Faculté des Sciences appliquées
Faculté des Sciences appliquées

Machine learning of proxies for power systems reliability management

Duchesne, Laurine ULiège
Promotor(s) : Wehenkel, Louis ULiège
Date of defense : 27-Jun-2016/28-Jun-2016 • Permalink :
Title : Machine learning of proxies for power systems reliability management
Author : Duchesne, Laurine ULiège
Date of defense  : 27-Jun-2016/28-Jun-2016
Advisor(s) : Wehenkel, Louis ULiège
Committee's member(s) : Geurts, Pierre ULiège
Ernst, Damien ULiège
Karangelos, Efthymios ULiège
Language : English
Discipline(s) : Engineering, computing & technology > Electrical & electronics engineering
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil électricien, à finalité approfondie
Faculty: Master thesis of the Faculté des Sciences appliquées


[en] Nowadays, electrical power is essential for the functioning of our society. Power
system reliability management intends to prevent service interruptions while minimising
the socio-economic costs. In order to meet this purpose, it takes decisions
based on a reliability criterion which is currently a deterministic criterion. However,
several factors such as the ageing of the power systems’ components, the increasing
share of renewable energies and the development of the electricity market complicate
the work of power system’s operators. All of this calls for a new probabilistic
reliability management approach and criterion, that is able to take into account the
stochastic nature of a power system. This should allow operators to design and
operate their system more economically while ensuring a desired level of reliability.
In this context, the European project GARPUR developed a new reliability
management approach and criterion. This master’s thesis intends first to study the
behaviour of this new reliability management approach and criterion when its use
in real-time operation is anticipated in the day-ahead operation planning context,
and to build simplified models (called proxies) with machine learning so as to mimic
the optimisation tool implementing the criterion. In order to reach these objectives,
a database is generated using Monte-Carlo simulations and the optimisation tool
developed in the GARPUR project. The dataset is first analysed by using classical
exploratory statistical methods and then by computing feature importances derived
from tree-based ensemble methods to assess the link between input parameters and
target quantities computed by optimising real-time decisions based on the reliability
criterion. Finally, several machine learnt proxies are tested to assess their accuracy
to predict several target outputs of the decision making program. The analysis
demonstrated the importance of the weather on the results. A comparison of the
proposed probabilistic reliability criterion with a “pseudo” deterministic criterion
based on a predefined list of contingencies has shown that the deterministic criterion
is more costly for an identical reliability level. Furthermore, considering the
fixed N-1 set of contingencies is typically conservative, but may sometimes be insufficient.
The building of proxies also revealed that tree-based ensemble methods seem
to be the most accurate estimators. In particular the models based on extremely
randomized trees were found to be the most accurate models to predict most target



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  • Duchesne, Laurine ULiège Université de Liège > Master ingé. civ. électr., fin. appr. (ex 2e master)


Committee's member(s)

  • Geurts, Pierre ULiège Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
    ORBi View his publications on ORBi
  • Ernst, Damien ULiège Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids
    ORBi View his publications on ORBi
  • Karangelos, Efthymios ULiège Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
    ORBi View his publications on ORBi
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