The objective of this paper is to discuss research trends in the context of power system emergency control First, different possible strategies are discussed for the design of emergency control schemes Then some new research directions are presented The paper does not restrict its scope to a particular type of stability problem Rather, it aims at providing a global view of emergency control and discusses the potential impact of new approaches

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Emergency control and its strategies

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 uses 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 95 % 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.

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Voltage Instability Prediction Using a Deep Recurrent Neural Network

We propose in this paper a novel approach for identifying rare events that may endanger power system integrity. This approach is inspired by the rare-event simulation literature and, in particular, by the cross-entropy (CE) method for rare- event simulation. We propose a general framework for exploiting the CE method in the context of power system reliability evaluation, when a severity function defined on the set of possible events is available. The approach is illustrated on the IEEE 30 bus test system when instability mechanisms related to static voltage security are considered.

Cross-Entropy Based Rare-Event Simulation for the Identification of Dangerous Events in Power Systems

We consider the problem of performing N − k security analyses in large scale power systems. In such a context, the number of potentially dangerous N − k contingencies may become rapidly very large when k grows, and so running a security analysis for each one of them is often intractable. We assume in this paper that the number of dangerous N-k contingencies is very small with respect to the number of non-dangerous ones. Under this assumption, we suggest to use importance sampling techniques for identifying rare events in combinatorial search spaces. With such techniques, it is possible to identify dangerous contingencies by running security analyses for only a small number of events. A procedure relying on these techniques is proposed in this work for steady-state security analyses. This procedure has been evaluated on the IEEE 118 bus test system. The results show that it is indeed able to efficiently identify among a large set of contingencies some of the rare ones which are dangerous.

Cyril Lebrevelec

Papers

Probabilistic design of power-system special stability controls