Implementation of a Massively Parallel Dynamic Security Assessment Platform for Large-Scale Grids

Konstantelos, Ioannis; Jamgotchian, Geoffroy; Tindemans, Simon H.; Duchesne, Philippe; Cole, Stijn; Merckx, Christian; Štrbac, Goran; Panciatici, Patrick

doi:10.1109/tsg.2016.2606888

Cited by 68 publications

(32 citation statements)

References 22 publications

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“…Note that this step can be a computational bottleneck due to the very large number of simulations required (1 million simulations arise when analyzing 1000 contingencies across 1000 network states). The iTesla platform's excellent scaling capability has been verified in experiments using up to 10,000 cores [20].…”

Section: A Offline Computation Of Security Rulesmentioning

confidence: 93%

Online security assessment with load and renewable generation uncertainty: The iTesla project approach

Vasconcelos

Carvalho

Meirinhos

et al. 2016

2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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Abstract-The secure integration of renewable generation into modern power systems requires an appropriate assessment of the security of the system in real-time. The uncertainty associated with renewable power makes it impossible to tackle this problem via a brute-force approach, i.e. it is not possible to run detailed online static or dynamic simulations for all possible security problems and realizations of load and renewable power. Intelligent approaches for online security assessment with forecast uncertainty modeling are being sought to better handle contingency events. This paper reports the platform developed within the iTesla project for online static and dynamic security assessment. This innovative and open-source computational platform is composed of several modules such as detailed static and dynamic simulation, machine learning, forecast uncertainty representation and optimization tools to not only filter contingencies but also to provide the best control actions to avoid possible unsecure situations. Based on High Performance Computing (HPC), the iTesla platform was tested in the French network for a specific security problem: overload of transmission circuits. The results obtained show that forecast uncertainty representation is of the utmost importance, since from apparently secure forecast network states, it is possible to obtain unsecure situations that need to be tackled in advance by the system operator.

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Section: A Offline Computation Of Security Rulesmentioning

confidence: 93%

Online security assessment with load and renewable generation uncertainty: The iTesla project approach

Vasconcelos

Carvalho

Meirinhos

et al. 2016

2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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“…In real applications, more delicate methods for efficient database generation and more careful generation of relevant OCs should be used [3], [18], where for instance the impact of unit commitment and topology changes are taken into account.…”

Section: A Generation Of Training Datamentioning

confidence: 99%

“…To ensure a secure operation, system operators often use an approach called dynamic security assessment (DSA). DSA includes time-domain analysis to test the power system's dynamic response after a set of contingencies [3]. Assessment of the dynamic stability is a complex task, and even with recent progress in high performance computing, it is generally not feasible to assess the dynamic stability in real-time [3].…”

Section: Introductionmentioning

confidence: 99%

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On-Line Voltage Instability Prediction using an Artificial Neural Network

Hagmar

Tuấn

Carlsot

et al. 2019

2019 IEEE Milan PowerTech

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This paper develops a new method for voltage instability prediction using a recurrent neural network with long short-term memory. The method is aimed to be used as a supplementary warning system for system operators, capable of assessing whether the current state will cause voltage instability issues several minutes into the future. The proposed method use a long sequence-based network, where both real-time and historic data are used to enhance the classification accuracy. The network is trained and tested on the Nordic32 test system, where combinations of different operating conditions and contingency scenarios are generated using time-domain simulations. The method shows that almost all N-1 contingency test cases were predicted correctly, and N-1-1 contingency test cases were predicted with over 93 % accuracy only seconds after a disturbance. Further, the impact of sequence length is examined, showing that the proposed long sequenced-based method provides significantly better classification accuracy than both a feedforward neural network and a network using a shorter sequence.Index Terms-Dynamic security assessment, long short-term memory, recurrent neural network, voltage instability prediction, voltage security assessment.Robert Eriksson (SM'16) received the M.Sc. and Ph.D. degrees in electrical engineering from the KTH Royal in 2004. He is currently a Senior Lecturer with the Division of Electric Power Engineering, Department of Energy and Environment, Chalmers University of Technology. His current research interests include power system operation and planning, power market and deregulation issues, grid integration of renewable energy, and plug-in electric vehicles.

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“…Given the huge computational requirements of analyzing the security of national-level systems [27], efficient database generation is of paramount importance.…”

Section: Introductionmentioning

confidence: 99%

Using Vine Copulas to Generate Representative System States for Machine Learning

Konstantelos

Sun

Tindemans

et al. 2019

IEEE Trans. Power Syst.

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Abstract--The increasing uncertainty that surrounds electricity system operation renders security assessment a highly challenging task; the range of possible operating states expands, rendering traditional approaches based on heuristic practices and ad hoc analysis obsolete. In turn, machine learning can be used to construct surrogate models approximating the system's security boundary in the region of operation. To this end, past system history can be useful for generating anticipated system states suitable for training. However, inferring the underlying data model, to allow high-density sampling, is problematic due to the large number of variables, their complex marginal probability distributions and the non-linear dependence structure they exhibit. In this paper we adopt the C-Vine pair-copula decomposition scheme; clustering and principal component transformation stages are introduced, followed by a truncation to the pairwise dependency modelling, enabling efficient fitting and sampling of large datasets. Using measurements from the French grid, we show that a machine learning training database sampled from the proposed method can produce binary security classifiers with superior predictive capability compared to other approaches.

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Implementation of a Massively Parallel Dynamic Security Assessment Platform for Large-Scale Grids

Cited by 68 publications

References 22 publications

Online security assessment with load and renewable generation uncertainty: The iTesla project approach

Online security assessment with load and renewable generation uncertainty: The iTesla project approach

On-Line Voltage Instability Prediction using an Artificial Neural Network

Using Vine Copulas to Generate Representative System States for Machine Learning

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