电力系统静态电压稳定性在线监控(一)--一种可在线应用的计及负荷静态特性的电压稳定指标

基于局部量测数据和负荷静态特性．提出了一种可在线应用的节点静态电压稳定指标。首先介绍了ZIP负荷模型,然后用戴维南等值电路简化系统,将线路电流表达式整理成实部和虚部．消去负荷有功后代入ZIP负荷模型中的无功表达式得二次方程式．用根判别式得到以无功表示的电压稳定指标VSIQ;同理得以有功表示的电压稳定指标VSIP;取两者中的较小值作为节点电压稳定指标VSI。比较各节点VSI,最小值对应的节点即为系统最薄弱节点。以IEEE14节点和IEEE30节点系统为例,仿真系统在不同负荷特性下VSI的变化曲线。结果显示：恒定功率分量所占比例越大则节点VSI越小,电压稳定性越差;恒定功率比重相等情况下,恒定电流分量比重越大．节点电压稳定性越差．     

电力系统静态电压稳定性在线监控(二)--一种在线确定负荷裕度的方法

提出了一种可在线应用的静态电压稳定裕度计算方法．首先基于计及负荷静态特性的节点静态电压稳定指标（VSI）,用直线拟合VSI变化曲线．计算系统最大负荷系数,推导出负荷系数裕度指标,然后将此指标和发电机无功越界裕度指标配合使用．从而可以较好地对系统的负荷裕度进行在线估计．IEEE 30节点的仿真表明该方法应用简单、计算速度快．可用于电力系统运行状态的实时监控.     

基于负荷裕度的在线电压稳定指标

提出了一种基于PMU同步测量数据的负荷节点有功极限和无功极限快速计算方法,在此基础上分别给出电压崩渍的有功裕度指标和无功裕度指标.该方法基于系统的当前运行点,采用π型电路模型对复杂网络进行在线参数辨识,在此基础上推导了负荷的有功裕度和无功裕度的快速计算方法,然后采用基于节点功率的电压稳定指标进行电压稳定判别.该方法能明确区分是由于有功过载还是无功缺乏所引起的电压失稳,并且具有良好的单调性和线性,能够为电压稳定的在线控制提供准确的参考依据.在IEEE 118节点系统中的仿真证明了该方法的有效性.     

Load identification and voltage stability monitoring

The paper describes an algorithm for recursive identification of the power system composite load by using recursive least squares. The variation of the composite load components during slow voltage transients has been shown to deteriorate voltage stability margins. By combining the load identification and voltage stability monitoring, it is possible to make a better assessment of the risks associated with power system operation under heavy loading conditions     

500 kV电容式电压互感器的在线监测装置

为监视 5 0 0kV电容式电压互感器运行情况 ,研制了在线监测的专用装置。介绍了监测装置的原理、功能和实际使用情况     

智能化中压开关柜的在线监测和传感器技术(一)

综述了用于中压开关柜监视、测量的两大类传感器：电流和电压测量保护用传感器；非电量数值传感器。新型的电流和电压传感器优越性明显，在解决了细节及接口问题后，将逐渐安装使用。非电量数值传感器技术是将开关柜“定期”维护转变为“按状态”维护的关键技术，有很好的应用前景。     

智能化中压开关柜的在线监测和传感器技术(二)

综述了用于中压开关柜监视、测量的两大类传感器：电流和电压测量保护用传感器；非电量数值传感器。新型的电流和电压传感器优越性明显，在解决了细节及接口问题后，将逐渐安装使用。非电量数值传感器技术是将开关柜“定期”维护转变为“按状态”维护的关键技术，有很好的应用前景。     

考虑不均衡区域负荷增长的在线电压稳定评估

提出一种基于连续潮流的电力系统在线电压稳定评估方法,该方法考虑了不均衡区域负荷增长模式和发电计划.负荷增长系数由区域短期负荷预测数据得出,区域短期负荷预测考虑多种因素对负荷变化的影响,预测未来24小时区域的负荷情况,根据此负荷情况使用经济调度安排发电计划,在此基础上用连续潮流计算P-U曲线和负荷裕度.在IEEE 118...     

高压XLPE电缆绝缘多参数在线监测

建立了在交流电压作用下高压交联聚乙烯(XLPE)电缆绝缘的参数模型,并分析了电缆绝缘劣化时绝缘参数的外在表现.提出通过监测XLPE电缆的工作绝缘电阻、等值电容、介质损耗因数及各参数的变化趋势来综合评定电缆的绝缘状况.并分析了该方法的有效性及合理性.设计了高压XLPE电缆在线监测装置.在数据处理方面,采用基于LM(Levenberg-Marquardt)算法的非线性最小二乘拟合法对采样信号进行拟合,将信号进行快速傅里叶变换得到的谐波分析结果作为LM算法的迭代初值,并利用拟合结果计算出电缆绝缘的被监测参数.仿真分析表明,该算法收敛速度快、精度高,能很好地抑制电网频率波动、直流分量以及谐波含量对被监测参数的影响.     

10kV运行配电线路雷电感应过电压波形在线监测

雷电感应过电压是造成配电网故障的重要原因之一,针对雷电感应过电压波形特征不清晰的问题,作者采用过电压在线监测系统,对实际运行的佛山10kV富油甲线雷电感应过电压波形进行观测,根据波形特征给出了过电压的波形参数。结果表明,配电线路各相感应电压叠加在运行电压波形上,不同相上感应过电压与对应的回击电流在幅度、时间间隔和波形上都存在同时性,该线路观测的雷电感应电压波形表现为波头时间短、后续发生双极性高频振荡并逐渐转变为低频振荡的波形特点,首次回击感应过电压幅值大于后续回击过电压幅值,首次回击过电压的波头时间比后续过电压长,观测结果对于配电线路雷电保护具有重要意义。     

高压电缆线路护层接地超限电流在线监测系统

通过在线监测系统实时监测护层电流的瞬变、突变监测。捕捉护层电流的突变信号,分析故障原因,定位故障段,为线路抢修和前期预警提供技术条件。     

基于广域测量的在线电压稳定监视

广域测量技术的发展和应用为电力系统电压稳定监视奠定了基础.对采用局部电压、电流变化估计电网侧等值阻抗进行了分析,指出利用SCADA/EMS测量数据不能准确估计电网侧等值阻抗的缺陷,提出基于节点电压和负荷电流相量变化的改进方法和电压稳定性指标.采用广域测量系统间接地避免潮流方程在临界点的不收敛,弥补传统方法在应用中存在的系统规模过大、非线性模型不准确、数据多且更新不及时、速度慢等缺陷.通过仿真计算表明该指标能够正确指示节点的电压稳定性,适于对节点在线静态电压稳定的监视,具有一定实用性.     

基于广域测量的在线电压稳定监视

广域测量技术的发展和应用为电力系统电压稳定监视奠定了基础。对采用局部电压、电流变化估计电网侧等值阻抗进行了分析,指出利用SCADA/EMS测量数据不能准确估计电网侧等值阻抗的缺陷,提出基于节点电压和负荷电流相量变化的改进方法和电压稳定性指标。采用广域测量系统间接地避免潮流方程在临界点的不收敛,弥补传统方法在应用中存在的系统规模过大、非线性模型不准确、数据多且更新不及时、速度慢等缺陷。通过仿真计算表明该指标能够正确指示节点的电压稳定性,适于对节点在线静态电压稳定的监视,具有一定实用性。     

On-line voltage stability assessment using radial basis function network model with reduced input features

In recent years, voltage instability has become a major threat for the operation of many power systems. This paper presents an artificial neural network (ANN)-based approach for on-line voltage security assessment. The proposed approach uses radial basis function (RBF) networks to estimate the voltage stability level of the system under contingency state. Maximum L-index of the load buses in the system is taken as the indicator of voltage stability. Pre-contingency state power flows are taken as the input to the neural network. The key feature of the proposed method is the use of dimensionality reduction techniques to improve the performance of the developed network. Mutual information based technique for feature selection is proposed to enhance overall design of neural network. The effectiveness of the proposed approach is demonstrated through voltage security assessment in IEEE 30-bus system and Indian practical 76 bus system under various operating conditions considering single and double line contingencies and is found to predict voltage stability index more accurate than feedforward neural networks trained by back propagation algorithm and AC load flow. Experimental results show that the proposed method reduces the training time and improves the generalization capability of the network than the multilayer perceptron networks.     

Genetic algorithm based support vector machine for on-line voltage stability monitoring

A Genetic Algorithm based Support Vector Machine (GA-SVM) approach for online monitoring of long-term voltage instability has been proposed in this paper. The conventional methods for voltage stability monitoring are highly time consuming hence infeasible for online application. Support vector machine is a powerful and promising function estimation tool. To improve the accuracy and minimize the training time of SVM, the optimal values of SVM parameters are obtained using genetic algorithm. The proposed approach uses the voltage magnitude and phase angle obtained from Phasor Measurement Units (PMUs) as the input vectors to SVM and the output vector is the Voltage Stability Margin Index (VSMI). The effectiveness of the proposed approach is tested using the New England 39-bus test system and the Indian Northern Region Power Grid (NRPG) 246-bus real system. The results of the proposed GA-SVM approach for voltage stability monitoring are compared with grid search SVM (GS-SVM) and artificial neural networks (ANN) approach with same data set to prove its superiority.     

A new intelligent algorithm for online voltage stability assessment and monitoring

This paper presents a new approach for assessing power system voltage stability based on artificial feed forward neural network (FFNN). The approach uses real and reactive power, as well as voltage vectors for generators and load buses to train the neural net (NN). The input properties of the NN are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The performance of the trained NN is investigated on two systems under various voltage stability assessment conditions. Main advantage is that the proposed approach is fast, robust, accurate and can be used online for predicting the L-indices of all the power system buses simultaneously. The method can also be effectively used to determining local and global stability margin for further improvement measures.     

基于广域测量系统的电压稳定动态监测

基于同步相量技术的广域动态测量系统,可以在时空坐标下监测电力系统动态运行特性,弥补了现有 SCADA 系统和故障滤波系统的不足,为大电网动态电压稳定监测奠定了基础.介绍了WAMS的基本原理,接着比较详细地阐述了电压稳定在线监测系统的内容及其在线应用的具体实现方法.在EPRI-36节点系统上的仿真结果验证了该监测系统的有效性.     

On-line monitoring of eigen-frequency and stability of power system by use of SMES

It is useful to grasp power system stability margin from on-line data in real time. We have proposed to estimate the system operating conditions from on-line data by use of superconducting magnetic energy storage (SMES) system. In this paper, we developed the proposed method to monitor the eigen-frequency of the power system in real time, which changes with time due to the system operating conditions (such as, generator output change), by use of a SMES and a lock-in amplifier. Basic experiments were carried out on an analogue-type power system simulator and a model SMES for the simulator. By use of the SMES, the eigen-frequency change according to the operating condition change was successfully measured, tracked and monitored on-line. The stability margin can be estimated by the monitored eigen-frequency. The availability of the proposed method is discussed on the experimental results.     

An enhanced Radial Basis Function Network for voltage stability monitoring considering multiple contingencies

This paper proposes a scheme for online voltage stability monitoring using an enhanced Radial Basis Function Network (RBFN). A single RBFN is used to predict MW margins for different contingencies. A sequential learning strategy is used along with a regularization technique to design the RBFN and the weights in the output layer are determined by using linear optimization. The proposed network can be adapted with changing operating scenario of the power system. A network pruning strategy is used to limit the growth of the network size due to adaptive training. The proposed scheme is applied on the New England 39-bus power system model.     

考虑电压稳定的自动电压闭环控制

结合福建实际电网,提出了考虑电压稳定的自动电压优化控制方法,特定义了电压稳定约束电压下限和发电机动态无功储备的概念,将这两个量作为约束发送给AVC系统,在保证一定电压稳定裕度的情况下实现对全网的自动电压控制,即实现电压稳定分析和AVC系统的闭环控制.通过实际应用表明,所提出的方法较好地平衡了系统的经济性和安全性,实用化处理后控制设备动作合理,能够在保证一定的电压稳定裕度的基础上有效地降低系统网损,提高电压质量,在福建省网中取得了成功的应用.