Real-time transient stability assessment of electric power systems using predictive-SIME based on machine learning

Echeverría, Juan; Cepeda, Cátia; Colomé, D. G.

doi:10.1109/isgt-la.2017.8126760

Cited by 12 publications

(12 citation statements)

References 12 publications

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“…The database generation is then usually done offline, given the extensive simulation cost to build it, while the application of the resulting model trained on the dataset can be done offline or online, depending on the application and the context. [15], [16], [19], [22], [27]- [29], [32], [34], [35], [37], [38], [41], [43], [45], [47], [49]- [52], [54]- [56], [63], [64], [67]- [73], [75], [76], [81]- [83], [85]- [88], [90], [92], [93], [98], [102], [105], [107], [113], [115]- [117] Voltage stability [26], [30], [39], [40], [42], [44], [46], [48], [53],…”

Section: A Database Buildingmentioning

confidence: 99%

“…More classical algorithms are however still exploited in the literature. For instance, SVM methods are used for transient stability [50], [51], [86]- [88] or voltage stability [39], [89]. SVMs are easier to train than neural network models and they can be used to predict the stability status of the power system (stable or unstable) or a voltage stability margin index [89].…”

Section: Learning a Modelmentioning

confidence: 99%

“…SVMs are easier to train than neural network models and they can be used to predict the stability status of the power system (stable or unstable) or a voltage stability margin index [89]. The parameters of the SVM can be optimised with a grid search algorithm or particle swarm optimisation [88], [89]. In order to increase the accuracy of the assessment, in [87], two SVM models are used, an aggressive and a conservative one.…”

Section: Learning a Modelmentioning

confidence: 99%

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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 Database Buildingmentioning

confidence: 99%

Section: Learning a Modelmentioning

confidence: 99%

Section: Learning a Modelmentioning

confidence: 99%

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Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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“…C is selected from {0.1, 1, 10, 100, 1000, 10000}, and γ is selected from {0. 5 All baseline models are implemented by Python (V3.6). Table 3 shows the performance of different classifiers for the testing datasets of the 10-machine system, 16-machine system and 47-machine system, respectively.…”

Section: B Xgboost-fm Vs Other Kinds Of Classifiersmentioning

confidence: 99%

“…By contrast, the direct methods, such as transient energy function method [3] and extended equal-area criterion [4], can compute quickly and provide the stability margin, but the assessment results are conservative. Due to above drawbacks, these methods cannot simultaneously meet the accuracy and speed requirements for online TSA [5].…”

Section: Introductionmentioning

confidence: 99%

Transient Stability Assessment of Power System Based on XGBoost and Factorization Machine

Gao

2020

IEEE Access

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As the deployment of wide-area measurement systems (WAMS) expands, data-driven methods are playing an increasingly important role in transient stability assessment (TSA). However, if the measured data is disturbed by noise or the topological structure of the power system changes, the performance of the model based on data-mining would decline so that it could not meet the needs of real-world scenarios. In this paper, we develop a TSA methodology based on extreme gradient boosting (XGBoost) and factorization machine (FM). Utilizing XGBoost to complete automatically the feature construction and transform the power flow into a sparse matrix. The influence of noise can be reduce due to the sparsity of the new feature set. On this basis, FM algorithm, which has advantage in processing the large sparse matrix, is introduced into the model to complete fault classification. Furthermore, the feature crossing function of FM further mines the interactive information of the spatio-temporal features. For changed topology scenarios, we propose a extended-training set scheme. Adding some pivotal data of topology changes to the training set improves the robustness of the model. Compared with existing studies, the proposed assessment model based on XGBoost-FM not only has the better generalization performance in the case of noise interference or changed topology, but also has the least time consumption and complexity.

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Artificial intelligence techniques for dynamic security assessments - a survey

Cuevas,

Álvarez-Malebrán,

Rahmann

et al. 2024

Artif Intell Rev

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Real-time transient stability assessment of electric power systems using predictive-SIME based on machine learning

Cited by 12 publications

References 12 publications

Recent Developments in Machine Learning for Energy Systems Reliability Management

Recent Developments in Machine Learning for Energy Systems Reliability Management

Transient Stability Assessment of Power System Based on XGBoost and Factorization Machine

Artificial intelligence techniques for dynamic security assessments - a survey

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