Determination of TTC and application of SVM for demarcation of stability limits
01 Sep 2016-pp 1-5
TL;DR: In this paper, the authors aim at computing the stability limits of the power system network employed for the computation of Total Transfer Capability (TTC) using Support Vector Machine (SVM).
Abstract: The increasing demand for electrical power necessitates the expansion of power system, which is constrained by land availability and other resources. This results in the utilization of power system upto its stability limits. The TTC for an instance gives us the load that can be further supplied by the system before it loses stability. This paper aims at computing the stability limits of the power system network employed for the computation of Total Transfer Capability (TTC) using Support Vector Machine (SVM). Computation of voltage stability (using voltage stability index method and P-Q plane method) has been considered on IEEE 30 bus system. Small signal stability limit (Eigen value approach) has been considered on an WSCC 3 machine 9 bus system for which TTC has been calculated by employing SVM. All simulations are carried out in MATLAB 8.0-R 2012 b environment.
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TL;DR: An online measurement-based TTC estimator using the nonparametric analytics to take into account the uncertainties of the day-ahead generation scheduling and to reduce the number of redundant or infeasible data is proposed.
Abstract: Total transfer capability (TTC) is an effective indicator to evaluate the transmission limit of the interconnected systems. However, due to the large-scale wind power integration, operation conditions of a power system may change rapidly, yielding time-varying characteristics of the TTC. As a result, the traditional time-consuming transient stability constrained TTC model is unable to assess the online transmission margin. In this paper, we propose an online measurement-based TTC estimator using the nonparametric analytics. It consists of three major components: the probabilistic data generation, the composite feature selection, and the group Lasso regression-based training scheme. Specifically, we present a probabilistic data generation approach to take into account the uncertainties of the day-ahead generation scheduling and to reduce the number of redundant or infeasible data. Then, the composite feature selection is used to reduce the dimension of the generated data and identify the features which are highly correlated with TTC. The features are determined by the maximal information coefficients and nonparametric independence screening approach. Finally, these selected features are trained by the group Lasso regression to learn the correlation between the TTC and the online measurements. Once real-time measurements are available, the TTC can be assessed immediately through the learned correlation relationship. Extensive numerical results carried out on the modified New England 39-bus test system demonstrate the feasibility of the proposed TTC estimator for online applications.
36 citations
Cites background or methods from "Determination of TTC and applicatio..."
...the transfer capability of the studied systems [21]....
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...Comprehensive comparison results with existing approaches, such as multi-linear regression method, BP Neural Network and SVM, demonstrate its efficiency and effectiveness....
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...In addition, as shown in Section V-C, our proposed approach is able to provide the nonlinear numerical correlation between TTC and each feature subset, which is not the case for the multi-linear regression, the BP Neural Network and the SVMbased approaches; iii) the MIC is used together with the NIS to help select the most effective features for the enhanced TTC estimation....
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...Akhil et al. employed support vector machine (SVM) to demarcate the stability limits that restrict the transfer capability of the studied systems [21]....
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...[21] A. Prasad et al., “Determination of TTC and application of SVM for demarcation of stability limits,” in Proc....
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References
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01 Jan 1994
TL;DR: In this article, the authors present a model for the power system stability problem in modern power systems based on Synchronous Machine Theory and Modelling, and a model representation of the synchronous machine representation in stability studies.
Abstract: Part I: Characteristics of Modern Power Systems. Introduction to the Power System Stability Problem. Part II: Synchronous Machine Theory and Modelling. Synchronous Machine Parameters. Synchronous Machine Representation in Stability Studies. AC Transmission. Power System Loads. Excitation in Stability Studies. Prime Mover and Energy Supply Systems. High-Voltage Direct-Current Transmission. Control of Active Power and Reactive Power. Part III: Small Signal Stability. Transient Stability. Voltage Stability. Subsynchronous Machine Representation in Stability Studies. AC Transmission. Power System Loads. Excitation in Stability Studies. Prime Mover and Energy Supply Systems, High-Voltage Direct-Current Transmission. Control of Active Power and Reactive Power. Part III: Small Signal Stability. Transient Stability. Voltage Stability. Subsynchronous Oscillations. Mid-Term and Long-Term Stability. Methods of Improving System Stability.
13,467 citations
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01 Jan 2010
TL;DR: Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together.
Abstract: For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely upto-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists. Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.
4,943 citations
"Determination of TTC and applicatio..." refers background or methods in this paper
...The optimization problem as specified in [8] is as follows: Cost function : 12 + =1 (12) Constraints : ( + ) 1 (13) 0 (14) for all i =1,2,....
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...Lagrangian multiplier method is used by framing the quadratic optimization problem as given in [8]....
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01 Jan 1994
TL;DR: In this paper, the authors present a model for estimating the Impedance of Transmission Lines and the Capacitance of Transformer Lines in the presence of Symmetrical Faults.
Abstract: 1 Basic Concepts 2 Transformers 3 The Synchronous Machine 4 Series Impedance of Transmission Lines 5 Capacitance of Transmission Lines 6 Current and Voltage Relations on a Transmission Line 7 The Admittance Model and Network Calculations 8 The Impedance Model and Network Calculations 9 Power Flow Solutions 10 Symmetrical Faults 11 Symmetrical Components and Sequence Networks 12 Unsymmetrical Faults 13 Economic Operation of Power Systems 14 Zbus Methods in Contingency Analysis 15 State Estimation of Power Systems 16 Power System Stability
2,157 citations
Book•
30 Jul 1997TL;DR: This paper presents a meta-modelling procedure called Multimachine Dynamic Models for Energy Function Methods, which automates the very labor-intensive and therefore time-heavy and expensive process of Synchronous Machine Modeling.
Abstract: 1 Introduction 2 Electromagnetic Transients 3 Synchronous Machine Modeling 4 Synchronous Machine Control Models 5 Single-Machine Dynamic Models 6 Multimachine Dynamic Models 7 Multimachine Simulation 8 Small-Signal Stability 9 Energy Function Methods Appendix A: Integral Manifolds for Model Bibliography Index
2,004 citations
TL;DR: In this article, a method for the online testing of a power system is proposed which is aimed at the detection of voltage instabilities, and an indicator L is defined which varies in the range between 0 (noload of system) and 1 (voltage collapse).
Abstract: A method for the online testing a power system is proposed which is aimed at the detection of voltage instabilities. Thereby an indicator L is defined which varies in the range between 0 (noload of system) and 1 (voltage collapse). Based on the basic concept of such an indicator various models are derived which allow to predict a voltage instability or the proximity of a collapse. The indicator uses information of a normal load flow. The advantage of the method lies in the simplicity of the numerical calculation and the expressiveness of the result.
1,012 citations