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IEEE TRANSACTIONS ON POWER SYSTEMS 1

Fault Indicator Deployment in Distribution SystemsConsidering Available Control and Protection

Devices: A Multi-Objective Formulation ApproachAlireza Shahsavari, Seyed Mahdi Mazhari, Student Member, IEEE, Alireza Fereidunian, Member, IEEE, and

Hamid Lesani

Abstract—This paper introduces a multi-objective fault in-dicator (FI) placement method in electric distribution systems.The prevalent FI placement problem formulation is extended byconsidering effects of existing protection and control devices oncustomers’ restoration time. Moreover, the customers’ averagerestoration time index (CARTI) is proposed, as a new technicalobjective function with respect to uncertainties of automaticswitching. Furthermore, a multi-objective solution approach isdeveloped to simultaneously minimize indispensable economic andtechnical objectives. The resultant optimization problem is solvedthrough a multi-objective particle swarm optimization (MOPSO)based algorithm, accompanied by a fuzzy decision making methodto select the best result among the obtained Pareto optimal set ofsolutions. Assuming SAIDI and CARTI as technical objectives, theproposedmethod is applied to bus number four of theRoyBillintontest system (RBTS4), as well as a real-life distribution networkwithabout 5500 customers, followed by a discussion on results.

Index Terms—Distribution automation system (DAS), fault in-dicator placement, fuzzy decision making, multi-objective particleswarm optimization (MOPSO), power system planning, reliabilityevaluation.

NOTATION

The notation used throughout this paper is reproduced belowfor quick reference.

Sets:

Set of network load points.

Set of load types including residential,commercial, industrial, public, and critical.

Manuscript received August 18, 2013; revised December 24, 2013 and Jan-uary 27, 2014; accepted January 27, 2014. This work was supported by theControl and Intelligent Processing Center of Excellence (CIPCE), Universityof Tehran, Tehran, Iran. Paper no. TPWRS-00999-2013.A. Shahsavari and H. Lesani are with SMRL, CIPCE, School of Electrical

and Computer Engineering, University of Tehran, Tehran, Iran (e-mail: [email protected]; [email protected]).S. M. Mazhari is with the School of Electrical Engineering, Amirkabir Uni-

versity of Technology and School of Electrical and Computer Engineering, Uni-versity of Tehran, Tehran, Iran (e-mail: [email protected]).A. Fereidunian is with the Faculty of Electrical Engineering, K. N. Toosi

University of Technology (KNTU) and SMRL, CIPCE, University of Tehran,Tehran, Iran (e-mail: [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TPWRS.2014.2303933

Set of network lines/transformers/buses.

Set of candidate locations of FIs.

Set of automatically restorable load points.

Set of manually restorable load points.

Set of non-restorable load points.

Set of zones associated to the distributed FIsand RCSs.

Constants:

Planning horizon (year).

Average demand of load type at load pointwithin year (kW).

Failure rate of equipment .

Cost of installing an FI in candidate location($).

Maintenance cost of the th FI at year ($).

Inflation/interest rate.

Total number of customers of load point .

Average dead-time before the crew reach tothe outage area (hour).

Average automatic/manual switching time(hour).

Probability of successful operation ofcommunication interface when zone isfaulted.

Functions:

Objective functions of the FIs placementproblem.

Expected outage cost to customers ($).

Total investment and maintenance costs of FIs($).

Present worth factor.

Interruption cost of load type at load pointdue to outage time ($).

0885-8950 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.


2 IEEE TRANSACTIONS ON POWER SYSTEMS

Fig. 1. Illustrative network reinforced by FIs and their response to the occurred faults.

Variables:

Binary decision variable that is equal to 1 ifan FI installed at candidate location , and 0otherwise.

Interruption duration of load point due tofault occurrence at (h/f).

Restoration time associated with automaticswitching/manual switching/repairing (h/f).

Automatic restoration probability of load pointdue to fault occurrence at .

Average fault detection/repairing time whenzone is faulted (hour).

Length of section (km).

I. INTRODUCTION

E LECTRICAL power distribution systems play an unde-niable role in social welfare, following the daily increase

of electricity fed energy consumers. Electric utilities are alsoobliged to support an adequate level of service for their cus-tomers considering deregulation of power industry. Hence, sev-eral technical necessities such as reliability and power qualityhave to be met by the utilities, while delivering electrical en-ergy to the consumers [1], [2].Distribution automation systems (DASs) including demand

side management and outage management system (OMS) areemployed to augment efficiency and improve reliability ofthe distribution systems [3]–[5]. The OMS is devoted to con-duct fault location, isolation, and service restoration (FLISR)process under contingencies. To such aim, several control, andprotective devices accompanied by suitable measurements areinstalled throughout the network [6], [7]. Once a fault occurs,the FLISR disposes control sequences to supply restorablecustomers considering the switch locations. That is, the faultis firstly detected by the protective relays; then, the circuitbreaker opens and de-energizes the faulted feeder. Thus, allthe downstream customers experience an outage due to theoccurred fault. The control sequence is accomplished as thefault location is found and isolated, which leads to energizingthe restorable customers.Supplying both restorable and non-restorable customers are

effectively influenced by fault location time; thus, several ap-

proaches are developed to accelerate the fault location process[7]. It is empirically seen in distribution operation that fault lo-cation time takes about 25% of total service restoration time as-sociated to the manually restorable customers [8], [9].Fault indicators (FIs) are devices which enable visual or

remote indication of a fault on the distribution system. Theyinform the repairing/switching crew while a fault occurs inits downstream network. Fig. 1 shows a sample network rein-forced by several FIs; as shown in Fig. 1(a), the 1st and 2ndFIs have tripped as a fault occurred between buses , and. The repairing/switching crew has been informed of the

accurate faulted zone since the 2nd FI is tripped, yet the 3rdone is non-tripped. Hence, the patrol searching distance forthe faulted zone is decreased by almost 66%. According tothe above, expanding FIs within the distribution network is aproper solution to minimize the fault detection and restorationtime, thus reliability improvement. However it is neither eco-nomical nor necessary to install an FI at upstream/downstreamof every bus of main feeder and its laterals. Fig. 1 illustratessuch fact as a simple network which considers all inevitablefaults. If a fault occurs in upstream zones of bus , the faultlocation is detected by probing the 2nd and 3rd FIs, as Fig. 1(a)shows. Moreover, while a fault occurs in downstream of bus ,remote-controlled circuit breaker (RCCB) trips, yet the FIs arenot tripped; therefore, the faulted zone is recognized based onthe received data from the 1st FI and the RCCB, as shown inFig. 1(b). As it can be seen in Fig. 1(c) and (d), a fault occurredin lateral of bus is indicated by probing the 1st and 2nd FIs,which reveals that installing the 4th FI might be unnecessary.As defining optimal FIs placement is imperative for distribu-

tion utilities, it is formulated as a power system optimizationproblem. The aims of prevalent FIs placement problems are tofind optimal number and locations of FIs, while the technicalconstraints are respected with minimum cost [10]–[17].Mathematical models and algorithms have been developed

in literature [10]–[17]. In [10], a reliability model is presentedfor FIs, evaluating the effects of various numbers of FIs on re-liability indices. However, economic issues have not been con-sidered in the proposed model. In [11], an economic based FIsplacement via binary shuffled frog leaping algorithm is inves-tigated within a real-life distribution network. However, the ef-fects of available protective and control devices are not incor-porated. The performances of genetic and artificial immune al-gorithms are investigated for solving the FI placement problem


SHAHSAVARI et al.: FAULT INDICATOR DEPLOYMENT IN DISTRIBUTION SYSTEMS 3

Fig. 2. Candidate locations among a simple network with respect to the installation rules.

in [12] and [13], respectively. Nevertheless, the proposed ob-jective functions assume several simplifications, which make ithard to use in practical projects.A genetic based algorithm is employed in [14] to determine

the optimal positions of FIs in an actual distribution network.While a mid-point recloser is located in the studied network, noexisting switches are considered. Moreover, the candidate loca-tions of FIs are limited to themain branches. In [15], the immunealgorithm (IA) is adopted to solve the optimal FIs placementand conducted on relatively large distribution network. How-ever, modeling of FIs and their interactions with the availablecontrol devices are not described. In [16] and [17], an economicbased FIs placement via genetic algorithm is investigated withconsidering dispersed generation insertion. Nevertheless, nei-ther technical objective function nor reliability evaluations arereported. In addition to these studies, several efforts are devotedto solve the FI placement problem in literature [18]–[20].As discussed, the available remote protection and control

devices are rarely incorporated in problem formulation of theaforementioned research. Therefore, the interactions betweenthe FI placement problem and probabilities of possible controlsequences are rarely referred up to now. Moreover, a multi-ob-jective techno-economical approach is not yet applied to thisproblem.In this paper, a multi-objective FI placement model is pro-

posed as an effective approach to accelerate the fault locationfunction of the FLISR process. As the first contribution of thispaper, prevalent FI placement problem formulation is extendedby incorporating the available protection and control deviceswith respect to operation uncertainties under contingencies.To such aim, the customers’ average restoration time index(CARTI) is formulated and applied to the problem. As thesecond contribution of this paper, a multi-objective solutionapproach is employed via a particle swarm optimization (PSO)based algorithm, to simultaneously minimize the indispens-able economic and technical objective functions. Moreover, afuzzy decision making method is contributed to select the bestresult among the obtained Pareto optimal set of solutions. Inaddition, the impact of network topology changes due to feederreconfigurations on the FI placement problem is investigated.Finally, bus number four of the Roy Billinton test system(RBTS4), as well as a real-life distribution network is used todemonstrate the effectiveness of the developed model, followedby presenting the results and comparing them to those of priorresearch.

II. PROBLEM FORMULATION

The aim of the optimal FI placement problem is to find a setof FI locations which simultaneously minimizes the objectivefunctions with respect to the following presumed installationrules [10], [15]:• Feasible candidate locations for installing FIs include be-ginning of each lateral as well as both upstream and down-stream of buses located at the main feeder, except normallyopen tie nodes [Fig. 2(a)].

• Remote-controlled switches (RCSs) and the RCCBs con-tain fault detection and related communication interface;thus it is neither necessary nor economical to install FIs inpresence of these devices [Fig. 2(b)–(d)].

Fig. 2(a) shows a whole set of candidate locations which sat-isfy the first installation rule. The second rule invokes to selectthe candidate locations in Fig. 2(b)–(d). As Fig. 2(b) and (d)show, the candidate locations are decreased in presence of RCSand RCCB; however, the manual switches possess no effects onthe possible candidate locations, as Fig. 2(c) shows.

A. Economic Objective Function

The prevalent FI placement cost function includes investmentand maintenance costs of FIs accumulated with customers’ in-terruptions costs within the planning horizon. In this paper, theeconomic aspects of FIs deployment is formulated as a mixedinteger nonlinear programming problem through (1)–(4):

(1)

(2)

(3)

(4)

Economic objective function of the problem is representedin (1), where the first term shows expected outage cost to cus-tomers throughout the planning horizon which ismodeled via (2) [12], [15]. In (2), the annual expected outagecosts imposed by the customers located at each load point arecalculated by considering all possible contingencies. Moreover,



different types of customer service priorities are met by as-suming weighting coefficient through [6], [15]. In order toincorporate the time value of money, is applied to (2), andcalculated as (4) [21]. The second term of (1) considers total in-vestment and maintenance costs of FIs and calculates via (3).

B. Technical Objective Function

As this paper investigates the FI placement via a multi-objec-tive approach, SAIDI andCARTI are considered as the technicalobjectives and described as follows:1) System Average Interruption Duration Index (SAIDI):This index represents average interruption duration of cus-tomers served during a year. It is determined by dividingthe sum of all customer interruption durations by thenumber of customers served during a year, as follows [1]:

(5)2) Customers’ Average Restoration Time Index (CARTI): Asthe term described in (2) can approximately be found bymultiplying to customers interruption cost, ob-jectives , and seem to have high homogeneity thatmay harm the multi-objective solution method. Hence, inorder to efficiently investigate the effects of the multi-ob-jective approach on FI placement, CARTI is introduced asa new objective function. In comparison with the prevalentsystem and customer reliability indices, it enables better in-corporation of the direct effects of available protection andcontrol devices as well as their accurate operation proba-bilities on the FI problem:

(6)The numerator and denominator of (6) express total cus-

tomers’ restoration time and total number of load points,respectively; minimizing CARTI improves system restorationand reliability of service. It might be helpful to mention thatrestoring a load point can be obtained automatically, manuallyor after repairing the faulted zone [22]. Hence, the customers’restoration time varies based on the best possible restorationaction, all classified into three categories, as follows:• Restoration time associated with automatic switching: If aload point can be restored by successful operation of theexisting RCSs , the restoration time depends on auto-matic switching , otherwise it is equal to the manualswitching time , as represented in (7):

(7)

• Restoration time associated with manual switching: Therestoration process is accomplished manually while eitherthe automation procedure fails, or automatic restoration fa-cilities are not available. In such case, the manual restora-tion time affected by the deployed FIs is expressed in (8):

(8)

Fig. 3. Simple network to illustrate the restoration times.

Fig. 4. Restoration times of each load points obtained by different methods.

The first term of (8) represents the time elapsed before thecrew reach to the outage area in field. The time requiredby crew for patrolling the outage area to locate the exactplace of the fault is considered in the second term of (8),which depends on probability of successful operation ofthe IT infrastructure. The last term of (8), represents thetime associated with manual switching action carried outafter locating the exact place of the fault.The simple network shown in Fig. 3 is employed to explainthe calculation process of (8). For instance, if a fault occursin line 3, which is located in zone 2, the service forand can be manually restored. In this situation,and are calculated using (8). The first and the thirdterms of (8) depend on crew dispatching and switchingaction time, while deployed FIs have significant impact onthe second term. The second term of and is equalto , since the faulted zoneconsists of lines 2 and 3.

• Restoration time associated with repairing: A set of cus-tomers which cannot be restored by automatic/manualswitching should wait until the faulted zone is repaired;hence, total restoration time of such customers is calcu-lated as (9):

(9)

C. Discussion on the Objective Function Formulation Choice

In order to properly illustrate effectiveness of the proposedapproach formulated in (7)–(9), the restoration times for eachload point of Fig. 3 are calculated and compared to those of [15].While the proposed method in this paper is based on conditionalprobabilities of switching, the proposed method of [15] relayson a fixed time for average fault detection duration. The restora-tion times of load points are shown in Fig. 4. As seen in thisfigure, the simplifying assumptions considered in the literature[15] caused almost 2%, 4.6%,, and 4.5% errors in restorationtimes of , , and , respectively. Hence, the pro-posed method devotes an effort to obtain more accurate results



for restoration times within practical projects. The detailed dataand calculations of this illustrative example are given in [23].

III. SOLUTION APPROACH

A. Multi-Objective Particle Swarm Optimization (MOPSO)

In this paper, the single/multi-objective optimization pro-cedure is implemented by PSO/MOPSO. PSO is a populationbased stochastic optimization technique developed by Kennedyet al. which roughly models the social behavior of swarms foroptimization of continuous nonlinear functions [24], [25]. Inthis algorithm, the particles which represent possible solutionsfly through the problem space to find the best solution [26].A general multi-objective problem can be formulated as fol-

lows [27]:

(10)

(11)

(12)

(13)

where is the number of objective functions, is the numberof inequality constraints, is the number of equality constraints,and are the boundaries of the th variable [28].In multi-objective version of PSO, the particles trace the best

location in their paths. In contrast to PSO, there is no single“best” solution to track [26]. Hence, particles must considertheir own non-dominated solutions as well as one ofthe non-dominated solutions the swarm have obtained so far

when updating position. The mathematical model ofparticles velocity is as follows [24]:

(14)

(15)

where represents the velocity of particle at iteration ,is the inertia weight, , and are weighted constant confi-

dents called cognitive social parameters, respectively, is arandomnumber in[0,1], showsthepositionofparticle at it-eration , and indicates one of the non-dominated solutionsthe swarm has obtained so far. The first term of (14), providesexploration ability for PSO; the second and third terms con-tribute to the algorithm’s exploitation of known good solutions.The positions of particles are manipulated according to (15)

during iterations until stopping criteria are met. Upon comple-tion of the generations, Pareto-optimal set of non-dominated so-lutions is achieved.

B. Fuzzy Decision Making

The Pareto-optimal set of solutions obtained by solving amulti-objective problem includes several solutions; where noneof which possess a priority to the others [29], [30], since the solu-tions are partially ordered [31]. Fuzzy decision making methodis known as an effective approach to select the final solutionfrom the set of non-dominated partially ordered solutions.

Based on the fuzzy set theory, while minimizing objectivefunctions, each of them should be normalized through (16) toachieve a linear membership function [30]:

(16)

(17)

where , are absolute maximum and minimum valueof objective within the non-dominated set of solutions, respec-tively. Equation (17) represents normalized membership of theth non-dominated solution. In order to select the best compro-mising solution, the result associated with the maximum nor-malized membership value is employed [30].

C. Proposed Solution Method

Aimed at solving the multi-objective FI deployment throughMOPSO, the following codification is employed:

(18)

(19)

In the above equations, represents the particles positionvector, where is a binary decision variable that is equal to1 if an FI is installed at candidate location , and 0 otherwise.The MOPSO algorithm is conducted based on [24], [26] and

random initialization is applied to the problem; over the courseof iterations, the velocity of particles and their positions are ad-justed by (16) and (17). Then, all non-dominated solutions arearchived. The algorithm is terminated when a maximal numberof iterations have been produced. The overall process of the de-veloped method is as follows:Step 1) Technical and economic information is received;Step 2) The set of candidate locations is produced ;Step 3) Particle positions and their velocity are generated/

updated ;Step 4) The objective functions are evaluated [(1), (5), and

(6)];Step 5) Searching for non-dominated solutions;Step 6) Find both local and global bests for each particle;Step 7) Steps 3–6 are repeated until the stopping criteria are

met;Step 8) The best compromising solution is selected by the

fuzzy decision making through (16) and (17).It should be noted that the FIs can be regarded as reinforce-

ment devices for the FLISR process of DAS, which leads to dis-tribution network reliability improvement, by accelerating thefault location process. As the unfeasible solutions are chiefly as-sociated to devices of the legacy distribution system, there areno unfeasible solutions in FI deployment problem [22].

IV. EXPERIMENTAL RESULTS

In order to solve the FI deployment problem by the proposedmethod, the proposed method is realized as software within auser-friendly environment. By providing the GIS readymap andentering technical and economic information, the user can seethe FI placement results.



Fig. 5. Single-line diagram of RBTS4 [33].

To evaluate the effectiveness of the proposed method, theproblem is solved for bus number four of the Roy Billinton testsystem (RBTS4) in several scenarios. It includes three supplypoints (substations), seven feeders, 38 load points, and 4779customers [32], [33], as Fig. 5 shows. Five types of customers(including residential, commercial, industrial, public customers,and critical customers) are considered in this study, the same as[6] and [15]. While line lengths and failure rates are given in[32], the line repairing time is set equal to three hours.Moreover, it is assumed that the probability of successful op-

eration of RCSs, fuses, RTUs, control center, communicationinterface and feeder protection relays are 0.985, 0.90, 0.98, 0.98,0.996, and 0.995, respectively [2].Moreover, the auto-switchingtime and manual switching times are set equal to 30 s and 1 h,respectively. Total installation costs of a remote access FI areassumed US$ 1000 [15]. The planning horizon, inflation and in-terest rates are considered 10 years, 6%, and 7%, respectively.Technical specifications of the computer used for simulationsare Centrino1.8GHz CPUwith 1 GB of RAM. The detailed datalisting may be obtained from the authors, by request.In this section, the main contributions of this paper are numer-

ically studied in three scenarios. The first scenario investigatesthe capabilities of the proposed method considering prevalenteconomic functions. It also explores the effects of available con-trol devices on the optimal FI locations. The second scenariois devoted to illustrate the effects of multi-objective approachon FI placement problem. To do so, several combinations of, , and are assumed as objective functions

and the obtained results are compared to those of the first sce-nario. Finally, the third scenario illustrates the performance ofthe proposed approach to find the best layout within a real-lifedistribution network located in northwest of Iran, followed byinvestigating the effects of network reconfiguration on the FIs’arrangement.

A. First Scenario

In this scenario, the FI placement problem is solved via asingle-objective PSO based algorithm which minimizes theprevalent cost function represented in (1). The problem issolved for two different situations considering/neglecting theavailable control devices as represented below:Case 1) Considering the available control devices;Case 2) Neglecting the available control devices.

TABLE IOPTIMAL NUMBER AND LOCATIONS OF FIS IN THE FIRST SCENARIO

TABLE IIRELIABILITY INDICES FOR CASE 1 AND CASE 2 OF RBTS4

Obtained results of such examination are reported in Tables Iand II. As it can be seen in Table I, while the optimal layoutassociated to “Case 1” reaches by 22 FIs, the algorithm proposes35 FIs for “Case 2” which is almost 37% more than “Case 1”.Optimal values of objective function are US k$ 630.84,and 1184.75 for the “Case 1” and “Case 2”, respectively.To properly show the effects of the existing control devices

on FI placement, economic objective function associated to in-stalling different number of FIs in the third feeder of RBTS4 isfound and shown in Fig. 6. The optimal FI locations of “Case 1”are 21U, 26D, 27D, 28U in which “U” and “D” indicate “Up-stream” and “Downstream” of a line section, respectively. Theoptimal FI layout of “Case 2” has 23U and 25D besides thoseof “Case 1”. It can be seen that available control devices havesignificant effects on the FIs placement problem.Reliability indices of the RBTS4 before and after distributing

the FIs for both cases are presented in Table II. It might behelpful to mention that reliability indices of the existing net-works in “Case 1” and “Case 2” are different due to consid-



Fig. 6. Economic objective function versus various numbers of FIs for “Case1” and “Case 2” in feeder 3 of Fig. 5.

TABLE IIICOMPARISON BETWEEN FI PLACEMENT RESULTS

OF DIFFERENT METHODS FOR RBTS4

Fig. 7. Trajectory of best solution of PSO and GA for RBTS4.

ering/neglecting the available control devices. Table II showsthat the optimal FI deployment of “Case 1” results in almost15% reduction in both and CAIDI. The decrease in

is about 12% during the planning horizon, which notonly compensates the investment and maintenance costs, butalso brings almost US k$ 62 benefit; hence, it seems that theFI placement is economically justifiable for distribution utility.Besides these, in order to investigate optimality of the de-

veloped algorithm, the problem is also solved by the GA [14].Each algorithm is conducted for 20 independent runs and ob-tained results are shown in Table III. As it can be seen in thistable, although fitness values associated to the best solution arethe same, average results of the PSO based algorithm are betterthan those of the GA. In addition, trajectory of the best solutionfor each algorithm is shown in Fig. 7. As shown in this figure,both methods could find the best solution; however, the PSOconverges at the 852nd generation while GA converges at the945th generation.In the above section, the FI placement is solved to find the op-

timal layout which minimizes the prevalent cost function shownin (1). To consider other objectives reported in the specializedliterature in presence of existing remote-controlled devices, theproblem is conducted to minimize with respect to max-imum investment cost set equal to US k$ 22. It might be helpfulto mention that the maximum investment cost is set according tothe total installation cost associated to “Case 1” of Table I. Ob-tained results of this test reveal that the proposed FI locations

are changed by almost 60% in comparison to those reported inTable I. Moreover, reliability indices are significantly altered, asshown in Table IV.It can be seen in Table IV that minimizing conse-

quences higher reliability level as regards planning profit is de-creased about 63%. Thus, although optimal FI placement byminimizing results higher reliability level, it is not aseconomical as the obtained layout reaches by minimizing thecost function. In other words, the single objective optimizationof FI placement indicates that the FI locations are highly de-pendent to the selected objective function. Hence, it seems thata compromise between the technical an economic objectivesmight be helpful to find the optimal FIs arrangement.

B. Second Scenario

In this scenario, a multi-objective optimization based ap-proach is applied to the FI problem to simultaneously minimizethe cost function represented in (1) as well as the technicalobjectives formulated in (5) and (6). Fig. 8 depicts the ob-tained Pareto-optimal set of solutions while minimizingand ; the best compromised result among the obtainedsolutions is selected by means of the introduced fuzzy decisionmaking method and reported in Table V. As it can be seen in thistable, 33 FIs are selected which lead to US k$ 640.94, 0.34522(hr/yr) for and , respectively. In comparison with Table I,while 13 FIs are located at the same location, 20 further FIsare added to the network. Reliability indices associated to thisconfiguration is represented in Table IV; it can be seen thatin contrast with the situation in which is minimized, the

and are decreased by 0.3%, and 9.1%,respectively; however, the current solution has increased totalinstallation cost by 33.3%.These results clearly illustrate that theproposed solution approach could find a proper compromisedresult for the FI problem in case of minimizing and .However, considering the obtained results shown in Table IV,

the objectives and seem to have high homogeneity whichmay harm the multi-objective solution method. Hence, in orderto efficiently investigate the effects of the multi-objective ap-proach on FI placement, the problem is solved byminimizingand ; the obtained Pareto-optimal set of solutions is depictedin Fig. 9. Moreover, as represented in Table V, the best compro-mised result is obtained by locating 27 FIs within the network,which results US k$ 634.86, 0.81089 for and , respec-tively. The associated reliability indices reported in Table IVshows that the new arrangement of FIs results lower restora-tion time which makes a little increase in total planning benefits(about US $ 6000) in comparison with the previous multi-objec-tive study; however suggested layout is not as reliable as thoseobtained by minimizing and .Table IVwhich summarizes the reliability indices can be used

to compare the single and multi-objective approaches for FIplacement. It shows that the highest reliability level is achievedby just minimizing ; however, this case may not be aseconomical as the others. On the other hand, the result obtainedbyMOPSO reveals that the multi-objective solution method caneffectively compromise between reliability level and the eco-nomic issues.



TABLE IVRELIABILITY INDICES ASSOCIATED TO THE OPTIMAL LAYOUTS FOR RBTS4

TABLE VOPTIMAL NUMBER AND LOCATIONS OF FIS IN THE SECOND SCENARIO

Fig. 8. Pareto-optimal set of solutions while minimizing and .

Fig. 9. Pareto-optimal set of solutions while minimizing and .

Fig. 10 represents the restoration times of each load pointcalculated in different situations. It can be deduced from theseresults that single/multi-objective FI placement efficientlyimprove the restoration times; however, the percentage ofimprovement depends on the load type, as well as restorationtimes of the load point before the FI placement. For instance,restoration times of load points 8 and 25 are 1.5 and 0.6 hoursper fault, respectively. Hence, restoration time of load point8 is more sensitive than the 25th in case of FI placement.

The results show that the distribution utilities may considermulti-objective FI placement while planning their networksaccording to the customer’s choices on reliability.In order to find a comprehensive solution for FI placement,

the problem is solved by simultaneously minimizing , ,and . The obtained Pareto-optimal set of solutions is shownin Fig. 11, and the best compromised results, as well as the as-sociated reliability indices are reported in Tables IV and V, re-spectively. As shown in Table IV, the results reveal that mini-mizing a set of three objectives not only leads to an interactionbetween reliability level and the economic considerations, butalso makes compromise between the prevalent and developedtechnical objective functions. As shown in Table V, total in-stalled FIs are decreased by 9% in comparison to minimizing, and increased by 11% in comparison to minimizing ,which clearly support the claims regarding compromising of

the obtained solutions.and are both depend on the load demand and

number of customers within any load point. Hence, objectivesand seems to have high homogeneity which may harm

the multi-objective solution method. However, neither numberof customers, nor customers’ demand are involved byas it represents average restoration time in an identical prece-dence for each load point based on possible restoration proce-dures. Hereupon, the best layout of FIs may achieve by consid-ering both and as technical objective func-tions, which obviates the ambiguities of previous analyses.

C. Third Scenario

In this scenario, the proposed approach is applied to a real-lifedistribution network located in northwest of Iran [34]. Typi-cally, the Iranian distribution networks possess 20-kV feederswhich are supplied by 63/20-kV substations; low voltage 400-Vground or air networks feed the customers. The GIS ready mapof the under-study network is shown in Fig. 12, which containsone distribution substation that supplies six feeders. The net-work covers an almost rural area consists of 5332customers, with average load of 48 MW; total length of feeders



Fig. 10. Restoration times of load points in various situations.

Fig. 11. Pareto optimal set of solutions while minimizing , , and .

Fig. 12. Real-life radial distribution network located in northwest of Iran.

and low voltage network are approximately 362 and 188 km, re-spectively. While overall information of the implemented net-work is presented in Table VI, complete data listing and loca-tions of available component in GIS ready map can be foundin [23].Based on the proposed installation rules and considering

communication constraint of a real-life distribution network,such as communication range, there are 1330 candidate loca-tion to install FIs. It should be noted that in this simulation

TABLE VIDETAILED INFORMATION OF THE REAL-LIFE DISTRIBUTION NETWORK

candidate locations install FIs not only include the buses whichcontain transformers, but also consider several pole stations.Moreover, in order to illustrate the impacts of possible feederreconfigurations, the FIs placement problem is solved for twodifferent situations neglecting/considering the possible feederreconfigurations as represented below:

Case I) Neglecting possible feeder reconfigurations;Case II) Considering possible feeder reconfigurations.

The single-objective and multi-objective approaches are ap-plied to “Case I”. The multi-objective approach is conductedto reach the optimal layout associated to “Case II”. The ob-tained results are reported in Table VII; as it can be seen in thistable, the algorithm proposes 91 FIs for “Case I”, to minimizethe prevalent cost function. The results associated to this con-figuration show that the optimal FI deployment of “Case I” re-sults in almost 12% reduction in both and .The decrease in is about 14% during the planninghorizon, which brings almost US k$ 546 benefit; implying thatthe FI placement is economically justifiable for the under-studyreal-life distribution network.Further, the optimal FI deployment problem for “Case I” is

conducted to minimize with respect to maximum in-vestment cost set equal to US k$ 91, which is set according to thetotal installation cost associated to minimize the prevalent costfunction. As it can be seen in Table VII, minimizingleads to higher reliability level; however, planning profit is de-creased about 58%, in comparison to the previous single-objec-tive study.In addition, results of applying the multi-objective ap-

proaches to “Case I” are also presented in Table VII. Same asthe second scenario, the reported results clearly illustrate thatconsidering set of three objective functions can find a propercompromised solution for the FI deployment problem.



TABLE VIIRELIABILITY INDICES ASSOCIATED TO THE OPTIMAL FI LAYOUTS WITHIN REAL-LIFE DISTRIBUTION NETWORK

As discussed earlier, the distribution networks topology maychange to satisfy several operational constraints; to illustratethe effects of feeder reconfigurations on the FI placement, theproblem is solved as “Case II”. Although, several possibletopologies exist for this network, the distribution utility typi-cally executes three preferred switching orders, which resultsthree different topologies [23]. The three mentioned config-urations and the multi-objective approach is applied to thiscase, to simultaneously minimize , , and . The obtainedresults are reported in Table VII. As shown in this table, thereliability indices associated to the “Existing system” in “CaseI” and “Case II” are not the same, which is due to the feederreconfigurations applied to “Case II”. The algorithm proposes124 FIs for “Case II”, comparing to the 117 FIs proposed for“Case I”. The results of minimizing the set of three objectivesreveal that the proposed FI locations for “Case II” are changedby almost 24% in comparison to those of “Case I”. Moreover,the optimal layout associated to “Case I” —which is achievedby minimizing , and —results in almost 14% reductionin both and . Whereas, the proposed layoutfor “Case II” leads to approximately 11% reduction in both

and . Therefore, associated to op-timal arrangements of FIs for “Case II” is decreased less than itis decreased in “Case I”. Hence, considering possible networktopologies not only change the FIs arrangement, but also affectthe reliability improvement of proposed layout.The obtained results associated to “Case II” are more realistic

in comparison to those of “Case I”, since the distribution sys-tems topology is changed during operation practice. Hereupon,the best layout of FIs may be achieved by simultaneously min-imizing , , and and considering the possible networktopologies.

V. CONCLUSIONS

The FI placement problem was investigated through a multi-objective approach solved via an MOPSO based algorithm. Theprevalent FI placement problem formulation was extended byincorporating the available protection and control devices withrespect to operation uncertainties under contingencies. Further-more, the CARTI, as a new technical objective, was formulatedin the problem. Effects of the existing control and protection de-vices on the FI problemwere studied on different cases. Also, ef-fects of possible distribution topology changes on FI placementproblem were investigated. Finally, The proposedmethodologywas implemented to a standard test system (RBTS4), as well asa real-life distribution network located in northwest of Iran, and

was studied via several scenarios. The obtained results and dis-cussions reported in the paper show that the proposed approachcan be used as an effective framework for optimal FI deploy-ment of a practical network under possible contingencies.Further research might be conducted to consider the effects

of redundant FIs, and to find the optimal layout of FIs with re-spect to the limitations imposed by IT infrastructure. Moreover,the FI placement problem might be studied in presence of DGsconsidering both online and offline DG operations, as well asuni/bi-directional FIs

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Alireza Shahsavari received the B.S. degree inelectrical engineering from Iran University of Sci-ence and Technology (IUST), Tehran, Iran, in 2010.Currently, he is pursuing the M.S. degree at theUniversity of Tehran, Tehran, Iran.He works as a research assistant at Control and In-

telligent Processing Center of Excellence (CIPCE),as well as System and Machine Research Laboratory(SMRL), School of Electrical and Computer Engi-neering, University of Tehran. His research interestsinclude Smart Grid, planning of electric power distri-

bution, distribution automation system, and artificial intelligence applications topower system optimization problems.

Seyed Mahdi Mazhari (S’12) received the B.S.(Hon.) degree from the University of Birjand, Bir-jand, Iran, in 2010 and the M.S. (Hon.) degree fromthe University of Tehran, Tehran, Iran, 2012 all inelectrical engineering. Currently, he is pursuing thePh.D. degree at the School of Electrical Engineeringof Amirkabir University of Technology, Tehran,Iran.He serves as supervisor at the Research Laboratory

of Power System Operation and Planning Studies,University of Tehran, Tehran, Iran. His research

interest includes planning of the electric power distribution and transmissionsystems, power system real-time analysis and artificial intelligence applicationsto power system optimization problems.

Alireza Fereidunian (M’09) received the M.Sc. andPh.D. degrees from the University of Tehran, Tehran,Iran, in 1997 and 2009, respectively.He is a Post-Doctoral Research Associate the Uni-

versity of Tehran as well as an Assistant Professorat the K. N. Toosi University of Technology, Tehran,Iran. His research interests include Smart Grid, en-ergy distribution systems, and application of IT andAI in power systems. Moreover, he works in complexsystems, systems reliability and human-automationinteractions areas, where he has invented the Adap-

tive Autonomy Expert System (AAES).Dr. Fereidunian is a member of IEEE (and IEEE-SMC-HCI TC member) and

INCOSE (as INCOSE Iran point of contact).

Hamid Lesani received the M.S. degree in powerengineering from the University of Tehran, Iran, in1975, and the Ph.D. degree in electrical engineeringfrom the University of Dundee, U.K., in 1987.Then, he joined the Department of Electrical and

Computer Engineering, University of Tehran, wherehe currently serves as a Professor at the Center of Ex-cellence for Control and Intelligent Processing. Histeaching and research interests is focused on designand modeling of electrical machines and power sys-tems.

Prof. Lesani is a member of IEEE (PES) and IEEE Iran Section.

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