Real-time transient stability assessment in power system based on improved SVM

Hu, Wei; Lu, Zongxiang; Wu, Shuang; Zhang, Weiling; Dong, Yu; Yu, Rui; Liu, Baisi

doi:10.1007/s40565-018-0453-x

Cited by 96 publications

(42 citation statements)

References 11 publications

(22 reference statements)

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“…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. This allows to predict a third class, called the grey region, when both models disagree, to indicate when the assessment is uncertain.…”

Section: Learning a Modelmentioning

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: Learning a Modelmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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“…They are not injective mappings, and some sub-models share the same parameters. By combining (7), (12) and (13), the advanced model is yielded.…”

Section: A Model Improvement Via Ensemble Learningmentioning

confidence: 99%

“…However, it highly depends on the system model and can only adopt simple models at present, which limits its further application. In recent years, data-driven methods have developed rapidly to deal with the above issues [12], [13]. These methods exploit massive simulation data and machine learning tools to construct assessment models, which describe the aforementioned mapping between pre-fault power flow or network topology and the CCT.…”

mentioning

confidence: 99%

Data-driven Transient Stability Assessment Model Considering Network Topology Changes via Mahalanobis Kernel Regression and Ensemble Learning

Liu

Zhang

Chen

et al. 2020

Journal of Modern Power Systems and Clean Energy

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Transient stability assessment (TSA) is of great importance in power system operation and control. One of the usual tasks in TSA is to estimate the critical clearing time (CCT) of a given fault under the given network topology and pre-fault power flow. Data-driven methods try to obtain models describing the mapping between these factors and the CCT from a large number of samples. However, the influence of network topology on CCT is hard to be analyzed and is often ignored, which makes the models inaccurate and unpractical. In this paper, a novel data-driven TSA model combining Mahalanobis kernel regression and ensemble learning is proposed to deal with the problem. The model is a weighted sum of several sub-models. Each sub-model only uses the data of one topology to construct a kernel regressor. The weights are determined by both the topological similarity and numerical similarity between the samples. The similarities are decided by the parameters in Mahalanobis distance, and the parameters are to be trained. To reduce the model complexity, sub-models within the same topology category share the same parameters. When estimating CCT, the model uses not only the sub-model which the sample topology belongs to, but also other sub-models. Thus, it avoids the problem that there may be too few data under some topologies. It also efficiently utilizes information of data under all the topologies. Moreover, its decision-making process is clear and understandable, and an effective training algorithm is also designed. Test results on both the IEEE 10-machine 39-bus and a real system verify the effectiveness of the proposed model.

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“…1) Support vector machine (SVM) [8]. It is a classical and fast method in the determination of system stability.…”

Section: Introductionmentioning

confidence: 99%

Data-driven Transient Stability Assessment Based on Kernel Regression and Distance Metric Learning

Liu

Min

Chen

et al. 2021

Journal of Modern Power Systems and Clean Energy

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Transient stability assessment (TSA) is of great importance in power systems. For a given contingency, one of the most widely used transient stability indices is the critical clearing time (CCT), which is a function of the pre-fault power flow. TSA can be regarded as the fitting of this function with the prefault power flow as the input and the CCT as the output. In this paper, a data-driven TSA model is proposed to estimate the CCT. The model is based on Mahalanobis-kernel regression, which employs the Mahalanobis distance in the kernel regression method to formulate a better regressor. A distance metric learning approach is developed to determine the problem-specific distance for TSA, which describes the dissimilarity between two power flow scenarios. The proposed model is more accurate compared to other data-driven methods, and its accuracy can be further improved by supplementing more training samples. Moreover, the model provides the probability density function of the CCT, and different estimations of CCT at different conservativeness levels. Test results verify the validity and the merits of the method.

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Real-time transient stability assessment in power system based on improved SVM

Cited by 96 publications

References 11 publications

Recent Developments in Machine Learning for Energy Systems Reliability Management

Recent Developments in Machine Learning for Energy Systems Reliability Management

Data-driven Transient Stability Assessment Model Considering Network Topology Changes via Mahalanobis Kernel Regression and Ensemble Learning

Data-driven Transient Stability Assessment Based on Kernel Regression and Distance Metric Learning

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