基于PMU量测信息的面向过程状态估计研究
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摘要
电力系统状态估计是能量管理系统(EMS-Energy Management System)的重要组成部分,它为在线分析和控制功能提供电网实时工况,其性能直接影响分析的准确性和控制的效果。传统状态估计主要基于监控和数据采集(SCADA-Supervisory Control and Data Acquisition)系统提供的遥测量,采用迭代计算求得状态向量。目前相量测量单元(PMU-Phasor Measurement Unit)已逐步成为电力系统的一个重要数据来源,较之SCADA它具有精度高、全网严格同步、更新周期短等优点,能够实现节点状态的直接测量。因此研究PMU量测信息在状态估计中的应用具有重要的理论及实践意义。
利用部分节点的PMU量测数据,通过电流相量值计算新息建立了交流潮流模型下的新息网络图。交流潮流模型下的新息网络图是电流新息值的物理载体,与直流流潮流模型的建立不同,节点注入电流值利用PMU直接测量或间接计算得到,因此在建立新息网络的过程中需要保留这些注入新息源。选择有PMU电流量测的支路作为连支,其余支路作为树支,然后由连支推算得到没有PMU量测的树支的新息值。在进行计算的时候,考虑节点注入新息源的影响,把新息注入源节点与地节点之间的支路作为连支然后再进行修正,这样便得到更为精确的连支推算新息值。
给出了存在多重网络结构变化等不良情况时,采用交流潮流模型的新息网络图法状态估计的识别逻辑及估计计算过程。当交流潮流模型下的新息网络图建立之后,支路电流值可以由部分连支量经过连支推算得到。针对网络中存在相关的多重拓扑结构变化,分别定义交流潮流模型下的潮流修正电流值和修正预估比,以便进行识别。在排除网络拓扑结构变化的影响之后,根据得到的潮流修正电流值和网络参数就可以线性地估计出网络中各个节点的状态,并对可能存在的坏数据问题进行了讨论。
提出了一种面向过程的新息图法特征状态分析方法,用以处理海量PMU量测数据。首先基于交流潮流模型下的拓展新息图法进行时间过程的划分,然后在过程内建立表征电力系统极端运行情况和平均运行情况的极端运行网络图和期望运行网络图。通过对这些新息网络图的解算,能够快速得到表征电力系统在这个运行时段内的典型特征状态,为控制中心制订及时和全面的决策提供有效依据。
提出了一种基于时间过程新息的SCADA/PMU混合量测状态估计方法。首先把PMU的电流量测转换成功率量测,然后在初始计算时刻与SCADA量测一起进行非线性运算,得到状态估计值和功率量测量之间的灵敏度矩阵。在以后的PMU采样时刻将转换的PMU量测和通过负荷预报补充的伪量测组成混合量测,然后根据求得的灵敏度矩阵进行以PMU采样为周期的线性跟踪计算。当估计误差积累到一定程度时需要从新进行一次混合非线性计算,以更新灵敏度矩阵。
Power system state estimation is an important part of Energy Management System (EMS), it can provide real-time state for the on-line analysis and control function. Its performance influences the validity of analysis and the effect of control. Traditionally state estimation uses the remote measurements provided by the Supervisory Control And Data Acquisition(SCADA)system, obtaining the state vector iteratively. Nowadays the Phasor Measurement Unit( PMU)becomes an important source of the measurements gradually, compared with SCADA it has the virtue of high precision, strict synchronization of the whole system, short renewal cycle, etc., and can realize the direct measure of the state vectors. Thus the researches concerned with the using of information provided by PMU in the state estimation area have important theoretical and practical meanings.
By utilizing PMUs’measurements of partial nodes as well as current phasors construct the innovation network graph under the AC load flow model. The innovation network graph under the AC load flow model is the physical carrier of current innovation. Different from the construction of DC load flow model, the nodal injecting current are either measured directly by PMU or calculated in an indirect way, so these injecting innovations should be kept during the whole process of constructing the innovation network. Choose the branch which had PMU current measurement as link, others as tree, then use link to deduce the innovation of the tree which had no PMU measurements. During the calculation, the effects of nodal injecting innovation source should be taken into account, the branch between innovation injecting source node and ground node should be looked as link and modified again so as to obtain more precise link deduction innovation.
When there are ill-conditions such as multiple network configuration changes, the identification logics and estimation calculation procedure are provided. After the construction of the innovation network graph in the AC load flow model, the branch current phasors can be reckoned by the current phasors of the link branch. Aim at the multiple topology changes of the network, the corrected current phasor and the ratio of the corrected current to the forecasting current phasor of the AC load flow model are defined to identify it. After eliminating the influence of the topology changes, by using direct PMU measurements, the corrected current phasors as well as the network parameters the network state can be calculated linearly. The problem of the possible existing PMU bad data is also discussed.
A novel process-oriented characteristic state analysis method is put forward, so as to deal with the abundant PMU measurements. First the time process is divided by the innovation network graph in the AC load flow model, then the extreme innovation network graph and the expected innovation network graph, which present the extreme running condition and the medial running condition of the power system, is constructed. Through the computation of these innovation networks, the typical network state representing the running condition of the process can be obtained quickly, and it can provide effective information for the control center to formulate timely and comprehensive strategies.
A SCADA/PMU mixed measurements state estimation method based on the process innovation vectors is also put forward. First the PMU current measurements is transformed into power measurements, then mixed with the SCADA measurements and carry out non-linear calculation at the beginning time, thus the sensitivity matrix of the estimated state vector to the power measurements can be obtained. In the following PMU sampling points, the transformed PMU measurements and the pseudo-measurements derived from the load forecasting are mingled together to form the mixed-measurements, then based on obtained sensitivity matrix, the linear tracking state estimation can be carried out in accordance with the sampling cycle of PMU. When the estimation error is accumulated to some extent, the non-linear state estimation is performed once again so as to update the sensitivity matrix.
利用部分节点的PMU量测数据,通过电流相量值计算新息建立了交流潮流模型下的新息网络图。交流潮流模型下的新息网络图是电流新息值的物理载体,与直流流潮流模型的建立不同,节点注入电流值利用PMU直接测量或间接计算得到,因此在建立新息网络的过程中需要保留这些注入新息源。选择有PMU电流量测的支路作为连支,其余支路作为树支,然后由连支推算得到没有PMU量测的树支的新息值。在进行计算的时候,考虑节点注入新息源的影响,把新息注入源节点与地节点之间的支路作为连支然后再进行修正,这样便得到更为精确的连支推算新息值。
给出了存在多重网络结构变化等不良情况时,采用交流潮流模型的新息网络图法状态估计的识别逻辑及估计计算过程。当交流潮流模型下的新息网络图建立之后,支路电流值可以由部分连支量经过连支推算得到。针对网络中存在相关的多重拓扑结构变化,分别定义交流潮流模型下的潮流修正电流值和修正预估比,以便进行识别。在排除网络拓扑结构变化的影响之后,根据得到的潮流修正电流值和网络参数就可以线性地估计出网络中各个节点的状态,并对可能存在的坏数据问题进行了讨论。
提出了一种面向过程的新息图法特征状态分析方法,用以处理海量PMU量测数据。首先基于交流潮流模型下的拓展新息图法进行时间过程的划分,然后在过程内建立表征电力系统极端运行情况和平均运行情况的极端运行网络图和期望运行网络图。通过对这些新息网络图的解算,能够快速得到表征电力系统在这个运行时段内的典型特征状态,为控制中心制订及时和全面的决策提供有效依据。
提出了一种基于时间过程新息的SCADA/PMU混合量测状态估计方法。首先把PMU的电流量测转换成功率量测,然后在初始计算时刻与SCADA量测一起进行非线性运算,得到状态估计值和功率量测量之间的灵敏度矩阵。在以后的PMU采样时刻将转换的PMU量测和通过负荷预报补充的伪量测组成混合量测,然后根据求得的灵敏度矩阵进行以PMU采样为周期的线性跟踪计算。当估计误差积累到一定程度时需要从新进行一次混合非线性计算,以更新灵敏度矩阵。
Power system state estimation is an important part of Energy Management System (EMS), it can provide real-time state for the on-line analysis and control function. Its performance influences the validity of analysis and the effect of control. Traditionally state estimation uses the remote measurements provided by the Supervisory Control And Data Acquisition(SCADA)system, obtaining the state vector iteratively. Nowadays the Phasor Measurement Unit( PMU)becomes an important source of the measurements gradually, compared with SCADA it has the virtue of high precision, strict synchronization of the whole system, short renewal cycle, etc., and can realize the direct measure of the state vectors. Thus the researches concerned with the using of information provided by PMU in the state estimation area have important theoretical and practical meanings.
By utilizing PMUs’measurements of partial nodes as well as current phasors construct the innovation network graph under the AC load flow model. The innovation network graph under the AC load flow model is the physical carrier of current innovation. Different from the construction of DC load flow model, the nodal injecting current are either measured directly by PMU or calculated in an indirect way, so these injecting innovations should be kept during the whole process of constructing the innovation network. Choose the branch which had PMU current measurement as link, others as tree, then use link to deduce the innovation of the tree which had no PMU measurements. During the calculation, the effects of nodal injecting innovation source should be taken into account, the branch between innovation injecting source node and ground node should be looked as link and modified again so as to obtain more precise link deduction innovation.
When there are ill-conditions such as multiple network configuration changes, the identification logics and estimation calculation procedure are provided. After the construction of the innovation network graph in the AC load flow model, the branch current phasors can be reckoned by the current phasors of the link branch. Aim at the multiple topology changes of the network, the corrected current phasor and the ratio of the corrected current to the forecasting current phasor of the AC load flow model are defined to identify it. After eliminating the influence of the topology changes, by using direct PMU measurements, the corrected current phasors as well as the network parameters the network state can be calculated linearly. The problem of the possible existing PMU bad data is also discussed.
A novel process-oriented characteristic state analysis method is put forward, so as to deal with the abundant PMU measurements. First the time process is divided by the innovation network graph in the AC load flow model, then the extreme innovation network graph and the expected innovation network graph, which present the extreme running condition and the medial running condition of the power system, is constructed. Through the computation of these innovation networks, the typical network state representing the running condition of the process can be obtained quickly, and it can provide effective information for the control center to formulate timely and comprehensive strategies.
A SCADA/PMU mixed measurements state estimation method based on the process innovation vectors is also put forward. First the PMU current measurements is transformed into power measurements, then mixed with the SCADA measurements and carry out non-linear calculation at the beginning time, thus the sensitivity matrix of the estimated state vector to the power measurements can be obtained. In the following PMU sampling points, the transformed PMU measurements and the pseudo-measurements derived from the load forecasting are mingled together to form the mixed-measurements, then based on obtained sensitivity matrix, the linear tracking state estimation can be carried out in accordance with the sampling cycle of PMU. When the estimation error is accumulated to some extent, the non-linear state estimation is performed once again so as to update the sensitivity matrix.
引文
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