New Solutions for Fault Detections and Dynamic Recoveries of Flexible

Power Smart Grids

Syrine Ben Meskina

, Narjes Doggaz

and Mohamed Khalgui

LISI, ISI, University Tunis-El Manar, Tunis, Tunisia

LIPAH, FST, University Tunis El-Manar, Tunis, Tunisia

LISI, INSAT, University of Carthage, Tunis, Tunisia

Keywords:

Smart Grid, Fault Detection, Dynamic Recovery, Multi-agent System, Mobile Agent, Local Solution,

Distributed Solution.

Abstract:

This research paper deals with fault detections and dynamic recoveries of electrical smart grids that should be

ﬂexible and automatically adapted at run-time when faults occur on lines or devices. These grids are composed

of three levels of power lines: High Voltage Lines (44KV ), Medium Voltage Lines (11KV ), and Low Voltage

Lines (380V ). In order to control the complexity of detection and deduction, we propose new relations between

faults. We deﬁne also a multi-agent architecture to allow dynamic recoveries where two types of agents are

deﬁned: static agents and mobile agents. Static agents have as a task the detection of local faults on power

lines and the recovery of their normal behaviors by using a local knowledge-base. The mobile agents are

created to dynamically move on lines and to ﬁnd new solutions when no local solution is found. To validate

and test our approach, we present experimental results showing the originality of the paper’s contribution by

assuming a case study.

1 INTRODUCTION

Faults and blackouts may be caused by the worsen

system conditions such that short-circuit, overloaded

loss of power plants, protection hidden failure... (Lu

et al., 2006). Around the world, there are many deﬁni-

tions of smart grids including big amounts of multidi-

mensional characteristics. Smart grids can be seen as

the modernization of the current electric grid through

the intensive use of communication technologies, the

integration of renewable and green energies to decar-

bonize power systems, as well as the improvement of

both security and reliability of the network and, even,

the addition of new smart electric hardware devices

(like meters, storage devices, sensors...). The impor-

tance of the damages caused by power breaks and out-

ages, as well as, the classical centralized production

architecture have encouraged and inspired researchers

to work on it in order to bring changes on power grids

and, even, on their infrastructures. The majority of the

related works deployed Multi-Agent Systems (MAS)

in the domain of power networks to ensure a commu-

nication between several components of the network

and to organize their tasks. (Rohbogner et al., 2012)

discusses what is an agent in the context of smart grids

and (Endriss et al., 2004) discusses the problem of

checking an agent’s conformance to protocol.

In this paper, we address new problems related to

the performance of Smart Grids such as the deduction

of faults as soon as possible and the optimal recovery

with feasible run-time solutions. We propose, a novel

agent-based approach for the fault detection and re-

covery in the context of electrical smart grids when

faults occur on lines or devices. To control the com-

plexity of detection and fault deduction, we propose

new relations between faults to deﬁne our new efﬁ-

cient fault resolution method. This new fault catego-

rization allows the forecast of other consequent faults

by deduction. We deﬁne also a multi-agent architec-

ture to allow dynamic recoveries composed of three

types of agents. To reduce the time required for the

system recovery, our solution ensures the update at

run-time of data base agent when new solutions are

found. These solutions may be, directly, used when

the same fault occurs again. The originality of our

work lies, also, in the agent mobility allowing; (i)

a speed and useful information exchange, (ii) a de-

centralized new solution as mobile agents can move

over the grid and (iii) the best solution at any time

as the calculus is done at real-time. In Section 2, we

370

Ben Meskina S., Doggaz N. and Khalgui M..

New Solutions for Fault Detections and Dynamic Recoveries of Flexible Power Smart Grids.

DOI: 10.5220/0005091303700377

In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO-2014), pages 370-377

ISBN: 978-989-758-039-0

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

present the state of the art. In Section 3, we formalize

and characterize smart grids, fault detection and de-

duction and propose new relations between faults. In

Section 5, we expose the deployed multi-agent sys-

tem to be composed of static and mobile entities in

order to search, respectively, local and distributed so-

lutions. Section (6) presents our system recovery ap-

proach. And in Section 7, we test our solution for

several scenarios in a simulated case study.

2 STATE OF THE ART

Over the last years, many researchers worked on dif-

ferent topics and concepts relative to power networks

and smart grids. There is an important number of

works relative to the problems of fault detection in

power systems, self-healing and system recovery...

The majority of the proposed approaches in the liter-

ature use MAS. A MAS is a decentralized system to

be composed of a set of agents placed in some envi-

ronment of software agents (McArthur et al., 2007b).

(Rohbogner et al., 2012) shows that distributed hier-

archical control structures are more efﬁcient than cen-

tral ones. A new useful concept appears which con-

sists of dividing the network on subnetworks, called

micro-grids or ”islands”, is highlighted. Distributed

infrastructures decentralizing the control imply, gen-

erally, the deployment of Distributed Energy Re-

sources (DER). Based on this concept, (Chen et al.,

2010) showed that it is possible to reduce the like-

lihood of dramatically cascading failures, even, when

integrating a small number of local generators into the

power grid. In fact, thanks to this strategy based on

DER and decentralized power system control, (Rah-

man et al., 2007) proposed a concept of micro-grids

working during normal operation and, also, in case of

failures, as islands independent of the main grid and

supplied by local generators. (McArthur et al., 2007a)

and (McArthur et al., 2007b) investigates and stud-

ies the higher value-added by (MAS) to power indus-

try. (Tate and Overbye, 2008) uses the Phasor Mea-

surement Unit to detect simple line outages and (Tate

and Overbye, 2009) uses it to detect double line out-

ages and to identify network parameter errors. Other

works on failure identiﬁcation and diagnosis include

(Cai et al., 2010), (Calderaro et al., 2011), (He and

Zhang, 2010) and (Russell and Benner, 2010). (Vy-

atkin et al., 2010c) proposes a distributed MAS com-

posed of a number slave agents called BAGg and only

one unique master agent called FAG - centralizing

the control in the power grid system and achieving

a self-healing action through fault location and power

restoration. This MAS architecture was proposed in

(Nagata and Sasaki, 2002) and was used, also, in

(Zhabelova and Vyatkin, 2012) integrating Intelligent

Logic Nodes for self-healing power system in case of

circuit ruptures. These two industrial standards were

combined, in (Vyatkin et al., 2010b), to develop an

intelligent control architecture for smart grids.

After these short observations on related works,

we draw the following conclusions. As we previously

mentioned, there are many research works focussed

on fault detection and self-healing in power systems.

Some of them detect and repair only one type of en-

countered fault(s) but, neither of them studies the con-

sequent faults. For example, (Vyatkin et al., 2010a)

handles the case of only one fault type occurs (elec-

tricity break due to a tree falling on an electric line).

In our paper, we are looking for formalizing the strat-

egy of fault detection and resolution. A step of fault

analysis and categorization is necessary to guide the

resolution strategy. In the other hand, we note that

the majority of the proposed solutions are based on

MAS deploying many agents making the communi-

cation process very expensive. Rahman and al. pro-

poses, in (Rahman et al., 2007), an efﬁcient approach

of power systems self-healing based on specialized

micro-grids but it is very expensive in terms of com-

munication costs (as they deploy 7 agents). They re-

duced the number of agents to 4, in (Pipattanasom-

porn et al., 2009), in order to decrease the response

time but the used fact bases are inextensible. (Vy-

atkin et al., 2010c) deploys only a unique one master

agent FAG and multiple BAGs leading to a centralized

control power system and to a big number of BAGs.

Whence, it is important to minimize the number of

agents in order to reduce the required communication

costs. In our approach, we propose a small number of

agents to be deployed and to instantiate mobile agents

in order to reduce the required response time in order

to ﬁnd a solution.

3 SMART GRID

CHARACTERIZATION

Generally, electric grids involve three voltage lev-

els: High Voltage Line (HVL), Medium Voltage Line

(MVL) and Low Voltage Line (LVL). Commonly, we

have principal lines which are operational and emer-

gency lines used when the ﬁrst ones are in failure.

In our study, the electric grids are formed by three

principal electric components interconnected through

electric lines. These components are: (i) Power

Generators (PG) that produce high voltage energy

transported through HV L. (ii) Down Power Trans-

formers (DPT ) that transform the received electric-

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371

ity from a voltage level to the lower one. We con-

sider two types of power transformers: The Medium

Voltage Transformer (MV T) and the Low Voltage

Transformer (LV T ). (iii) Consumers (as end-users

in electrical grids): we distinguish Medium Con-

sumers (MC) which are supplied by MV T through

MV L and Low Consumers(LC) which are supplied by

LV T through LV L.

In our system each component and electric line C

is characterized by:

1 - its activation state, A(C), as in(1):

A(C) =



0 , i f C is deactivated

1 , i f C is activated

(1)

2 - its voltage level, VoltLevel(C), as in (2):

VoltLevel(C) =







1, i f C ∈ high voltage level

2, i f C ∈ medium voltage level

3, i f C ∈ low voltage level

(2)

Each High Voltage Line L is deﬁned by its trans-

ported load, TranspL(L). While, the Medium and

Low Voltage Lines L

are deﬁned by their distributed

load DistL(L

The Power generators are characterized by the pro-

duced power ProdPow. While each power trans-

former X is characterized by its transformed power

Trans f Pow(X) and its priority. The priority of a

transformer X, pr(X), is calculated by equation (3):

pr(X ) =

∑

i=1

(pr(S

))/

∑

j=1

pr(P

) (3)

where S

is a supplied device, P

is a power grid de-

vice, n is the number of supplied devices by X and m

is the the number of all power grid’s devices.

Each consumer X is characterized by its:

1 - rank, rank(X), which is its relative place in the

supplying line,

2 - priority, pr(X), which represents the priority of the

consumer X.

3 - required load, ReqLoad(X), which represents the

load to need,

4 - received load, ReceivLoad(X ), which is calculated

by summing the loads distributed by the n incoming

lines L as mentioned in (4).

ReceivL(X) =

∑

i=1

(DistL(L))) (4)

4 SYSTEM

CHARACTERIZATION AND

FAULT CATEGORIZATION

The robustness of electrical grids is proven through

their ability of managing and facing all eventual sub-

merging problems. In this section, we propose a set

of minimum conditions characterizing the system.

4.1 System Operating Conditions

To insure the healthy operation of the electric grid, the

following conditions have to be veriﬁed.

1 - Activation Constraint: The activation states of all

involved electric components have to be conserved.

For a High Voltage Grid, this condition is deﬁned by:

(A(PG) = 1) ∧ (A(HV L) = 1) ∧ (A(MV T ) = 1) (5)

2 - Stability Constraint: The stability of the power

grid must be maintained by keeping the frequency of

all electric components and the voltage of all electric

lines, approximatively, equal to the respective pre-

ﬁxed default values f

and U

. For a High Voltage

Level, the stability constraint is deﬁned by:

( f req(PG) ≈ f

) ∧ (volt(HV L) ≈ U

)

∧( f req(MV T ) ≈ f

)

(6)

3 - Flowing Load Constraint: The safe and secure

operation of power systems depends, also, on loads

ﬂowing into the grid. In fact, they must respect the

capacities of the deployed components. These loads

must, also, respect the constraint relative to both re-

quired and received loads by a consumer X as in (7).

ReqL(X) ≤ ReceivL(X) (7)

To avoid the under-voltage and over-voltage prob-

lems, each consumer X should be, sufﬁciently, sup-

plied. Therefore, the received loads should not be less

than their minimum capacities and should not exceed

the devices capacities. The violation of one of these

constraints leads to faults.

4.2 Fault Classiﬁcation

The encountered faults can, easily, propagate through

electric lines from the sub-grid on which it occurs to

another one as the power system is a mesh network

to be composed of inter-connected electric compo-

nents. We deﬁne faults as consequences of one or

some unsatisﬁed constraints. Based on the constraints

presented in Section 4.1, the faults that can occur on

power grids may be; switching-off (deactivation), in-

stability, under-voltage, over voltage and isolated sub-

grids. The open-circuits present a consequent fault to

the violation of constraint 1 or 2 or both of them. The

consequences of this fault are summarized by Table

1. As the dissatisfaction of only one constraint can

engender multiple faults, we look for minimizing the

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Table 1: Isolated sub-grids constraints.

Fault origin Isolated Sub Grid Consequences

f req(PG) 6= f

A(PG) = 0 ∧ A(MV T ) = 1 ∧ A(HV L) = 1

ProdPow(PG) = 0 ∧ TranspL(HV L) = 0

∧ Trans f Pow(MV T ) = 0

f req(MV T) 6= f

A(PG) = 1 ∧ A(MV T ) = 0 ∧ A(HV L) = 1 Trans f Pow(MV T ) = 0

volt(HV L) 6= U

A(PG) = 1 ∧ A(MV T ) = 1 ∧ A(HV L) = 0

TranspL(HV L) = 0 ∧ Trans f Pow(MV T )

= 0

faults to be resolved and guiding the search for solu-

tions. For that, we propose new deﬁnitions for domi-

nant and equivalent faults based on new relations. We

denote the fault set by F and the i

fault by F

such

that: F

∈ F/i ∈ N.

Deﬁnition1: Dominant Fault. Let F

and F

be,

respectively, a fault and a subset of faults such that

⊆ F. Let us consider a component X connected to

a set of components Y such that |F

| = |Y |. A fault F

on a component X is said to be dominant of a fault F

on a component Y

and noted by: F

(X) → F

) if

for each F

∈ F

, Y

∈ Y and j = 1,...,|F











VoltLevel(X) < VoltLevel(Y

)

VoltLevel(X) = VoltLevel(Y

) and

rank(X ) < rank(Y

)

(8)

The resolution of either the dominant or the domi-

nated fault(s) resolves the problem. This strategy al-

lows to reduce the required time of resolution.

Deﬁnition2: Equivalent Fault. Let F

and F

be two

faults. Let us consider two connected components X

and Y . The faults F

and F

, respectively, on the com-

ponents X and Y are said to be equivalent and noted

by: F

(X) ⇔ F

(Y ) if

VoltLevel(X) = VoltLevel(Y ) and rank(X ) = rank(Y )

(9)

In fact, the resolution of only one of the equivalent

faults can resolve the problem. These new relations

allow us to control and reduce the complexity of faults

by deduction. The advantage is, particularly, ob-

served in the case of multiple equivalent faults, as we

are focused on resolving only one of them.

5 NEW ARCHITECTURE FOR

FLEXIBLE SMART GRIDS

The resolution of the occurred faults can be done, ei-

ther, locally by taking a simple and systematic reac-

tion or by searching new solutions among the other

sub-grids. We propose, then, the use of multi-agent

paradigm to implement our distributed system decen-

tralizing the control. It ensures the fault detection and

deduction as well as the real time resolution to pro-

vide the best solution. Our MAS is composed of three

types of software agents:

- Reconﬁguration Agent: Each RAgent is responsi-

ble of the continuous supervision and maintaining of

the healthy operation of the power sub grid under its

scope. It is composed of a set of rules describing its

behavior.

- Mobile Agent: A MAgent is a software entity mov-

ing, through electric lines, among the connected com-

ponents of the smart grid to collect some information.

- Data Base Agent: The DBAgent is responsible of

the management of the data base relative to the power

system. It contains, mainly, the new solutions found

by our MAS system as well as all the information

relative to the whole power grid structure as well as

the history of all the encountered faults and the cor-

responding solutions. It may be requested by both

RAgent and MAgent about information relative to the

power system. It should update its data base for each

new solution found by MAgent. The DBAgent may

be requested to avoid searching solutions for prob-

lems already resolved.

In order to reduce the communication cost, we asso-

ciate one RAgent to each electrical sub-grid. Hence,

we assign one RAgent to each PG, MV T and LV T .

The RAgents have, then, to supervise the concerned

component as well as the components directly sup-

plied belonging to the voltage level below. We de-

ploy only one central DBAgent (Figure 1). In fact,

the number of DBAgents depends of the whole con-

sidered grid size and, also, of the cardinality of sub-

grids formed from the global grid, as well as, of the

size of knowledge to be stored.

6 SYSTEM RECOVERY

In the current section, we present how our MAS op-

erates according to the power system circumstances.

6.1 Fault Detection and Local Solutions

The RAgent sensors must, continuously, observe their

environment in order to detect all changes and anoma-

NewSolutionsforFaultDetectionsandDynamicRecoveriesofFlexiblePowerSmartGrids

373

Figure 1: Example of agent assignment: attributing one

RAgent to each sub-grid, MAgent are created by RAgent

which can be cloned in bifurcation and unique DBAgent to

the overall power grid.

lies (unsatisﬁed constraint) on any electric compo-

nent. When a fault is detected on the component X,

the concerned RAgent begins by isolating the compo-

nent or line responsible of this anomaly (X) such it is

presented by Algorithm 1. Then, the RAgent in ques-

tion categorizes the occurred fault as it is described

by Algorithm 2 in order to guide the search for local

solutions where F is the fault detected on X. A local

solution consists of ﬁnding a deactivated emergency

line belonging to the supervised sub-grid SG and sup-

plying, sufﬁciently, X as it is described in Algorithm

3. When there is more than one local solution, the

RAgent chooses the one having the bigger remaining

load according to the (10).

Maximize( DistL(eL) − ReqL(X) ) (10)

Algorithm 1: Fault Detection and Isolation.

Require: SensorDetection 6= 

if ∃ FrequencyProblem(X) then Isolate(X)

if ∃ VoltageProblem(L) then Isolate(L)

if ∃ Isolated(X) then Categorize the occurred faults

if no LocalSolution then

Request DBAgent(Problem)

if ∃ Solution in DB then call Effectors

else create MAgent

if ∃ Under-voltageProblem OR ∃ Over-

voltageProblem then create MAgent

When no local solution is found, the RAgent requests

the DBAgent about a solution already found for

the encountered problem. If no solution for the

occurred fault is stored in the data base, the RAgent

searches for a cooperative solution from the neighbor

sub-grids. For that, the RAgent creates an MAgents to

Algorithm 2: Fault Categorization.

Require: F: the detected fault

if F is dominant fault then

call(RAgent Local Solution) on the component

corresponding to the dominant fault

if no found solution for the dominated fault then

call(RAgent Local Solution) on the compo-

nents corresponding to the dominated faults

end if

if F is an equivalent fault then

call(RAgent Local Solution) on the components

corresponding to the equivalent faults

end if

Algorithm 3: RAgent Local Solution.

if ∃ deactivated emergency line incoming to X

then

choose the one having the bigger positive re-

maining load

call effectors to execute solution

else if no local solution then

Create MAgent on X

end if

obtain information about the components belonging

to the connected sub-grids. Based on the received in-

formation, the RAgent creator decides about continu-

ing or not with the studied alternative -ie. validate or

not this solution or sub-solution.

6.2 Distributed Search For Solution

Algorithm 4 presents how a MAgent MA

is created

by a RAgent RA

on a failed component C to move

dynamically on lines and visit all the devices con-

nected to C through the existing outgoing lines called

paths. At each visited device, MA

notiﬁes RA

sending a message. This message contains some in-

formation that are; 1- the remaining load (ReceivL -

ReqL), 2- the cumulative priority by adding the prior-

ity of the visited component and 3- information about

the end of the taken path (if there is no connected

component to be visited ie- no outgoing lines then

reaches the end of the taken path). This message

allows RA

to validate or not the sub-solution/solution

provided by each MA

and even to choose the best one

using the utility evaluation expressed in (11). A valid

sub-solution should not have a negative or a null re-

maining load when the end of the visited path is no yet

reached. A valid solution should not have a negative

value for the calculated remaining load when the end

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374

of the visited path is reached. When the visited com-

ponent has more than one outgoing line (connected

to more than one components), MA

will be dupli-

cated in order to analyze all the existing alternatives or

paths. The duplicated agents are called clones. Each

clone takes only one line at a time and all the clones

operate simultaneously to decrease the required time

(Algorithm 5). Each component should be visited at

most once by the same MAgent to ensure that the res-

olution terminates.

Maximize(

∑

(ReceivL(X) − ReqL(X)) +

∑

pr(X ))

(11)

Algorithm 4: RAgent New Solutions.

InstantiateMAgentOn(X)

for each MAgent created do

switch (received message(RL, cpr, stateEOP))

case sub-solution:

Check validity of the sub-solution

if invalid sub-solution then

destroy MAgent

end if

case solution:

Check validity of the solution

if valid solution then

store the solution

end if

destroy MAgent

end switch

end for

if no stored solution then

send a reconﬁguration request to user

else

choose the best solution according to (11)

call effectors to execute solution

request DBAgent for adding new solution

end if

7 EXPERIMENTATION: CASE

STUDY

In this section, we present an example of an elec-

tric grid to be composed of 2 PG, 10 DPT (4 MV T

and 6 LV T ) and 24 consumers (7 MC and 17 LC). It

contains 45 lines (4 HV L, 18 MV L including 5 eMV L

and 23 LV L including 6 eLV L). We are interested on

failure as well as their causes in order to ﬁnd good

and satisfactory solutions. We study two type of fault

cases to validate and test our approach that we devel-

oped in Java platform. We used the Aglet API to de-

velop the Mobile Agents. The simulations were done

over an electrical network which is simulated by ref-

erence Matlab of Simulink.

Algorithm 5: MAgent Movement.

for each visited component Y do

calculate RL(Y )

calculate cprY

if no outgoing lines from Y then

stateEOP = true

else

stateEOP = f alse

end if

send message(RL(Y), cpr(Y), stateEOP(Y))

switch (number of outgoing lines from Y)

case =1:

call (MAgent Movement on the unique con-

nected component to Y)

case > 1:

cloning MAgent

for each clone or for each connected compo-

nent to Y do

call (MAgent Movement on the component

to be visited)

end for

end switch

end for

7.1 Local Fault Recovery

Let us consider the case where there is a problem

on the line LV L2 and let us suppose that the volt-

age value detected on LV L2 is equal to 80V 6= U

(Figure 2). This value involves an instability prob-

Figure 2: Detection of a voltage problem on LV L2.

lem on the concerned sub-grid. This voltage instabil-

ity is detected and identiﬁed by RA1, the RAgent as-

signed to the sub-grid supplied by LV T 6. RA1 reacts,

then, and isolate the fault by deactivating LV L2 (Fig-

ure 3). As a consequence to this deactivation, there

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375

is an emergence of faults F1 and F2 which are de-

tected, respectively, on LC13 and LC16. As deﬁned

in section 4, F1 is dominant on F2. Hence, RA1

tries to resolve the dominant fault F1. For that, a

local solution is searched and RA1 looks for the ex-

istence of emergency lines in the supervised sub-grid.

Since, there is an emergency line (eLV L1) incoming

to LC13 and eLV L1 is able to, sufﬁciently, supply

LC13. RA1 executes this feasible solution and acti-

vates the emergency line eLV L1, which resolves the

problem. The detection and resolution CPUs, were,

respectively, equal to 1, 78s and 0,69s.

Figure 3: Problem Localization and Resolution.

7.2 Distributed Fault Recovery

The second studied case is about a frequency problem

observed on the transformer LV T 5 (Figure 4). Let us

suppose that the frequency value detected on LV T 5,

at 11PM, is equal to 25hz 6= f

. The low voltage con-

sumers LC2, LC3 and LC6 represent, respectively, a

hospital, a university and a policy ofﬁce having the

respective priorities 1, 3 and 2. At this time, LC3

does not require electricity while LC2 and LC6 re-

quire each one 180V . This instability of frequency

is detected by RA2, the agent assigned to the sub-grid

supplied by LV T 5. Then, RA2 isolates the fault by

deactivating LV L1. As a consequence, 3 faults; F

, F

and F

occur, respectively, on LV T 5, LC11 and LC17.

and F

are faults dominated by F

as illustrated by

Figure ??. As it is not possible to resolve F

since

LV T 5 requires a physical repairing, RA2 looks for re-

solving the dominated faults F

and F

. There is no

local solutions as there is no emergency lines belong-

ing to the sub grid. Then, RA2 requests the DBAgent

for searching a solution, stored in its central data base,

to the encountered problem. As this problem happens

for the ﬁrst time over this sub grid, the DBAgent does

not return any solution. Thus, RA2 instantiates a MA-

gent MA1 on LC11 to search new solutions from other

Figure 4: Detection of a frequency problem on LV T 5.

Figure 5: Problem Localization and Resolution

sub grids connected to the supervised one. After vis-

iting LC17, MA1 is duplicated; the ﬁrst clone MA11

visits LC6 and the second one MA12 visits LC3. Both

of them execute Algorithm 5 at each visited compo-

nent. The RAgent creator analyzes the results sent

by these two clones MA11 and MA12. For MA11, the

calculated remaining load is equal to 0 knowing that it

has not yet reached the end of its path (as LC6 has an

outgoing line). MA12, visiting LC3, reaches the end

of its path (as LC3 has 0 outgoing line) with remain-

ing load equal to 180V . At the next iteration, MA11

reaches the end of its path with a negative remaining

load equal to −180V which is invalid as it is negative.

The two responses are found on, respectively, 1,17s

and 1,09s. RA2 chooses the second solution provided

by MA12 as the ﬁrst one is rejected; it consists of ac-

tivating eLV L2. There were only ﬁve messages ex-

changed between our deployed agents RA2 and MA1.

ICINCO2014-11thInternationalConferenceonInformaticsinControl,AutomationandRobotics

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8 CONCLUSIONS

In this paper, we propose an agent-based approach to

resolve the faults in power systems which run accord-

ing to the following rules; (i) detect and localize the

problem,(ii) identify the problem by checking con-

straints, (iii) categorize the occurred faults and (iv)

choose the suitable strategy of search for solution (lo-

cal through RAgent or distributed through MAgent)

to accelerate the resolution. In future works, we are

interested in reducing the number of the deployed

agents by integrating a knowledge base in RAgents to

store local solutions in order to reduce the requesting

time. We are, also, interested in investigating multi-

ple and even concurrent faults at the same time. The

use of smart storage devices present, also, an interest-

ing issue for us as well as the failure rates to forecast

failures.

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