
(4) A state estimation method for the power system in the electromechanical transient process is proposed. Based on the direct solving method of machine-network interfacing, the calculation method of the pseudo measurement error of network voltage phasor transformed from the dynamic state estimation results for synchronous machines is given. The power system state estimator in the electromechanical transient process is proposed, in which the constraint of dynamic state estimator for synchronous machines is used to increase the accuracy.
引文
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