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Probabilistic design of power-system special stability controls

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TLDR
A probabilistic approach to the design of power-system special stability controls is presented here, using Monte-Carlo simulations that takes into account all the potential causes of blackouts, slow and fast dynamics, and modeling uncertainties.
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This article is published in Control Engineering Practice.The article was published on 1999-02-01 and is currently open access. It has received 21 citations till now. The article focuses on the topics: Probabilistic design & Probabilistic logic.

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Citations
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Emergency control and its strategies

TL;DR: Different possible strategies are discussed for the design of emergency control schemes and the potential impact of new approaches in the context of power system emergency control are discussed.
Journal ArticleDOI

Voltage Instability Prediction Using a Deep Recurrent Neural Network

TL;DR: In this article, a recurrent neural network with long short-term memory (LSTM) was used to predict voltage instability in the Nordic32 test system, where combinations of different operating conditions and contingency scenarios are generated using timedomain simulations.

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

Belmudes, +2 more
TL;DR: In this article, a cross-entropy (CE) method for rare-event simulation is proposed for power system reliability evaluation, when a severity function defined on the set of possible events is available.

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

TL;DR: 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, is proposed.
Proceedings ArticleDOI

A rare event approach to build security analysis tools when N − k (k >1) analyses are needed (as they are in large scale power systems)

TL;DR: A procedure relying on importance sampling techniques for identifying rare events in combinatorial search spaces for steady-state security analyses is proposed and it is shown that it is able to efficiently identify among a large set of contingencies some of the rare ones which are dangerous.
References
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Book

Pattern classification and scene analysis

TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Book

A Theory of Learning and Generalization

TL;DR: This new edition, with substantial new material, takes account of important new developments in the theory of learning and deals extensively with the Theory of learning control systems, which has now reached a level of maturity comparable to that of learning of neural networks.
Book

Automatic Learning Techniques in Power Systems

TL;DR: This book presents a representative subset of automatic learning methods - basic and more sophisticated ones - available from statistics, and from artificial intelligence, and appropriate methodologies for combining these methods to make the best use of available data in the context of real-life problems.
Journal ArticleDOI

Decision tree based transient stability method a case study

TL;DR: In this paper, the decision tree transient stability method is revisited via a case study carried out on the French EHV power system and the results obtained show real promise for the method to meet practical needs of electric power utilities.
Related Papers (5)
Frequently Asked Questions (15)
Q1. What are the contributions in "Probabilistic design of power-system special stability controls1" ?

A probabilistic approach to the design of power-system special stability controls is presented here. The approach is tested on a large-scale study on the South—Eastern part of the extra-high-voltage system of Electricite & de France. Using Monte-Carlo simulations, it takes into account all the potential causes of blackouts, slow and fast dynamics, and modeling uncertainties. 

Admittedly, this goal is very ambitious and needs further research to be fully reached. As far as future work is concerned, the first step will consist of taking full advantage of the database. 

The first step of this work consists of extending the decisiontree induction method, in order to build automatically, instability detection rules that offer a good compromize between selectivity and degree of anticipation (Geurts and Wehenkel, 1998b). 

The second research direction is aimed at developing machine-learning methods that are able to exploit the temporal nature of the databases more efficiently. 

Loss of synchronism extends to the whole study region, and the defense plan trips lines to isolate it from the system, and activates load shedding in France. 

due to the faster changes occurring in today’s power systems, the need for more regular studies is increasing, while human expertise may quickly become obsolete and even misleading. 

since it is becoming increasingly difficult to expand transmission systems within reasonable delays (e.g., due to ecological constraints), and since the operation with high security margins conflicts with economic efficiency, the present trend in power systems is to rely more and more on special emergency control schemes. 

The design of such special emergency control schemes implies the identification of the main failure modes of the system, the determination of appropriate mitigating control actions, and the design of automatic triggering devices. 

Predicting voltage collapse in region LSince region L is weak in terms of voltage stability, and tap-changer blocking may act too late to avoid voltage collapse, it would be interesting to find an anticipatory criterion to trigger emergency control. 

above all, the principal shortcoming of this deterministic approach is its inability to take into account the stochastic nature of the causes of power system failures, and the unavoidable modeling uncertainties. 

The approach consists of three steps:(i) modeling probabilistically the causes potentially leading to extreme conditions (weakened operating conditions, abnormal operation of protections, multiple disturbances) as well as uncertainties (load behavior, external systems2),(ii) using parallel Monte-Carlo simulations to sample scenarios according to this information (random combinations of operating conditions, dynamic modeling hypotheses (e.g., relay settings, malfunctions, external systems, load2), and disturbances),(iii) building up a database of simulation results, collecting key variables and their temporal behavior, and using data-mining techniques to extract from this synthetic information about the main breakdown modes and possible ways to improve emergency control schemes. 

The approach proceeds by iterating through the following elementary steps:Study specification: setting up a detailed probabilistic model of the possible causes of insecurity: multiple disturbances, bad coordination and/or mal-operation of protective devices, over-optimistic preventive security strategies due to uncertainties in modeling parameters. 

On a workstation with 256MB of main memory it is thus possible to access simultaneously up to 50 million attribute values: about 1500 scenarios, described by 200 temporal attributes, with an average number of 150 time steps per scenario. 

The data-mining process is itself composed of successive steps, aimed at extracting more and more refined information from the simulation database. 

Some of these attributes are to be used in order to define the severity of the scenarios, i.e. to measure the consequences in terms of loss of load and generation.