Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements

Gomez, F. R.; Rajapakse, Athula D.; Annakkage, U.D.; Fernando, I.T.

doi:10.1109/pes.2011.6038936

Cited by 60 publications

(107 citation statements)

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“…There exist two basic types of feature selection, i.e. time-series synchronized data such as a few cycles of voltage trajectory [25] and dynamic performance indices such as kinetic energy indicator of rotors [11].…”

Section: Features Selectionmentioning

confidence: 99%

“…Most of the existing works are focused on the binary state prediction for global stability using clustering and classification. For example, support vector machine, decision tree and artificial neural network (ANN) are widely used to detect instability of power systems by using post-fault dynamic data during a few cycles [11][12][13]. Guo and Milanović presented a probabilistic framework to evaluate the accuracy of data mining tools applied for online prediction of transient stability [14], enabling the comprehensive analysis of performance of different implementations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High-performance predictor for critical unstable generators based on scalable parallelized neural networks

Liu¹,

Yang²,

et al. 2016

J. Mod. Power Syst. Clean Energy

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A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.

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Section: Features Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-performance predictor for critical unstable generators based on scalable parallelized neural networks

Liu¹,

Yang²,

et al. 2016

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

show abstract

“…Further, the assessment accuracy obtained using the direct method of stability assessment using simplified power system models along with simple methods of critical energy computation (Potential Energy Boundary Surface (PEBS) [14]) is insufficient for DSA The applicability of machine learning for transient stability assessment has been investigated in literature for DSA and for post disturbance transient stability assessment and has shown promising results in recent literature [15][16][17][18][19][20][21]. The approaches presented in [15][16][17][18][19][20] use databases generated off-line aiming to cover all possible operating conditions and topology changes. However, it is challenging to determine if an initially trained network is valid to assess the stability of a particular system operating state once the system is evolved to a different operating state or a different network topology.…”

Section: Motivationmentioning

confidence: 99%

Application of energy-based power system features for dynamic security assessment

Geeganage

Annakkage

Weekes

et al. 2015

2015 IEEE Power &Amp; Energy Society General Meeting

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To date, the potential of on-line Dynamic Security Assessment (DSA)-to monitor, alert, and enhance system security-is constrained by the longer computational cycle time. Traditional techniques requiring extensive numerical computations makes it challenging to complete the assessment within an acceptable time. Longer computational cycles produce obsolete security assessment results as the system operating point evolves continuously. This thesis presents a DSA algorithm, based on Transient Energy Function (TEF) method and machine learning, to enable frequent computational cycles in on-line DSA of power systems.The use of selected terms of the TEF as pre-processed input features for machine learning demonstrated the ability to successfully train a contingency-independent classifier that is capable of classifying stable and unstable operating points. The network is trained for current system topology and loading conditions. The classifier can be trained using a small dataset when the TEF terms are used as input features. The prediction accuracy of the proposed scheme was tested under the balanced and unbalanced faults with the presence of voltage sensitive and dynamic loads for different operating points. The test results demonstrate the potential of using the proposed technique for power system on-line DSA. Power system devices such as HVDC and FACTS can be included in the algorithm by incorporating the effective terms of a corresponding TEF.An on-line DSA system requires the integration of several functional components.Some of these components already exist and the others are newly introduced to the i power system. The practicality of the proposed technique in terms of a) critical data communications aspects b) computational hardware requirements; and c) capabilities and limitations of the tools in use was tested using an implementation of an on-line DSA system. The test power system model was simulated using a real-time digital simulator. The other functional units were distributed over the Local Area Network (LAN). The implementation indicated that an acceptable computational cycle time can be achieved using the proposed method.In addition, the work carried out during this thesis has produced two tools that can be used for a) web-based automated data generation for power system studies; and b) testing of on-line DSA algorithms.ii

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“…In geosceinces, SVM have been applied to remote sensing analysis [27][28][29], land cover change [30][31][32], landslide susceptibility [33][34][35][36] and hydrology [37,38]. In power systems, SVM was used for transient status prediction [39], power load forecasting [40], electricity consumption prediction [41] and wind power forecasting [42]. Stock price forecasting [43][44][45] and business administration [46] can also use SVM.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Support Vector Machines with Uncertainties

Wang

Pardalos

2014

Ann. Data. Sci.

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Support Vector Machines (SVM) is one of the well known supervised classes of learning algorithms. SVM have wide applications to many fields in recent years and also many algorithmic and modeling variations. Basic SVM models are dealing with the situation where the exact values of the data points are known. This paper presents a survey of SVM when the data points are uncertain. When a direct model cannot guarantee a generally good performance on the uncertainty set, robust optimization is introduced to deal with the worst case scenario and still guarantee an optimal performance. The data uncertainty could be an additive noise which is bounded by norm, where some efficient linear programming models are presented under certain conditions; or could be intervals with support and extremum values; or a more general case of polyhedral uncertainties with formulations presented. Another field of the uncertainty analysis is chance constrained SVM which is used to ensure the small probability of misclassification for the uncertain data. The multivariate Chebyshev inequality and Bernstein bounding schemes have been used to transform the chance constraints through robust optimization. The Chebyshev based model employs moment information of the uncertain training points. The Bernstein bounds can be less conservative than the Chebyshev bounds since it employs both support and moment information, but it also makes a strong assumption that all the elements in the data set are independent.

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Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements

Cited by 60 publications

References 0 publications

High-performance predictor for critical unstable generators based on scalable parallelized neural networks

High-performance predictor for critical unstable generators based on scalable parallelized neural networks

Application of energy-based power system features for dynamic security assessment

A Survey of Support Vector Machines with Uncertainties

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