Support Vector Machine-Based Algorithm for Post-Fault Transient Stability Status Prediction Using Synchronized Measurements
TL;DR: In this paper, the transient stability status of a power system following a large disturbance such as a fault can be early predicted based on the measured post-fault values of the generator voltages, speeds, or rotor angles.
Abstract: The paper first shows that the transient stability status of a power system following a large disturbance such as a fault can be early predicted based on the measured post-fault values of the generator voltages, speeds, or rotor angles. Synchronously sampled values provided by phasor measurement units (PMUs) of the generator voltages, frequencies, or rotor angles collected immediately after clearing a fault are used as inputs to a support vector machines (SVM) classifier which predicts the transient stability status. Studies with the New England 39-bus test system and the Venezuelan power network indicated that faster and more accurate predictions can be made by using the post-fault recovery voltage magnitude measurements as inputs. The accuracy and robustness of the transient stability prediction algorithm with the voltage magnitude measurements was extensively tested under both balanced and unbalanced fault conditions, as well as under different operating conditions, presence of measurement errors, voltage sensitive loads, and changes in the network topology. During the various tests carried out using the New England 39-bus test system, the proposed algorithm could always predict when the power system is approaching a transient instability with over 95% success rate.
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TL;DR: In this paper, the authors present an energy fundiment analysis for power system stability, focusing on the reliability of the power system and its reliability in terms of power system performance and reliability.
Abstract: (1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.
1,080 citations
TL;DR: This bibliography will aid academic researchers and practicing engineers in adopting appropriate topics and will stimulate utilities toward development and implementation of software packages.
Abstract: Phasor measurement units (PMUs) are rapidly being deployed in electric power networks across the globe. Wide-area measurement system (WAMS), which builds upon PMUs and fast communication links, is consequently emerging as an advanced monitoring and control infrastructure. Rapid adaptation of such devices and technologies has led the researchers to investigate multitude of challenges and pursue opportunities in synchrophasor measurement technology, PMU structural design, PMU placement, miscellaneous applications of PMU from local perspectives, and various WAMS functionalities from the system perspective. Relevant research articles appeared in the IEEE and IET publications from 1983 through 2014 are rigorously surveyed in this paper to represent a panorama of research progress lines. This bibliography will aid academic researchers and practicing engineers in adopting appropriate topics and will stimulate utilities toward development and implementation of software packages.
239 citations
Cites methods from "Support Vector Machine-Based Algori..."
...Moreover, similar efforts were conducted based on the largest windowed Lyapunov exponent index [344], [345], clustering estimation integration along with a reduced-order modeling [344], [346], decoupled PQ integration [347], artificial neural networks (ANN) [348], fuzzy neural networks [349], ball-on-concave-surface mechanics system [288], support vector machine classifier [350], [351], unscented Kalman filter [351], and derivative of total angle separation index [277]....
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TL;DR: In this paper, the authors developed a transient stability assessment system based on the long short-term memory network by proposing a temporal self-adaptive scheme, which aims to balance the trade-off between assessment accuracy and response time.
Abstract: Online identification of postcontingency transient stability is essential in power system control, as it facilitates the grid operator to decide and coordinate system failure correction control actions Utilizing machine learning methods with synchrophasor measurements for transient stability assessment has received much attention recently with the gradual deployment of wide-area protection and control systems In this paper, we develop a transient stability assessment system based on the long short-term memory network By proposing a temporal self-adaptive scheme, our proposed system aims to balance the trade-off between assessment accuracy and response time, both of which may be crucial in real-world scenarios Compared with previous work, the most significant enhancement is that our system learns from the temporal data dependencies of the input data, which contributes to better assessment accuracy In addition, the model structure of our system is relatively less complex, speeding up the model training process Case studies on three power systems demonstrate the efficacy of the proposed transient stability as sessment system
203 citations
TL;DR: The extensive test results indicate that the proposed intelligent differential relaying scheme can be highly reliable in providing an effective protection measure for safe and secured microgrid operation.
Abstract: This paper presents a data-mining-based intelligent differential protection scheme for the microgrid. The proposed scheme preprocesses the faulted current and voltage signals using discrete Fourier transform and estimates the most affected sensitive features at both ends of the respective feeder. Furthermore, differential features are computed from the corresponding features at both ends of the feeder and are used to build the decision tree-based data-mining model for registering the final relaying decision. The proposed scheme is extensively validated for fault situations in the standard IEC microgrid model with wide variations in operating parameters for radial and mesh topology in grid-connected and islanded modes of operation. The extensive test results indicate that the proposed intelligent differential relaying scheme can be highly reliable in providing an effective protection measure for safe and secured microgrid operation.
201 citations
TL;DR: A robust scheme is proposed based on adaptive ensemble decision tree (DT) learning that is first illustrated on the IEEE 39-bus test system, and then applied to a regional grid of the Western Electricity Coordinating Council (WECC) system.
Abstract: Online dynamic security assessment (DSA) is examined in a data-mining framework by taking into account the operating condition (OC) variations and possible topology changes of power systems during the operating horizon. Specifically, a robust scheme is proposed based on adaptive ensemble decision tree (DT) learning. In offline training, a boosting algorithm is employed to build a classification model as a weighted voting of multiple unpruned small-height DTs. Then, the small-height DTs are periodically updated by incorporating new training cases that account for OC variations or the possible changes of system topology; the voting weights of the small-height DTs are also updated accordingly. In online DSA, the updated classification model is used to map the real-time measurements of the present OC to security classification decisions. The proposed scheme is first illustrated on the IEEE 39-bus test system, and then applied to a regional grid of the Western Electricity Coordinating Council (WECC) system. The results of case studies, using a variety of realized OCs, illustrate the effectiveness of the proposed scheme in dealing with OC variation and system topology change.
175 citations
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TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Abstract: The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.
15,696 citations
Book•
01 Mar 2000
TL;DR: This book is the first comprehensive introduction to Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory, and introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods.
Abstract: This book is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. The book also introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc. Their first introduction in the early 1990s lead to a recent explosion of applications and deepening theoretical analysis, that has now established Support Vector Machines along with neural networks as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and application of these techniques. The concepts are introduced gradually in accessible and self-contained stages, though in each stage the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally the book will equip the practitioner to apply the techniques and an associated web site will provide pointers to updated literature, new applications, and on-line software.
4,327 citations
Book•
30 Apr 1980TL;DR: In this paper, the authors present a mathematical model of the Synchronous Machine and the effect of speed and acceleration on the stability of a three-phase power system with constant impedance load.
Abstract: Preface.Part I: Introduction.Chapter 1: Power System Stability.Chapter 2: The Elementary Mathematical Model.Chapter 3: System Response to Small Disturbances.Part II: The Electromagnetic Torque.Chapter 4: The Synchronous Machine.Chapter 5: The Simulation of Synchronous Machines.Chapter 6: Linear Models of the Synchronous Machine.Chapter 7: Excitation Systems.Chapter 8: Effect of Excitation on Stability.Chapter 9: Multimachine Systems with Constant Impedance Loads.Part III: The Mechanical Torque Power System Control and Stability.Chapter 10: Speed Governing.Chapter 11: Steam Turbine Prime Movers.Chapter 12: Hydraulic Turbine Prime Movers.Chapter 13: Combustion Turbine and Combined-Cycle Power Plants.Appendix A: Trigonometric Identities for Three-Phase Systems.Appendix B: Some Computer Methods for Solving Differential Equations.Appendix C: Normalization.Appendix D: Typical System Data.Appendix E: Excitation Control System Definitions.Appendix F: Control System Components.Appendix G: Pressure Control Systems.Appendix H: The Governor Equations.Appendix I: Wave Equations for a Hydraulic Conduit.Appendix J: Hydraulic Servomotors.Index.
3,249 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