Power system security assessment for multiple contingencies using multiway decision tree

Oliveira, Werbeston Douglas de; Vieira, Joao Paulo Abreu; Bezerra, Ubiratan Holanda; Martins, Daniel; Rodrigues, Benedito das Graças Duarte

doi:10.1016/j.epsr.2017.03.029

Cited by 56 publications

(31 citation statements)

References 34 publications

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“…[139] uses a deep auto-encoder to extract features and proposes an objective-based loss function to learn the auto-encoder, in such a way that it minimises the misclassification of unstable observations. Multiway decision trees were exploited in [140] to determine if the system is secure or insecure while considering system topology. Furthermore, Stratified Random Sampling was used to obtain the same proportion of secure and insecure labels.…”

Section: A Prediction Of Power Flowsmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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Section: A Prediction Of Power Flowsmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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“…One is the artificial neural network (ANN) algorithm [22][23][24]. In Oliveira et al [25]. In Sunitha et al [16], the static security index is predicted by adopting the ANN algorithm for contingency screening and ranking.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although the ANN algorithm has been employed in the literature, it is inappropriate for large-scale data modules, and the internal mechanism is difficult to understand. Another is the decision tree (DT) algorithm [25,26]. In Oliveira et al [25], the static security assessment applies machine learning techniques, which are based on decision tree algorithms, to improve the efficiency of contingency screening and ranking.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Another is the decision tree (DT) algorithm [25,26]. In Oliveira et al [25], the static security assessment applies machine learning techniques, which are based on decision tree algorithms, to improve the efficiency of contingency screening and ranking. In Saeh et al [26], a modified decision tree algorithm is employed for static security assessment.…”

Section: Literature Reviewmentioning

confidence: 99%

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Online Static Security Assessment of Power Systems Based on Lasso Algorithm

Sun

2018

Applied Sciences

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As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton–Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.

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“…Therefore, at the test time, the task is to predict all these output variables from one input instance. An approach based on multiway decision tree was used to assess power system operation security for multiple contingencies in [7]. Power system security assessment with multiclass classification was performed using multiclass support vector machine in [8].…”

Section: Introductionmentioning

confidence: 99%

Multilabel learning for the online transient stability assessment of electric power systems

Beyranvand¹,

Genç²,

Çataltepe³

2018

Turk J Elec Eng & Comp Sci

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Dynamic security assessment of a large power system operating over a wide range of conditions requires an intensive computation for evaluating the system's transient stability against a large number of contingencies. In this study, we investigate the application of multilabel learning for improving training and prediction time, along with the prediction accuracy, of neural networks for online transient stability assessment of power systems. We introduce a new multilabel learning method, which uses a contingency clustering step to learn similar contingencies together in the same multilabel multilayer perceptron. Experimental results on two different power systems demonstrate improved accuracy, as well as significant reduction in both training and testing time.

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Power system security assessment for multiple contingencies using multiway decision tree

Cited by 56 publications

References 34 publications

Recent Developments in Machine Learning for Energy Systems Reliability Management

Recent Developments in Machine Learning for Energy Systems Reliability Management

Online Static Security Assessment of Power Systems Based on Lasso Algorithm

Multilabel learning for the online transient stability assessment of electric power systems

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