电力系统低频振荡关键线路/母线辨识与控制研究
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摘要
大电网互联输电在带来巨大的经济和技术方面的益处的同时,也使低频振荡成为威胁弱互联电网安全稳定运行的关键问题之一。
近年来,广域相量测量系统(WAMS)在世界各国电网得到了广泛的应用,使得运行过程中的各状态量能够都得到实时监测。采用先进的数学方法从实时监测得到的数据中获得相关信息并采取适当的控制措施成为各国电力系统专家研究的热点问题。但也对电力系统动态稳定性分析方法提出更高的要求,如何找到系统振荡过程中网络中的最为薄弱环节,从而以最小的量测代价获得最有价值的系统动态信息,并实现相应控制策略是亟待解决的关键问题。
本文在保留原网络结构模型基础上,计及负荷和励磁系统的动态特性,以及网络的功率约束方程,建立了保留网络信息的线性化状态方程和状态矩阵,同时在推导过程中,回避了繁琐的d-q变换。利用结构保留模型下的线性化状态矩阵得到的特征值和特征向量,建立了可方便求解网络中各元件振荡功率增量的算法,不仅避免传统方法复杂的计算过程,还能够分析负荷动态特性、网络结构的影响。提出了支路脆弱性指标和振荡割集的概念,从电力系统网络信息角度对低频振荡进行了分析,同时利用特征值灵敏度分析法对各机电模式所对应的关键母线进行了计算与分析。
Prony算法和ARMA算法受噪声、系统实际阶数的影响较大,带噪声的非线性、非平稳信号处理结果的精度受到了质疑;HHT变换过程缺乏坚实的数学基础,辨识过程复杂,导致整个计算过程占用的时间较长,很难应用于电力系统低频振荡的在线辨识。针对现有辨识方法的不足,本文将振动工程模态参数辨识中广泛应用的随机子空间辨识(SSI)算法引入到电力系统低频振荡分析和控制中来。辨识过程中利用稳定图法确定系统阶数,同时随机子空间辨识(SSI)算法能够有效的克服噪声的影响,而且具有计算过程简单,耗时少的优点,同时利用辨识结果和留数法对PSS参数进行整定。该方法无需事先知道扰动信号,只根据发电机有功功率振荡情况即可对其PSS参数进行整定,可以用于PSS参数的实时调整,对低频振荡进行在线辨识和控制。
对四机两区域算例系统进行仿真与分析,算例结果验证了本文提出的计及负荷频率阻尼特性线性化状态空间模型的正确性及振荡功率增量分布算法、关键支路/母线辨识方法及振荡割集的有效性。测试信号、动模试验数据及数字仿真充分验证了随机子空间辨识(SSI)算法在电力系统低频振荡模态参数辨识及控制中的优越性。
Power system dynamics is highly complex, especially the low-frequency oscillation is becoming a serious bottleneck for increasing power transfer.
With the wide applications of WAMS, synchrophasors can be accurately measured and quickly transferred to the control center which makes it possible to identify and control oscillation online. Advanced mathematical tools with the potential to identify and characterize these dynamics in near real time have been applied very successfully to power system. But the higher requirements were put forward to the power system stbability analysis methods. And how to identify the Critical Link of oscillations becomes the important issue which is to be solved urgently.
Based on the structure-preserving power system model, taking excitation, dynamic load model and power equation into account, the state space equation and state space matrix preserving network information was founded. The d-q transformer was avoided in the process of derivation. The novel method for calculating low frequency oscillatory active power increment distribution by employing the eigenvalues and eigenvectors of the linearized state matrix is presented. The method not only can be applied to calculate oscillatory active power increment distribution over generators, transmission lines and loads, but also can be used to analyze the effects of load and network structure on power system low frequency oscillatory. The concept of branch brittleness index and oscillations cutsets is given. The low frequency oscillatory phenomenon is analyzed by employing network information.
The identification results of Prony and ARMA are influenced strongly by signal noise and system order, and the HHT lacks basically mathematical theory. Priory, the process of HHT is very complex and consumes a long time. Based on analyzing the localization of the traditional small signal stability analysis method and identify method, this paper introduces the stochastic subspace identification (SSI) into the overcomes the influence of signal noise and order. And the time consuming in calculating is shorter than the other methods. The parameters of PSS were designed using the results of stochastic subspace identification and residue method. Comparing with other identify method, the parameters of PSS can be designed only using the observational signal. So, this method can be used to analysis and control the low frequency oscillations on-line real-time.
Simulations carried on four-machines example system testify the validation of the proposed method. Test signal and physical simulation data show the strongpoint of stochastic subspace identification used in power system low frequency oscillations analysis.
近年来,广域相量测量系统(WAMS)在世界各国电网得到了广泛的应用,使得运行过程中的各状态量能够都得到实时监测。采用先进的数学方法从实时监测得到的数据中获得相关信息并采取适当的控制措施成为各国电力系统专家研究的热点问题。但也对电力系统动态稳定性分析方法提出更高的要求,如何找到系统振荡过程中网络中的最为薄弱环节,从而以最小的量测代价获得最有价值的系统动态信息,并实现相应控制策略是亟待解决的关键问题。
本文在保留原网络结构模型基础上,计及负荷和励磁系统的动态特性,以及网络的功率约束方程,建立了保留网络信息的线性化状态方程和状态矩阵,同时在推导过程中,回避了繁琐的d-q变换。利用结构保留模型下的线性化状态矩阵得到的特征值和特征向量,建立了可方便求解网络中各元件振荡功率增量的算法,不仅避免传统方法复杂的计算过程,还能够分析负荷动态特性、网络结构的影响。提出了支路脆弱性指标和振荡割集的概念,从电力系统网络信息角度对低频振荡进行了分析,同时利用特征值灵敏度分析法对各机电模式所对应的关键母线进行了计算与分析。
Prony算法和ARMA算法受噪声、系统实际阶数的影响较大,带噪声的非线性、非平稳信号处理结果的精度受到了质疑;HHT变换过程缺乏坚实的数学基础,辨识过程复杂,导致整个计算过程占用的时间较长,很难应用于电力系统低频振荡的在线辨识。针对现有辨识方法的不足,本文将振动工程模态参数辨识中广泛应用的随机子空间辨识(SSI)算法引入到电力系统低频振荡分析和控制中来。辨识过程中利用稳定图法确定系统阶数,同时随机子空间辨识(SSI)算法能够有效的克服噪声的影响,而且具有计算过程简单,耗时少的优点,同时利用辨识结果和留数法对PSS参数进行整定。该方法无需事先知道扰动信号,只根据发电机有功功率振荡情况即可对其PSS参数进行整定,可以用于PSS参数的实时调整,对低频振荡进行在线辨识和控制。
对四机两区域算例系统进行仿真与分析,算例结果验证了本文提出的计及负荷频率阻尼特性线性化状态空间模型的正确性及振荡功率增量分布算法、关键支路/母线辨识方法及振荡割集的有效性。测试信号、动模试验数据及数字仿真充分验证了随机子空间辨识(SSI)算法在电力系统低频振荡模态参数辨识及控制中的优越性。
Power system dynamics is highly complex, especially the low-frequency oscillation is becoming a serious bottleneck for increasing power transfer.
With the wide applications of WAMS, synchrophasors can be accurately measured and quickly transferred to the control center which makes it possible to identify and control oscillation online. Advanced mathematical tools with the potential to identify and characterize these dynamics in near real time have been applied very successfully to power system. But the higher requirements were put forward to the power system stbability analysis methods. And how to identify the Critical Link of oscillations becomes the important issue which is to be solved urgently.
Based on the structure-preserving power system model, taking excitation, dynamic load model and power equation into account, the state space equation and state space matrix preserving network information was founded. The d-q transformer was avoided in the process of derivation. The novel method for calculating low frequency oscillatory active power increment distribution by employing the eigenvalues and eigenvectors of the linearized state matrix is presented. The method not only can be applied to calculate oscillatory active power increment distribution over generators, transmission lines and loads, but also can be used to analyze the effects of load and network structure on power system low frequency oscillatory. The concept of branch brittleness index and oscillations cutsets is given. The low frequency oscillatory phenomenon is analyzed by employing network information.
The identification results of Prony and ARMA are influenced strongly by signal noise and system order, and the HHT lacks basically mathematical theory. Priory, the process of HHT is very complex and consumes a long time. Based on analyzing the localization of the traditional small signal stability analysis method and identify method, this paper introduces the stochastic subspace identification (SSI) into the overcomes the influence of signal noise and order. And the time consuming in calculating is shorter than the other methods. The parameters of PSS were designed using the results of stochastic subspace identification and residue method. Comparing with other identify method, the parameters of PSS can be designed only using the observational signal. So, this method can be used to analysis and control the low frequency oscillations on-line real-time.
Simulations carried on four-machines example system testify the validation of the proposed method. Test signal and physical simulation data show the strongpoint of stochastic subspace identification used in power system low frequency oscillations analysis.
引文
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