A Learning Framework for Size and Type Independent Transient Stability Prediction of Power System Using Twin Convolutional Support Vector Machine

Real-time transient stability assessment (TSA) of power systems is an important real world problem in electrical energy engineering and pattern recognition scope. The definition of most discriminative trajectory features and proper supervised trajectory-based classifier has remained a motivational challenge for scholars vis-à-vis real-time TSA. In addition, increase in the consumption of electrical energy along with constraints such as amortization of network equipment induces electric power system inadequacy risk. The retrieval of power system adequacy involves network expansion planning such as installing new power plants for the network. This policy affects the structure and electrical specification of the network significantly. Furthermore, due to sudden or the scheduled tripping of network equipment stemming from action of protection devices or maintenance procedures, the network must undergo shallow structural changes. The different level of changes in network specification is becoming a potential barrier for network analysis tools like real-time TSA platform. In fact, the lack of consideration of the incompatibility of TSA tool with expansion planning affects the performance of TSA learning model that is trained using the pre-expansion network. However, this paradoxical problem can be solved by generalized learning for power system size & type independent (PSs&tInd) real-time TSA. For this purpose, first, we used a set of PSs&tInd trajectory features. Next, we presented a trajectory-based deep neuro classifier to eliminate kernel functions weaknesses plugged into the hyperplane-based classifier. Finally, experimental comparisons were conducted to assess the efficacy of the proposed framework. The results showed that the proposed technique offered high-generalization capacity on real-time TSA during network expansion.

[1]  Meinard Müller,et al.  Information retrieval for music and motion , 2007 .

[2]  Mohammad Shahidehpour,et al.  The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee , 1999 .

[3]  Jovica V. Milanovic,et al.  Online Identification of Power System Dynamic Signature Using PMU Measurements and Data Mining , 2016, IEEE Transactions on Power Systems.

[4]  Carson W. Taylor,et al.  Definition and Classification of Power System Stability , 2004 .

[5]  Hui Deng,et al.  Real-time transient instability detection based on perturbed voltage trajectories , 2015 .

[6]  Yoshua Bengio,et al.  Object Recognition with Gradient-Based Learning , 1999, Shape, Contour and Grouping in Computer Vision.

[7]  David J. Hill,et al.  Delay Aware Intelligent Transient Stability Assessment System , 2017, IEEE Access.

[8]  Bing-Yu Sun,et al.  A Study on the Dynamic Time Warping in Kernel Machines , 2007, 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System.

[9]  Claudio Moraga,et al.  The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning , 1995, IWANN.

[10]  Innocent Kamwa,et al.  Development of New Predictors Based on the Concept of Center of Power for Transient and Dynamic Instability Detection , 2018, IEEE Transactions on Smart Grid.

[11]  Udaya Annakkage,et al.  Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements , 2011, 2011 IEEE Power and Energy Society General Meeting.

[12]  Jian Pei,et al.  Data Mining: Concepts and Techniques, 3rd edition , 2006 .

[13]  Azza Ahmed Eldesouky,et al.  Power System Transient Stability: An Algorithm for Assessment and Enhancement Based on Catastrophe Theory and FACTS Devices , 2018, IEEE Access.

[14]  A. C. Zambroni de Souza,et al.  Energy function applied to voltage stability studies – Discussion on low voltage solutions with the help of tangent vector , 2016 .

[15]  Manoj Fozdar,et al.  Real-Time Monitoring of Post-Fault Scenario for Determining Generator Coherency and Transient Stability Through ANN , 2018, IEEE Transactions on Industry Applications.

[16]  Pratyasa Bhui,et al.  Real-Time Prediction and Control of Transient Stability Using Transient Energy Function , 2017, IEEE Transactions on Power Systems.

[17]  Jun Li,et al.  A novel real-time transient stability prediction method based on post-disturbance voltage trajectories , 2011, 2011 International Conference on Advanced Power System Automation and Protection.

[18]  Shigeki Sagayama,et al.  Support vector machine with dynamic time-alignment kernel for speech recognition , 2001, INTERSPEECH.

[19]  Ke Wang,et al.  Transient stability assessment of power system using support vector machine with generator combinatorial trajectories inputs , 2013 .

[20]  Zhen Yang,et al.  Application of EOS-ELM With Binary Jaya-Based Feature Selection to Real-Time Transient Stability Assessment Using PMU Data , 2017, IEEE Access.

[21]  Claudio A. Canizares,et al.  Benchmark systems for small signal stability analysis and control , 2015 .

[22]  P. Kundur,et al.  Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions , 2004, IEEE Transactions on Power Systems.

[23]  Junliang Liu,et al.  Convolutional neural networks for time series classification , 2017 .

[24]  Yu-Jen Lin Comparison of CART- and MLP-based power system transient stability preventive control , 2013 .

[25]  M.A. El-Sharkawi,et al.  Support vector machines for transient stability analysis of large-scale power systems , 2004, IEEE Transactions on Power Systems.

[26]  Sylvie Gibet,et al.  On Recursive Edit Distance Kernels With Application to Time Series Classification , 2010, IEEE Transactions on Neural Networks and Learning Systems.

[27]  Reshma Khemchandani,et al.  Twin Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Peter Crossley,et al.  Rotor angle instability prediction using post-disturbance voltage trajectories , 2010, IEEE PES General Meeting.

[29]  Yongzhang Huang,et al.  Synchronous Motor-Generator Pair to Enhance Small Signal and Transient Stability of Power System With High Penetration of Renewable Energy , 2017, IEEE Access.

[30]  Bijaya Ketan Panigrahi,et al.  Transmission line fault detection and location using Wide Area Measurements , 2017 .

[31]  Norman Mariun,et al.  A Novel Implementation for Generator Rotor Angle Stability Prediction Using an Adaptive Artificial Neural Network Application for Dynamic Security Assessment , 2013, IEEE Transactions on Power Systems.

[32]  P. Kundur,et al.  Power system stability and control , 1994 .

[33]  Mu-Chun Su,et al.  Application of a novel fuzzy neural network to real-time transient stability swings prediction based on synchronized phasor measurements , 1999 .

[34]  S. P. Singh,et al.  Voltage stability evaluation of power system with FACTS devices using fuzzy neural network , 2007, Eng. Appl. Artif. Intell..