Constructing operating rules to avoid voltage collapse: a statistical approach

Cholley, P.; Lebrevelec, C.; Vitet, S.; Pasquale, M. de

doi:10.1109/icpst.1998.729331

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…French transmission operator RTE has been using decision tree based security assessment methods to define operational security rules, especially regarding voltage collapse prevention Schlumberger et al 1999Schlumberger et al , 2002Pierre et al 1999;Martigne et al 2001;Paul and Bell 2004;Henry et al 1999Henry et al , 2004aHenry et al , 2006Cholley et al 1998). They provide operators a better knowledge of the distance from instability for a post-contingency scenario in terms of pre-contingency conditions, and thus save a great amount of money by preserving the reliability while enabling more informed operational control closer to the stability limits.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Power System Security Assessment Through Intelligent Decision Trees

Krishnan

2014

Complex System Modelling and Control Through Intelligent Soft Computations

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Power system security assessment involves ascertaining the post-contingency security status based on the pre-contingency operating conditions. A system operator accomplishes this by the knowledge of critical system attributes which are closely tied to the system security limits. For instance, voltage levels, reactive power reserves, reactive power flows are some of the attributes that drive the voltage stability phenomena, and hence provide easy guidelines for the operators to monitor and maneuver the highly stressed power system to a secure state. With tremendous advancements in computational power and machine learning techniques, there is increased ability to produce security guidelines that are highly accurate and robust under a wide variety of system conditions. Particularly, the decision trees, a data mining tool, has lend itself well in extracting highly useful and succinct knowledge from a very large repository of historical information. The most vital and sensitive part of such a decision tree based security assessment is the stage of training database generation, a computationally intensive process which involves sampling many system operating conditions and performing power system contingency assessment simulations on them. The classification performance of operating guidelines under realistic testing scenarios depend heavily on the quality of the training database used to generate the decision trees. So the primary objective of this chapter is to develop an improvised database generation process that creates a satisfactory training database by sampling the most influential operating conditions from the input operating parameter state space prior to the stage of power system contingency simulation. Embedding such intelligence to the system scenario sampling process enhances the information content in the training database, while minimizing the computing requirements to generate it. This chapter will clearly explain and demonstrate the process of identifying such high information contained sampling space and the advantage of deriving security guidelines from decision trees that exclusively use such an enhanced training database.

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Section: Introductionmentioning

confidence: 99%

Enhanced Power System Security Assessment Through Intelligent Decision Trees

Krishnan

2014

Complex System Modelling and Control Through Intelligent Soft Computations

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“…Many utilities have taken and are continuing to take a serious interest in implementing learning algorithm such as decision tree in their decision making environment. French transmission operator RTE has been using decision tree based security assessment methods to define operational security rules, especially regarding voltage collapse prevention [34,35,36,37,38,39,40,41,42,43,44]. They provide operators a better knowledge of the distance from instability for a post-contingency scenario in terms of pre-contingency system conditions, and they save a great amount of money while preserving the reliability of the system by enabling more informed control of the operation nearer to the stability limits.…”

Section: Decision Tree Based Inductive Learningmentioning

confidence: 99%