Application of ANN for stability assessment of large power system by post-fault rotor angle measurements

Soni, Bhanu Pratap; Gupta, Vikas; Kumar, Rajesh; Saxena, Akash; Surana, S.L.

doi:10.1109/etechnxt.2018.8385363

Cited by 5 publications

(3 citation statements)

References 34 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%

“…Recently, due to the success of deep neural networks in other applications, many researchers tackled the dynamic security assessment problem with such an approach. Classical Feed-Forward Neural Networks (FFNNs) are used as classifiers to predict the small-signal stability [61], oscillatory stability [62] or transient stability [63]; or as regressors to predict the CCT or damping ratio [22], the CCT and final value of rotor angle after a fault [64], the load stability margin [65] or a voltage stability index [66]. In [67], a MapReduce algorithm is used to parallelise the learning of several networks and predict both stability status and the transient stability index.…”

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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