Online Voltage Stability Research Articles

New online voltage stability margins and risk assessment for multi-bus smart power grids

This paper presents a quantitative framework for assessment of voltage stability in smart power networks. First, new stability indices similar to gain and phase margins in linear time invariant control systems are introduced. These indices can be computed online in conjunction with load-flow calculations. Then, a novel risk assessment framework incorporating the new stability indices is developed to methodologically quantify the voltage stability risks a power system faces at any given operating condition. In contrast to existing local stability indices and qualitative risk approaches, the indices and framework introduced provide analytical and quantitative evaluation of voltage stability and associated risks. The results are illustrated with a numerical example.

Special issue on on-line voltage stability assessments and enhancement

International Transactions on Electrical Energy SystemsVolume 24, Issue 11 p. 1521-1523 Editorial Special issue on on-line voltage stability assessments and enhancement First published: 20 November 2014 https://doi.org/10.1002/etep.2029Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Citing Literature Volume24, Issue11Special Issue: On‐line Voltage Stability Assessments and EnhancementNovember 2014Pages 1521-1523 RelatedInformation

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.

Voltage Track Prediction Based on WAMS

In order to give online voltage monitoring and stability margin judgment, a radial basis function (RBF) is used in this paper to predict the node voltage amplitude. Using BPA build IEEE9 network and input the processed data to RBF neural network, compared the results with the actual voltage amplitude, got a accurate prediction result. So as to stand out the accuracy of RBF, compared the relative error of prediction results between RBF and the second exponential smoothing model (SES). It testified the accuracy of RBF was more superior.

Wavelet based feature extraction of voltage profile for online voltage stability assessment using RBF neural network

Online voltage stability assessment is one of the vital requirements for intricate electric power systems. Due to the restructuring and liberalization, modern power systems tend to operate close to their stability limits with small security margin. In such environment, online voltage stability evaluation plays a significant role in secure operation of power systems. This paper presents a new approach for estimating voltage stability margin VSM, based on application of wavelet feature extraction method to network voltage profile. Voltage profile is adopted as the original input data for VSM estimation, because it contains sufficient information concerning network topology, load level, load-generation patterns and all system controllers. In this approach, in order to provide high discrimination between network voltage profiles, Multi-Resolution Wavelet Transform (MRWT) is utilized to extract the features of voltage profiles. Also, in order to eliminate the redundant features, principle component analysis (PCA) is used to select the most relevant features extracted by MRWT. Radial Basis Function (RBF) neural network is adopted to estimate system VSM using the dominant extracted features of the voltage profile by MRWT and PCA. Using voltage profile as the original data makes the proposed approach capable of estimating system VSM in both static and quasi dynamic conditions. The proposed approach has been implemented in New England 39-bus test system with promising results demonstrating its effectiveness and applicability.

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.

The FrameWork of Voltage Stability in Multi-Infeed AC/DC Power System

This paper first introduces the concepts( SCR, ESCR, QESCR ,H, and VSF.) and their usage. Later MIIF, equivalent admittance, MISCR and MIESCR, PBR are detailedly expounded. This paper put forward to the steps of the primary steady voltage stability work , detailed scheme of the steady voltage stability, the studing methods of the transient voltage stability in the multi-infeed AC/DC system. At last, the online voltage stability and the virtual smart power grid were put forward.

Call for papers: special issue on online voltage stability assessments and enhancement

Call for papers: special issue on online voltage stability assessments and enhancement

Fast Prediction of Voltage Stability Index Based on Radial Basis Function Neural Network: Iraqi Super Grid Network, 400-kV

With the increase in power demand and limited power sources has caused the system to operate at its maximum capacity. Therefore, the ability of determine voltage stability before voltage collapse has received a great attention due to the complexity of power system. In this paper a prediction of voltage stability index (VSI) based on radial basis function neural network (RBFNN) for the Iraqi Super Grid network, 400KV. Learning data has been obtained for various settings of load variables using load flow and conventional FVSI method. The input data was performed by using a 135 samples test with different bus voltage (Vb), Bus active and reactive power (Pb, Qb), bus load angle (?b) and FVSIij. The RBFNN model has four input representing the (Vb, Pb, Qb and ?b), sixteen nodes at hidden layer and one output node representing FVSIij have been used to assess the security on line. The proposed method has been tested in the IEEE 30 and a practical system. In Simulation results show that the proposed method is more suitable for on-line voltage stability assessment in term of automatically detection of critical transmission line when additional real or reactive loads are added.

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.

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.

A Fast Algorithm for Identification and Tracing of Voltage and Oscillatory Stability Margin Boundaries

This paper presents a framework based on a differential manifold approach that combines identification and tracing of both saddle node and Hopf bifurcation margin boundaries without calculating any eigenvalues. For a given base case, we first identify either the saddle node or Hopf bifurcation. The Hopf bifurcation is easily detected by observing the sign change of scalar index in the tangent vector without eigenvalue calculation. Based on manifold and bifurcation theory, a unified formulation for a variety of bifurcation related voltage and oscillatory stability margin boundary tracing in multiparameter space is proposed. The bifurcation-related margin boundary could be traced along any control scenario in multicontrol parameter space combined with any given loading scenario. This is achieved by moving from one boundary point to the next without retracing the entire PV curve. This paves the way for online voltage and oscillatory stability assessment. The unified boundary predictor-corrector-identifier tracing framework is originally employed to trace both voltage collapse and oscillatory stability margin boundaries, which are limited by the saddle node and Hopf bifurcation, respectively. The manifold-based methodologies presented in this paper facilitate the development of fast margin monitoring and control algorithms.

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.

Voltage Stability Control Based on VIPI Including a NAS Battery System

In recent years, the majority of voltage control schemes are local controls on each bus. However there are a few on-line voltage stability control schemes that consider the whole system. Hence, we propose on-line voltage stability control schemes in addition to conventional local controls (Tanaka, et al., 1998; Lobato, et al., 2000; Feng, et al., 1999; Wang, et al., 1998; Tran-Quoc, et al., 2000). In this paper, we regard an NAS battery system as control equipment and we propose a voltage stability preventive control method based on the VIPI using the NAS battery system. Simulations have been run to verify the effectiveness of the proposed method. The results showed that the proposed methods coped well with severe faults.

Voltage stability preventive and emergency‐preventive control using VIPI sensitivity

AbstractRecently, power systems have become larger and more complicated, and the rate of occurrence of constant power loads has increased. Under a situation like this, concerns about voltage instability phenomena have been raised. Therefore, in addition to the conventional local voltage control, the necessity of a global on‐line voltage control scheme has risen.In this paper, we propose a method of on‐line voltage stability control including preventive and emergency‐preventive controls to retain voltage stability even if a severe fault occurs. According to the result of contingency analysis for severe faults, we consider operation of control equipment as preventive control. And in the case that the fault seems severe still after preventive control, we consider load curtailment as emergency‐preventive control. When we select the control equipment and loads to curtail, we use voltage stability index VIPI sensitivity. And we determine the amount of control and load curtailment using Optimal Power Flow calculation. The effectiveness of the proposed method is verified by simulations using a 28‐bus system. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 143(4): 22–30, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10128

A future application environment for BC Hydro's EMS

The paper reports on the results of a tailored collaboration project sponsored by BC Hydro and EPRI to review the state-of-the-art in EMS architectures addressing the necessary attributes of a modern EMS with respect to scalability, database and user interface openness, flexibility and conformance to industry standards, as well as to provide a migration path for the existing BC Hydro's EMS. This review was intended to assist BC Hydro in identifying functionality and standard requirements of future EMS procurements and a migration path for a future BC Hydro application program environment. The proposed migration path was prototyped to verify its prudence and applicability in providing an open architecture environment for EMS applications. The BC Hydro On-Line Voltage Stability Analysis (OLVSA) application developed by BC Hydro and Powertech Labs was chosen and integrated into the prototype.