A Multiple Randomized Learning based Ensemble Model for Power System Dynamic Security Assessment

Ren, Chao; Xu, Yan; Zhang, Yuchen; Hu, Chunchao

doi:10.1109/pesgm.2018.8585991

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…Ensemble learning algorithms can solve the same regression/classification problem by combining different ML units. With such a paradigm, the whole model can be more diverse, and its ensembling output would be more robust and more accurate, which has been demonstrated in [26–28].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…This paper designs a new hybrid ensemble model for real‐time FIDVR assessment. The hybrid model ensembles RVFL and ELM as the single ML units so as to construct the hybrid ensemble model for achieving probabilistic prediction, which can both improve the accuracy and supply the potential prediction and prediction error to make more reliable mechanism before failure [26, 27].…”

Section: Proposed Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid randomised learning‐based probabilistic data‐driven method for fault‐induced delayed voltage recovery assessment of power systems

Ren

Zhang²,

Zhang³

et al. 2020

IET Generation, Transmission & Distribution

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Hybrid randomised learning‐based probabilistic data‐driven method for fault‐induced delayed voltage recovery assessment of power systems

Ren

Zhang²,

Zhang³

et al. 2020

IET Generation, Transmission & Distribution

Self Cite

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“…It is fast to train, and thus can be easily updated during operation. In the literature, ensembles of ELMs are used mostly for stability classification [19], [29], [32], [35], [56], [75], [76], or combined with random vector functional links [31], [33], [77]. It was also used for regression, to predict load stability margins [78], the fault-induced voltage recovery [30], [79], and maximum frequency deviation and time [80].…”

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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“…In these, data is collected from time-domain simulations, sampled from a fraction of the space of expected system states, and used off-line to train a data model. Such models, with promising results from both deep learning (DL) [2], [3] and conventional ML models [4], [5], can then be used to rapidly predict the stability of other system states. In addition, Transfer Learning, a collection of methods for improving the performance of data models on data with different distributions or feature spaces, has recently gained attention in power system contexts.…”

Section: Introductionmentioning

confidence: 99%

Versatile and Robust Transient Stability Assessment via Instance Transfer Learning

Meghdadi¹,

Tack²,

Liebman³

et al. 2021

2021 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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To support N-1 pre-fault transient stability assessment, this paper introduces a new data collection method in a data-driven algorithm incorporating the knowledge of power system dynamics. The domain knowledge on how the disturbance effect will propagate from the fault location to the rest of the network is leveraged to recognise the dominant conditions that determine the stability of a system. Accordingly, we introduce a new concept called Fault-Affected Area, which provides crucial information regarding the unstable region of operation. This information is embedded in an augmented dataset to train an ensemble model using an instance transfer learning framework. The test results on the IEEE 39bus system verify that this model can accurately predict the stability of previously unseen operational scenarios while reducing the risk of false prediction of unstable instances compared to standard approaches.

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A Multiple Randomized Learning based Ensemble Model for Power System Dynamic Security Assessment

Cited by 11 publications

References 18 publications

Hybrid randomised learning‐based probabilistic data‐driven method for fault‐induced delayed voltage recovery assessment of power systems

Hybrid randomised learning‐based probabilistic data‐driven method for fault‐induced delayed voltage recovery assessment of power systems

Recent Developments in Machine Learning for Energy Systems Reliability Management

Versatile and Robust Transient Stability Assessment via Instance Transfer Learning

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