Online Voltage Stability Monitoring Research Articles

Extreme Learning Machine Approach for Real Time Voltage Stability Monitoring in a Smart Grid System using Synchronized Phasor Measurements

Online voltage stability monitoring using real-time measurements is one of the most important tasks in a smart grid system to maintain the grid stability. Loading margin is a good indicator for assessing the voltage stability level. This paper presents an Extreme Learning Machine (ELM) approach for estimation of voltage stability level under credible contingencies using real-time measurements from Phasor Measurement Units (PMUs). PMUs enable a much higher data sampling rate and provide synchronized measurements of real-time phasors of voltages and currents. Depth First (DF) algorithm is used for optimally placing the PMUs. To make the ELM approach applicable for a large scale power system problem, Mutual information (MI)-based feature selection is proposed to achieve the dimensionality reduction. MI-based feature selection reduces the number of network input features which reduces the network training time and improves the generalization capability. Voltage magnitudes and phase angles received from PMUs are fed as inputs to the ELM model. IEEE 30-bus test system is considered for demonstrating the effectiveness of the proposed methodology for estimating the voltage stability level under various loading conditions considering single line contingencies. Simulation results validate the suitability of the technique for fast and accurate online voltage stability assessment using PMU data.

Voltage Stability Assessment using Artificial Neural Networks

Objectives: To calculate the load flow analysis by using Artificial Neural Networks (ANN) and the Cascade Architecture (CC) with Levenberg-Marquardt (LM) algorithm is used for this proposed system. Methods/Statistical Analysis: Many conventional methods such as Newton-Raphson method, Gauss-Seidel method, AC load flow analysis etc., are used to estimate the load flow analysis of a power system. The major backdrops in using these methods are, using complex non-linear equations, iterative methods and time consuming. To overcome these problems, this paper discusses using Artificial Neural Networks (ANN) which reduces the time consumption in calculating load flow analysis. Findings: In the real-time planning and operation of a power system the major consideration is voltage stability assessment. The voltage instability in a power system will lead to a blackout condition. The continuous increase in load demand, changes in system conditions causes voltage collapse. So the on-line monitoring of voltage stability is a necessary condition. Application/Improvements: The output of the load flow analysis is used to calculate the Index that is used to maintain the system in stable limits.

Voltage Stability Assessment using Artificial Neural Networks

Objectives: To calculate the load flow analysis by using Artificial Neural Networks (ANN) and the Cascade Architecture (CC) with Levenberg-Marquardt (LM) algorithm is used for this proposed system. Methods/Statistical Analysis: Many conventional methods such as Newton-Raphson method, Gauss-Seidel method, AC load flow analysis etc., are used to estimate the load flow analysis of a power system. The major backdrops in using these methods are, using complex non-linear equations, iterative methods and time consuming. To overcome these problems, this paper discusses using Artificial Neural Networks (ANN) which reduces the time consumption in calculating load flow analysis. Findings: In the real-time planning and operation of a power system the major consideration is voltage stability assessment. The voltage instability in a power system will lead to a blackout condition. The continuous increase in load demand, changes in system conditions causes voltage collapse. So the on-line monitoring of voltage stability is a necessary condition. Application/Improvements: The output of the load flow analysis is used to calculate the Index that is used to maintain the system in stable limits.

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

Abstract 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.

Research on the Method of Calculating Node Injected Reactive Power Based on L Indicator

With the power grid load increasing, the problem of grid voltage stability is increasingly prominent, and the possibility of voltage instability is also growing. In order to improve the voltage stability, this paper analyzed how the voltage stability was influenced by different reactive power injection based on the simplified L-indicator of on-line voltage stability monitoring. According to the basic differential property of the simplified L-indicator, a general and normative analytical algorithm about reactive power optimization was deduced. The analytical algorithm can calculate the load node injected reactive power, and then the network can run in the optimal steady state on the basis of the calculation results. According to the simulation results of IEEE-14, IEEE-30, IEEE-57 and IEEE-118, the feasibility and effectiveness of the proposed algorithm to improve voltage stability and reduce the risk of grid collapse were verified.

Laboratory-Based Smart Power System, Part II: Control, Monitoring, and Protection

Wide area monitoring (WAM), wide area protection (WAP), and wide area control (WAC) systems will enhance the future of smart grid operation in terms of reliability and security. In part I of this paper, a proposed architecture for a hybrid ac/dc smart grid hardware test-bed system was presented. Design details of the various components and their connectivity in the overall system architecture were identified. In part II of the paper, the focus is on the design of monitoring, control, and protection systems and their integrated real-time operation. Various control scenarios for system startup and continuous operation are examined. We have developed a control system based on wide area measurements. The advanced measurement system based on synchrophasors was also implemented using DAQs real-time synchronous data. The developed system features a wide variety of capabilities such as online system parameters calculation and online voltage stability monitoring. These are implemented as an experimental case to enhance wide area monitoring systems. Moreover, the protection system was designed inside of the real-time software environment to monitor the real-time wide area data, and make a comprehensive and reliable coordination for the whole system. Ideas related to the interaction of a dc microgrid involving sustainable energy sources with the main ac grid have been also implemented and presented. The implemented system is explicit and achievable in any research laboratory and for real-time real-world smart grid applications.

Estimation of voltage stability index for power system employing artificial neural network technique and TCSC placement

This paper proposes a scheme for online voltage stability monitoring for various load conditions using feed forward back propagation network (FFBPN). A single FFBPN with minimal number of neurons is used to estimate the line voltage stability index for various load conditions. A sequential learning strategy is used to design the FFBPN and the weights in the output layer are determined by using linear optimization. The proposed network is applied on the IEEE 14-bus and the IEEE 30-bus power system and line stability indices are calculated for different loading conditions. Based on the calculated indices, the ranking of weakest lines is done. The optimal location for placement of Thyristor controlled series capacitor (TCSC) has been identified among the weakest lines for improving the voltage stability in the power system. The proposed network can be adapted with changing operating scenario of the power system.

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.

Voltage stability monitoring by artificial neural network using a regression-based feature selection method

This paper proposes a methodology for online voltage stability monitoring using artificial neural network (ANN) and a regression-based method of selecting features for training the ANN. Separate ANNs are used for different contingencies. Important features for the ANN are selected by using the sensitivities of the voltage stability margin with respect to the inputs. The sensitivities are computed by using the regression models, which overcomes many limitations of the conventional methods of computing sensitivities. The implementation of the feature selection scheme enhances the overall design of the neural network. The proposed scheme is applied on the New England 39-bus power system model.

Multicontingency voltage stability monitoring of a power system using an adaptive radial basis function network

This paper proposes a scheme for online voltage stability monitoring for multiple contingencies using an enhanced radial basis function network (RBFN). A single RBFN is used to estimate 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 and the IEEE 118-bus power system.

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.

An improved voltage stability index and its application

After some damaging blackouts, voltage stability and collapse have become worldwide concerned problems. In this paper, we focus on the aspect of voltage stability. An improved voltage stability index named L I is presented, which can give an accurate indication to power system voltage instability with considering the influence of the load model. Its calculating speed is very fast, so it can be used in the on-line monitoring and control of power system voltage stability. An appropriate external equivalent method is also presented to speed-up the calculation. So index- L I can be used in regional operating centers of the interconnection power systems. As an application of index- L I, approaches to identify voltage stability weak buses, coherent generator set and coherent load set of a weak bus are given in the end. New-England system and IEEE 50-generator-145-bus system are used to illustrate the accurateness and effectiveness of our methods.

Online voltage stability monitoring using var reserves

For many years, system operators have noticed that the VAr reserves of key generators can indicate the degree of controllability of key bus voltages in a system. This observation has led to the development of a few VAr reserve monitoring systems. In this paper, a rigorous investigation on the subject is conducted. Using the correlative relationship between generator VAr reserves and system voltage stability margins, a practical and systematic method for online voltage stability monitoring is proposed. The method has been thoroughly tested on two real-life power systems with promising results.

An improved method to approximate closest saddle node bifurcation based on multiple load flow solutions: A new method of voltage stability monitoring and control

Although a closest bifurcation point (CBP) is known to be a superior security measure against voltage collapse, no fast computation methods exist presently. In such a circumstance, the authors proposed an approximation method for CBP in Ref. 11 where two major problems were left unsolved: (1) the method cannot take into Q limits of generators and (2) it utilizes a low-voltage power flow solution, which is not easily obtained unless a suitable initial estimate is available. This paper presents a series of techniques to solve these problems. It is shown that the proposed method can provide fairly accurate CBP without suffering any major problems. The total computation time is not more than that of ten power flows even when no information of low-voltage solution is available in advance. The proposed method will make possible a reliable on-line monitoring and control of voltage stability. © 2000 Scripta Technica, Electr Eng Jpn, 133(1): 19–30, 2000

On-line voltage stability monitoring

This paper presents a new methodology for online voltage stability assessment, consisting of two steps. Firstly, the time evolution of the power system operating state is modeled with the help of a forecasting-aided state estimator; secondly the voltage collapse point is determined through an extrapolation technique based on tangent vector behavior. Tests are carried out using the IEEE-14 bus system, where different operating scenarios are considered. The results show that monitoring system load trends may improve the assessment of voltage stability.

電力潮流多根に基づく最近接サドルノード分岐点の近似手法

Although a closest bifurcation point (CBP) is known to be a superior security measure against voltage collapse, no fast computation methods exist presently. In such a circumstance, the authors have proposed an approximation method of CBP in(11) where two major problems are left unsolved. They are: (1) the method cannot take into Q limits of generators and (2) it utilizes a low volt-age power flow solution, which is not easily obtained unless a suitable initial estimate is available. This paper presents a series of techniques to solve these problems. It is shown that the proposed method can provide fairly accurate CBP without suffering from any major problems. The total computation time is not more than that of ten power flows even when no information of low voltage solution is available in advance. The proposed method will make it possible a reliable on-line monitoring and control of voltage stability.