A MAS Model Approach to a Wind Farm Maintenance Strategy
Miguel Kpakpo, Mhamed Itmi
and Alain Cardon
Normandie Université, INSA, LITIS, France
Keywords: Cost Optimization, Decision Support Systems, Maintenance Strategy, MAS, O&M.
Abstract: The aim of this work is to propose a new method of analysis and optimization of maintenance strategy for
wind farms. The objective is to help wind farm operator to carry out the optimization of the maintenance
costs through profitability analysis of the wind farm according to failures, planned shutdown situations and
maintenance budgets. Such approach has the advantage of combining the O&M (optimization and
maintenance) technical vision and the financial vision within the meaning of profitability. The platform
model is based on multi-agent systems. It aims to realize the calculation and optimization of scenarios.
Agents have been identified from the knowledge of the windfarm O&M domain thanks to the wind farm
operator’s point of view. The platform we’re developing is named PROMEEO, a French acronym for O&M
onshore wind farms rationalization and optimization’s platform.
1 INTRODUCTION
The maintenance of the equipment represents an
important issue in all industries. In wind energy’s
case where the exploitation of the wind farm is
strongly impacted by maintenance policies
management. A wind farm is established for an
approximate lifetime from 20 to 25 years. During
this period, the owner sets up various operations
intended to guarantee the availability of the wind
turbines and their good performance.
For most industrial owners, the optimization of
maintenance is firstly focused on the maintained
equipment (a wind farm for the wind power
operator). Most industrial owners thus focus
themselves on the equipment’s state to maximize its
operating time.
Consequently, the preventive or corrective
policies of maintenance are prioritized to guarantee
the availability of the wind farm, which is the major
indicator of maintenance analysis; it is calculated by
(1):
D % =
Operating time
total time
100
(1)
The temporal availability allows to know the ratio
corresponding to the operating time of the wind
turbine regarding the total time. It is the ability of an
equipment to be able to perform a given function
under given conditions at a given instant or during a
given interval of time, if the provision of external
means is assured. The percentage of availability
makes it possible to deduct the associated overall
loss of production.
The first objective of the wind farmer is to
maximize availability. Maintenance plays a strategic
role regardless the type of industry. In wind energy
industry, it represents a significant cost; it has a
major impact on the cost of operating the wind
farms. As it has been said, each wind turbine is built
for an approximate service life of 20 to 25 years.
During this period, the operator shall put in place
various maintenance operations to ensure the
availability and operation of the wind turbines. This
availability is determined upstream in the operating
contract. It is located between 80 and 95% according
to farms.
According to the AWE (American Wind
Energy), the cost of O&M is not negligible
throughout the life cycle of a wind farm (Ribrant,
2006). At the end of life, this cost can reach up to
25% of the total cost of kWh. Reducing O&M costs
by 0.18% would result in a 3% reduction in the total
cost of a kWh.
Availability implies a maintenance cost that
cannot be minimized without degrading the
availability rate; then the operators adapt each
maintenance contract to the windfarm’s
characteristics and to the availability objectives
(Piana, 2016). However, the final priority of an
Kpakpo, M., Itmi, M. and Cardon, A.
A MAS Model Approach to a Wind Farm Maintenance Strategy.
DOI: 10.5220/0006554501590167
In Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018) - Volume 1, pages 159-167
ISBN: 978-989-758-275-2
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
159
operator is to opt for the maintenance strategy that
would enable a better profitability of the windfarm.
Our method is focused on this priority. Indeed, the
vision of financial profitability, reinforces the
technical vision of maintenance while usually
profitability is often separated from the financial
sector. Availability is no longer the only criterion of
maintenance analysis because the wind farm’s
profitability accentuates this analysis.
Our approach uses a model based on MAS
theory to realize wind farm profitability scenarios
based on available budget forecasts and information
on breakdowns that would occur. The main
objective is to evaluate different scenarios and their
profitability providing financial indicators for one or
many failures list to choose the scenario that suits
the operator's requirements.
1.1 The Problem of Maintenance in
Wind Energy Sector
There are several types of maintenance that are used
in wind energy sector:
Preventive maintenance: it aims to reduce the
breakdowns by anticipating them. The interventions
are carried out after a well-defined duration (annual,
semi-annual etc…) or after a signal appearance
following the failure or the going beyond a
threshold. This type of maintenance aims to reduce
the possible risk of breakdown. It corresponds to a
logic of the breakdowns prevention and
maximization of the availability. Ideally for a wind
farm, this type of maintenance is carried out during
the periods of low wind to ensure availability during
the periods of strong winds.
Curative maintenance: used in a single way,
curative maintenance certainly reduced well the
maintenance costs, but it can quickly exceed the
forecasts and causes important disadvantages related
to the production. As the wind farm ages, the
number of corrective increases and generates
indirect costs which it is difficult to estimate before
the breakdowns.
Corrective maintenance: it’s a type of
maintenance made after a diagnosis of breakdown.
Its goal is to set back an element in operating
condition (Hajej and Rezg, 2012). It’s a strategy
which results in an unquestionable advantage
relating to the maximum use of the wind turbine’s
components; in fact, the equipment is replaced or
repaired only in the event of breakdown. It’s also
called the “breakdown” strategy. In the case of a
wind turbine, the failures often occur during period
of strong wind. However, it is in this period that the
wind turbine must be available to the production.
The wind turbine’s stop throughout corrective
maintenance thus involves a consequent production
loss. The single advantage of a corrective
maintenance is that it makes it possible to use the
equipment until exhaustion.
Hybrid maintenance: it is the most current type
of maintenance. It combines the two types of
maintenance: preventive and corrective. It consists
in anticipating some breakdowns by the means of
preventive interventions and being reactive for the
corrective O&M operations when the breakdowns
occur.
Several maintenance tools were developed by
research laboratories and companies. Each one of
these tools adopts an angle of analysis of
maintenance. It can be oriented to some maintenance
fields like spares management or to the whole O&M
field including: spares management, human
resources, installation etc…). For example:
1. SINBAD (Guillon, 2015): is a tool which main
objective is to predict the behaviour of a wind
turbine at any moment. This project was born
from a recommendation of a Franco-British
partnership dedicated to the offshore oil rig to
create a digital tool allowing the visualization of
the tree structure of wind turbines offshore oil
rigs.
2. The OMCE (Operation and Maintenance Cost
Estimator) (Rademakers et al., 2009): it is one of
the most complete models of simulation,
marketed since 2004. The project was initiated
by a consortium including Vestas, Shell Wind
Energy, DTU and ECN (Energy Research of the
Netherlands). It combines three strategies of
maintenance: corrective, preventive and
predictive to predict the annual cost of the
maintenance actions of wind farms. (Onshore
and Offshores) (van de Pieterman et al., 2011).
As inputs, the tool records the components
reliabilities, maintainer information and the
operation to provide maintenance costs.
From the maintenance operator’s point of view, the
problems consist in finding the “optimal” cost of
maintenance that represents balance between an
expected production of the wind farm and a budget
associated with a series of breakdowns planned
during the period. To optimize maintenance, we thus
must optimize the budget of maintenance
(subcontracts and spares) on the wind farms because
it represents the most important owner’s growth
drivers.
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1.2 Strategy of Maintenance over
One-Year Duration and Financial
Indicators
An efficient maintenance strategy is measured by the
reactivity of the team of maintenance, the capacity to
control the costs of parts and the additional costs and
profitability of the wind farm at the end of a period.
In our approach, the final profitability of a wind
farm, over a consequent period of operation, is
expressed by a customized EBITDA (Earnings
Before Interest, Taxes, Depreciation, and
Amortization) indicator. This indicator is called
“Function Cost”: FC (2)
FC= Theoretical turnover - Production
losses -O&M Costs
(2)
FC=ThT - PL -O&M Costs
The theoretical production (3) of a wind turbine is
equivalent to its production with an availability to
100% i.e. no downtime ago over the period. Over a
chosen period, we can obtain a theoretical
production of the wind turbine starting from the
variables of entry which are the data of wind
measured on the wind farm and the curve of real
power.
Data of wind: they represent the speeds of wind
measured by the anemometer-nacelle of the wind
turbine. In the event of unavailability, they are
recovered on the anemometer of the nearest wind
turbine. If no data is provided on the wind turbines,
it will take the data of the mast of measurement
which is located near to the windfarm.
Figure 1: A power curve diagram.
ThT = [
(
Vd
)
Pd(PC)]
∗ kWhPrice(€)
(3)
Where:
ThT: theoretical turnover
Vd: Wind measurement with the period in m/s
Pd: power curve measure
PC: power curve measures kWh
The production loss is defined as a production which
should have been realized for the period when the
wind turbine was stopped; it’s deduced in a
theoretical way. The production loss represents a
shortfall which is calculated by the same formula as
the theoretical turnover but over the duration of
production loss (PL). In this case, we use the same
formula as in (3).
The O&M Cost is related to all maintenance
cost. (4). It can be divided in two parts: spares et
subcontract costs.
O&M Cost (€) = ∑ Spares Costs (€)
+Subcontract cost (€)
Subcontract cost (€): this cost is related to the
company receiving benefits and which is responsible
for the wind farm maintenance. This company has
an availability rate to reach fixed by contract. In the
facts, the company in charge of maintenance
subcontract is a dissociated company of the farm
owner. The later delegates all the ordinary
maintenance actions.
Spares cost (€): this is the cost of all the parts
used during maintenance operations. This cost is
valued at the end of the stock.
Figure 2: A scenario of the evolution of the indicators in
relation to a series of breakdowns and O&M costs.
Operator estimates maintenance budget at the
beginning period: O&M Cost
estimated
of period; it
may be different at the end of the period O&M Cost
real
. In the present case, three situations may arise at
the end of the period:
1. The forecast of cost is higher than the real
maintenance costs; then maintenance is overpaid
compared to the work completed on the stops:
O&M Cost
estimated
> O&M Cost
real.
2. The expected O&M cost is lower than the final
real cost of maintenance. We suppose then, all
A MAS Model Approach to a Wind Farm Maintenance Strategy
161
other things being equal, that O&M Cost
estimated
<
O&M Cost
real.
3. The ideal situation where the operator has
properly estimated its O & M budget in relation
to the operation of the farm: O&M Cost
estimated
=
O&M Cost
real.
The evaluation consists of analysing the activity of
the wind farm during the period of operation through
the evolution of the O&M indicators over time
(Figure 2).
We will construct this evaluation thanks to
several scenarios that will help the operator in the
choice of his strategy.
1.3 State of Art Discussion and
Problem Definition
The tools for current maintenance are focused on the
wind turbine or the windfarm. These tools can
predict equipment failure that does not allow to
project on the income that can be derived from the
equipment itself.
In our case, at the “Compagnie du Vent”, a
company that manages several wind farms, there
exist a tool named PROMEEO (Platform of
Rationalization and optimization of the maintenance
of the Onshore wind farms). We’ve made a
comparison between the state of art’s tools and
PROMEEO.
Table 1: Comparison of state of art's tools and our tool
PROMEEO.
Tools
State of art’s tools
PROMEEO
Strengths
Tools focused on
turbines, their
operation.
Operational vision
for maintenance
Complete
maintenance
indicators on the
probabilities of
failures
occurrences.
Financial
maintenance-focused
tool
Corresponds to a
management vision
of maintenance
Shortcomings
Incompatibility
with the operation
of a wind farm
operator because
of several models
of different
turbines in a
territory.
Management of
simulation input
parameters: O&M
subcontractor’s time
of maintenance
reactivity.
Improvement of the
There is no
modularity to
follow the
business evolution
and the context of
the wind farm
operator.
prevention of failures
from alerts
Prevention of
defaults on large
components by
analysing the alarms
that precede these
defaults.
We set the problem upside down by optimizing
the wind farm production compared to the
maintenance that can be budgeted.
1.4 The System Agentification
With O&M Multiagent system we can evaluate the
indicators of current and future situations of a wind
farm in real-time with the management of the
evolution of knowledge. The system performs the
simulation of the indicators from the validated input
data (failures list and wind measurements over the
year for theoretical production). We proposed a
scenario calculation which is the expression of a
management of failures over a significant period.
Thus, a one-year (for example) scenario using the
history on-line gives the state of the fleet, the actual
cost of providing maintenance and its profitability.
(figure 3). A scenario S for year n is described by:
Sn = List Pn O&M Cost n ThT FP n
Where:
List P
n
: This list of failures is either recovered
from the year n chosen or constructed fictitiously
from the forecasts of stops.
ThT Theoretical Turnover: Theoretical
production forecast during the analysed period.
Subcontract cost: budget for the period for
maintenance providers.
FC: Function cost (windfarm profitability).
FP: «financial performance» ratio of
performance of the costs compared to the
theoretical turnover (5).
FP(n)=FC(n)/ThT(PC,n)
(5)
Figure 3: A scenario in PROMEEO.
We compare the various indicators of
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profitability according to the various proposals of
maintenance contract anything being equal. Several
scenarios can be provided for the same list of
breakdowns and different subcontract cost. Each
scenario is composed by a series of breakdowns. A
breakdown (Figure 4) involves a production loss and
costs specific to this breakdowns or failures. For
each breakdown, the system calculates the real cost
of maintenance.
O&M Cost
breakdown
= Subcontractor Cost
breakdown
+ Spares
Cost
breakdown
The set of O&M costs is the sum of the breakdown
costs Ct O&M Cost). Each scenario expresses a
strategy of maintenance of the breakdowns and the
results of the wind farm in terms of performance and
profitability.
Figure 4: From descriptive ontology extract of a
breakdown.
1.5 The Multiagent Model Used to
Develop the Different Scenarios
The multiagent approach is an alternative to more
conventional approaches to cooperation and problem
solving. In general, it can deal with a problem by
decomposing it into simpler sub problems, so that
agents must focus on only one subtask at a time. In
(Ferber and Simonin, 2003), an agent is defined as
an entity driven by a set of trends (satisfaction’s
function to optimize or goals to reach) which has its
own resources and has only a partial representation
of the environment. Its goal is to meet its objectives
while considering its skills, its resources, its
perception of its environment.
An agent is aware of its decision’s capacities and
seeks to achieve a precise goal. Another agent’s
characteristic is its capacity to cooperate with other
environment’s entities. While conforming to its level
of rationality, it can choose to interact, cooperate,
negotiate or limit its interaction with the
environment. An agent, like an object, encapsulates
a state and behaviour but the agent encapsulates its
control on its behaviour; an object has control only
on its state.
In a system with many agents, the goals to reach
can be complementary or contradictory. The agents
can be separated according to the type of relation in
two categories: the competitive agents and the
collaborative agents. A collaborative agent makes
decisions and carries out actions in agreement with
other agents to achieve their respective goals. This
effect of group enables them, consequently, to pool
knowledge and to bind their goals. The environment
of maintenance is composed of agents as well as
objects in the system. The agents present in the
environment are in interaction with objects and other
agents. Whether they are physical or abstract, they
carry out actions to conform to the objectives which
are predefined; the objects can interact with
databases and provide answers at the requests of
other agents.
In our work, an agent has a goal which consists
in modifying the object “Breakdown”, to modify its
state then to share its information with its dealings.
The concepts identified in the descriptive ontology
give us the objects which compose the field of the
O&M. The concepts in the ontology can be at the
same time physical or abstracted elements. The
concept, when it is a physical entity can be
translated in agent if it expresses behaviours, goals.
Each agent must be able to express at least one
objective to reach. Example: the wind turbine (the
equipment) expresses an objective to run with an
availability of 100%. Even if it has an objective of
operation (Availability ratio =100%), its behaviours
are oriented by the O&M indicators values. The
wind turbine cannot be proactive on its environment;
it expresses a set of states which will be evaluated
by indicators. When the concept is a physical entity,
it must be able to express at the same time objectives
and autonomous behaviours to be declined as an
agent.
For example, the indicators are abstract concepts;
the indicator “Cost O&M” (the total cost of
maintenance), has an objective that is to stay low.
The behaviours that it will express will have
consequences on the environment composed by
failures objects and the other agents.
1.6 The Organization of the MAS
According to Environment
Each object “failure” sent in the environment is
described by the following attributes expressing
knowledge:
Failure Id
Turbine Id
A failure description
A MAS Model Approach to a Wind Farm Maintenance Strategy
163
Start and ending date
O&M failure Cost
The scenarios are built with PROMEEO. The tool is
based on eight kinds of agents. For a list of failures
given as inputs of the system, the direction of
knowledge communication using agents is shown in
(Figure 5):
Figure 5: MAS model.
For each agent identified in the system, we will
describe its goals and the entities with which it
cooperates.
1.6.1 Search for Failure Agent
Goal: it detects possible failures from alerts and
messages based on the history.
Actions: it sends possible failures as objects to
Failure Agent.
Proactivity: The search for Failure Agent can
be proactive when it detects failure on a wind
turbine. It evaluates, and alerts based on the
probability of occurrence of failures that have
been registered.
Communication: With the SCADA
(Supervisory Control and Data Acquisition).
It retrieves alarms and signals as objects
coming from this entity. It sends to Failure
Agent the list of failures as objects. In
coordination with the Diagnosis Agent, it
recovers the causes of failures that had not
been identified.
1.6.2 Historic Agent
Goal: find former O&M operations for O&M
agent.
Actions: it perceives the failures in progress and
the potential breakdowns and asks for
maintenance operations.
Proactivity: from the details given by O&M
agent, it retrieves the operations that have been
performed for the same failure.
Communication: it provides to the O&M Agent
the breakdowns and asks for the execution of
maintenance actions. With the Search for failure
Agent by receiving information about potential
failures.
1.6.3 Failure Agent
Goal: to close all the breakdowns and to ask for
O&M operations.
Actions: it perceives the breakdowns and failures
in progress and the potential breakdowns and
asks for the maintenance actions.
Proactivity: the agent is proactive in sending the
breakdowns.
Communication: with the SCADA: it recovers
alarms in progress which describe the stopping
of the wind turbine in the event of breakdown. It
provides to the O&M Agent the
breakdowns/failures and asks for the
maintenance actions. It communicates also with
the Search for failure Agent by receiving
information on potential failures.
1.6.4 Diagnosis Agent
Its identifies the breakdowns and their causes. it
perceives the breakdowns, sends the causes and the
list of the parts to be used for the interventions. It
sends to the Search for Failure Agent the causes of
the breakdowns up to that point unknown. It has no
proactivity: no proactivity. Communication: It
provides to O&M Agent the diagnosis Agent.
1.6.5 O&M Agent
It’s equivalent to a maintenance project manager in
the company.
Goal: its main objective is to achieve
maintenance operations ASAP while respecting
the strategy set by the Function Cost Agent. It
provides O&M costs for each failure to the
Function Cost.
Actions: it performs one or more interventions to
resolve received failures. Before each operation,
it sends the total cost to Function Cost Agent.
Proactivity: this agent is responsive during the
failure management.
Communication: its communicates with Failure
Agent by receiving failures as objects; with
Service Provider Agent by receiving their cost
ICAART 2018 - 10th International Conference on Agents and Artificial Intelligence
164
for the failure management; with Diagnosis
Agent by receiving the spares that will be used
for the operations; with spares Agent: by
receiving available spares for operations; with
provides cost O&M to the Function Cost Agent
that validates the operation.
1.6.6 Service Provider Agent
This agent corresponds to the subcontractor; this
agent oversees the operation execution on the site. In
the system, it sends the Subcontract cost (€).
Goal: it must be able to provide a time of
intervention and the cost of the service.
Actions: for each failure or breakdown, it
provides a person receiving benefits, the cost of
service and the duration of associated
intervention.
Proactivity: it has no proactivity; it only
responses to the requests of the O&M Agent.
Communication: communicate with O&M Agent
by sending the provider cost. (It sends the service
provider chosen and the subcontractor cost).
1.6.7 Weather Agent
It lists the days with favourable weather for
maintenance operations. Proactivity: it responses to
the requests coming from the O&M Agent. The
agent can be proactive when it detects an alteration
(deterioration or improvement) of the weather on the
days it has sent.
1.6.8 Function Cost Agent
This agent validates the execution of the
maintenance action according to profitability.
Within the framework of a calculation, this agent is
responsible for analysing and validates each
operation. We have modelled this agent for
simulations where the system would be constrained
by an objective of profitability. According to the
owner’s strategy based on criteria like the
intervention’s duration or the importance of the
operation’s cost (cost O&M), it validates the
intervention. It is the most important agent in the
system according to the main goal of the system.
Goal: according to the cost of the intervention, it
gives its consent to the execution of the
intervention
Actions: it validates the maintenance operation if
it is consistent with the strategy (internal
argument).
Proactivity: it only responds to requests from the
O&M Agent.
Communication: it communicates with the O&M
Agent to get the “GO” or “NO GO” for the
operation.
1.6.9 Spares Agent
This agent is responsible for spares managements.
Its goal is to answers to spare request for operation
maintenance and send total spares cost (∑Cost
spares
(€)); It communicates with O&M Agent by sending
spares costs and supply delays.
1.7 Running the Agents System
We must implement a lot of agents according to the
categories we have defined. The agents in our
system are not in competition, each one of them
expresses a useful state for its dealings. Considering
the low number of dealings of each agent, the
interaction is direct between the agents. Each agent
is defined by its goal, its rules of behaviour and its
interactions with the other components within the
system. Thus, it has a representation of itself and
environment which surrounds it.
A scenario is the result of the operation of the
wind farm throughout a given period. This operation
considers the possible stops and the estimated
forecasts of wind. Three phases were identified for
the calculation of scenarios:
Firstly, we have “breakdown” event: this
indication gives the state of the wind turbines on the
wind farms. They are the system’s inputs as objects:
ListF (list of failures provided for calculation).
Secondly, the treatment of the breakdowns: at this
step, the failures are studied to know their costs and
effects (lowers availability and production loss) on
the productive apparatus; the system calculates for
each breakdown the cost of maintenance. Thirdly,
the final indicators which give the plan of
maintenance and the cost associated with the
maintenance actions while making it possible to
maximize the profitability of the wind farm
(minimum of loss of availability).
For the user, PROMEEO platform provides a set
of following scenarios with the data input which is
provided. Platform PROMEEO (Platform of
Rationalization and optimization of the maintenance
of the Onshore wind farms) resulting from this
approach is developed for a wind operator (“La
Compagnie du Vent”). The platform establishes the
scenarios with inputs from several data sources
which come from several different applications
A MAS Model Approach to a Wind Farm Maintenance Strategy
165
(figure 6). These applications provide information’s
to PROMEEO database.
Figure 6: System inputs and outputs.
Presently, we’re developing the agents and the
treatments for the platform. The data inputs were
formatted in a SQL database. The strategies of
maintenance of the wind farms are evaluated
compared to the results of the indicators. The
scenarios depend with time on the relative data to
the breakdowns and the results on indicators
calculated by the platform. The database schema
was designed in UML diagram.
Maintaining industrial equipment doesn’t mean
any more to keep it in a good condition; it means to
achieve goals to maximize the profit which is to get
more than a return on investment. It means also to
preserve the wind turbines for a long time and at
lower costs to amortize the expenses engaged for
construction and the exploitation. O&M Budgets and
subcontracts costs must be regularly revalued to
guarantee efficiency in the wind farms management.
To simulate the real costs of service of a wind farm
means to know the cost of the reactivity of the
maintenance subcontractor. Some questions remain.
For example, wow simulate the maintenance
subcontractor reactivity while varying the
contractual costs within the framework of a forecast?
Figure 7: MAS Operating.
Based on the real-time information produced by
PROMEEO, the MAS evaluates the various
scenarios proposed, improves them, quantifies them
by using its history and the old evaluations available
and proposes them to the operators. According to the
operators' choices, the MAS updates its knowledge
(the criteria of good choice for example) to make
them operational for any other scenario production
action evaluated by the operators (figure 7).
1.8 Conflict Management in the MAS
and Results
The agents in the MAS may face conflicts in the system:
On the failure’s treatment: for each failure, many
indicators are calculated. Their values depend on the
failure’s day start date and an end date. On each
failure, the O&M Agent which validates the failure’s
end date of the maintenance operation by granting to
the “failures object” an end date based on the
information at its disposal. The conflicts that may
arise if we add a couple of constraints for example:
calculate the O&M Cost with a profitability target
(6):
FC= ThT-x-PL
x=ThT-FC-PL
(6)
ThT: the theoretical production cannot be modified;
it represents a fatal data for the operator. The only
optimization action that can be applied is related to
the failures management and consequently
productions loss.
With PL (production loss) which is a function of
maintenance support duration; this duration can be
mechanically reduced but it depends on subjective
criteria such as the responsiveness of the O&M
subcontractor company that we are not able to
quantify.
2 CONCLUSIONS
To be efficient, we have proposed a MAS model for
maintenance that is proactive on the breakdowns,
failures and operations on the windfarm to maximize
profits. The platform is intended to the wind farms
operators. A first prototype is presently under
development towards a professional validation and
that is our first objective. Our final objective is to
produce a MAS system with real-time data and
continuous optimization.
ACKNOWLEDGEMENTS
The work presented in this paper was supported by
the French Foundation of Technological Research
under grant CIFRE N° 2014/0099.
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166
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