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Fuse cutout allocation in radial distribution system considering the effect of hidden failures
Electrical Power and Energy Systems 42 (2012) 575–582
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Fuse cutout allocation in radial distribution system considering the effect of hidden failures Mojtaba Gilvanejad a,b, Hossein Askarian Abyaneh a,⇑, Kazem Mazlumi c a
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran Substation & Transmission Department, Niroo Research Institute, Tehran, Iran c Department of Electrical Engineering, University of Zanjan, Zanjan, Iran b
a r t i c l e
i n f o
Article history: Received 23 May 2010 Received in revised form 5 April 2012 Accepted 20 April 2012 Available online 5 June 2012 Keywords: Fuse cutout Hidden failure Markov model
a b s t r a c t Among the several components of distribution systems, protection devices present a fundamental importance, since they aim at keeping the physical integrity not only of the system equipment, but also of the electricians team and the population in general. One of the protective devices playing a vital role in overhead distribution lines is fused cutout. At the era of privatized utilities, the protection devices should be allocated and coordinated optimally to reduce capital investments and the system outage costs. Amid this situation, the mentioned type of the protection device (fuse cutout) has not been studied for economical allocation up to now. In this paper, responding to this need, an accurate reliability model of fuse cutout containing hidden failures is figured out in the shape of a new Markov model. This model is used for economical allocation of the fuse cutouts. On the basis of the proposed model, a methodology for economic allocation of fuse cutouts is presented. This methodology involves the worth of energy not supplied (ENS) of the network and makes a balance between the cost of fuse cutout installation and the benefit of ENS decrease because of the minimizing the faulty zone by using more fuse cutouts. The methodology is tested on a sample distribution network as well as on IEEE 6-bus distribution test system (RBTS). Moreover, its capability on decision making about the fuse cutout placement and its simplicity for implementing on MV overhead lines are displayed. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Protective systems are designed to recognize certain types of power system disturbances and to isolate those parts of the system on which the disturbances occur [1]. These systems play a vital role in maintaining the high degree of service reliability required in present day power systems [2]. In reality, however, protection systems may be exposed to two main failure modes, since they can fail either by not responding when they should or by operating when they should not. The reliability of protection relay can be improved by carrying out routine maintenance or by including built-in monitoring and self-checking facilities during the design stages. In recent years, considerable efforts have been devoted to evaluate the extent up which these failure modes can affect the power system reliability [1–7]. In [2], a Markov model which is used to examine the effect of routine tests and self-checking intervals on system reliability is described. The proposed method has been
⇑ Corresponding author. Address: Department of Electrical Engineering, Amirkabir University of Technology, Hafez Street, Tehran, Iran. Tel.: +98 21 64543300; fax: +98 21 66406469. E-mail address: [email protected] (H.A. Abyaneh). 0142-0615/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijepes.2012.04.038
further improved in [3] by dividing the Markov model into two main parts which are devoted to express different situations of power component and protective devices. In [4], the effect of relay coordination methods on reliability indices of an interconnected power system has been studied. The authors evaluated the effect of protection failures on the reliability indices using two separate Markov models, one of them was dedicated to power system components and the other was considered for protective devices. In [1], a Markov model has been proposed which is capable to consider redundant protection systems. Most of the aforementioned papers have been based of two main assumptions. First, the power system has been assumed to be interconnected. Furthermore, protective relays have been considered as the main protection device to protect such systems [8,9]. However, the mentioned assumptions are of limited relevance in case of distribution systems which have usually radial configuration with fuse cutouts as the main protective devices. This device is more economical and, therefore, is favored on a distribution level. The fuse cutouts have some different features comparing to the protection relays which made it special in reliability modeling. For example, the rate of hidden failures of the fuses is lower than the rate of hidden failures of relays. The reason is the difference between the nature of relays and fuses. The relay protection
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system contains several parts such as transducers (CT1, VT2), circuit breaker, relay, etc. Each of them has its own failure rate and makes the total failure rate noticeable [10]. However, the fuse only operates as a single element which is in series with the other components of the circuit. Therefore, only its own failure rate is taking into account [11]. Therefore, there is a knowledge gap to find an appropriate Markov model applicable for these devices. This paper tries to focus on this topic by bringing two main contributions into the existing literature. First, a Markov model is proposed which is capable to consider the distinct reliability features of fuse cutouts. Then, the proposed Markov model is used to allocate fuse cutouts in MV overhead lines by using the well-known reliability index of energy not supplied (ENS). Finally, an economic analysis is presented to investigate cost-saving benefits of the proposed model in protection device allocation procedure. 2. Problem statement The ultimate purpose of protection is to provide power system reliability. It might seem that protection of equipments is the purpose of protection systems, but this misses the global picture. It is the integrity of the system which is being protected [5]. To design protection scheme of a specific system, various devices become available and may have significant cost. Thus, the evaluation of the impact of applying new device as well as its cost effectiveness, is required. Overcurrent relays (OCRs) and directional overcurrent relays (DOCRs) are widely used for the protection of radial and ring sub-transmission and distribution systems [12]. In some cases, differential protection scheme is also used in distribution feeder protection system due to its fast operation [13]. In distribution systems, these relays have relatively considerable cost comparing to other devices and mainly are used in the beginning of the feeders at the primary substations [14]. They have been studied in literature in several areas of research such as optimum coordination and advanced coordination methods, optimum routine test and self checking intervals, coordination of instantaneous trip functions with other protective devices like current-limiting fuses, etc. [2,15–18]. Reclosers and sectionalizers, which are other distribution protective devices, also have relatively high expense and are usually installed on the beginning of distribution branches which serve sensitive loads. They are also used for improving the reliability of the system especially in the DG-enhanced distribution networks with preserving the protection system coordination [19]. But, there is a protective device in distribution system which is inexpensive and widely used along the distribution feeders and branches and also for protecting the pole-mounted transformers. This device is fuse cutout and as mentioned above, has two different usages; one for protecting the pole-mounted transformers. Therefore, it is located at the primary side of each aforementioned transformer. The other is in feeder protection where the fuse cutout is placed at the joint points and protects the main feeder from outages initiated at the feeder taps. These two usages are shown in Fig. 1. The location of fuse cutouts which are used for transformer protection is completely definite. They are installed at the primary side of MV3/LV4 transformers. The rating of these fuses is chosen based on the standard list according to its protected MV/LV transformer capacity [20]. Such fuses should be coordinated with the upstream fuses and overcurrent relay exists at the beginning of the MV feeder. But, here questions raise as: ‘‘Where is the proper and cost 1 2 3 4
Current transformer. Voltage transformer. Medium voltage. Low voltage.
Fig. 1. Types of fuse cutout usages: (a) at joint point and (b) primary side of transformer.
effective place for the joint point fuse cutouts?’’, ‘‘Should they be installed at the beginning of every joint point along the distribution feeders?’’ For example, if a branch has a few meters length and only one or two small transformers are being installed along this branch, does it need an individual fuse cutout for protecting itself or an upstream protection device could provide the required protection? In an increasingly competitive market environment where companies emphasize cost control, correct modeling and allocation of fuse cutouts which can lead to a cost-saving approach become important. To answer the aforementioned questions, it is necessary to evaluate the reduction of frequency and duration of outages happening in a specific period of time and for a specific set of customers. Placing the fuse cutout at the joint points leads to decrease the faulty zone; therefore, the resultant benefit should be compared to the investments that need to be expended for the required number of fuse cutouts. Achieving this aim, the reliability modeling of fuse cutouts should be studied and the performance of fuse for providing an economical reliable distribution network should be assessed. Markov model of a fuse is a suitable approach for reliability modeling in Monte-Carlo simulations which will be expressed in the following section.
3. Fuse Markov model The first step in determining the reliability model for any system is to understand its function, the constraints under which it operates, and the root cause of the failure [2]. Here, to better understand the reliability modeling of the fuse cutout, the distinctive features of the fuse in comparison with the protective relays are discussed. There have been a number of models established to facilitate the reliability evaluation including protective relay failures. The models of current-carrying component paired with its associated relay protection system are proposed in literature [1–3,6]. Also, a model that separates the component Markov model from the protection Markov model has been suggested in [4]. In that paper [4], the model is capable of analyzing the different reactions of overcurrent relays that have been installed on the both sides of a line, when a fault occurs in an interconnected system. If the fuse is placed as the protective device in an electrical system, there is a reason that makes the Markov modeling of the fuse differ. The important point is that the fuse has not an inspection state when the current-carrying component is up. This is an intrinsic feature of the fuse which is placed in series with the line while the network operator is not able to test this device during the operation period of the network. The reason is the need for load deenergizing during the test process and detrimental effects of fuse tests leading to fuse burning and fuse-link removal after the test completion. Thus, the inspection state will never exist in the
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reliability model of the fuse. It leads to the fact that the probability of true or false operating of the fuse cutout can only be considered when the component fails. In other words, it is possible to construct a Markov model of the fuse cutout only involved when the current carrying component fails. The Markov model of fuse cutout having five states is shown in Fig. 2. As mentioned above, all of these states, except state 1, are probable to occur only when the current carrying component is down. The states in Fig. 2 are as follows:
State State State State State
1: 2: 3: 4: 5:
the fuse cutout is carrying the current. the fuse link of fuse cutout starts to melt. the fuse cutout trips. the fuse cutout has experienced ‘‘failure to operate’’. backup protection trips.
The notations in Fig. 2 are:
lI1 lI2 wN wB k kp1 kp2 kMC
sum of repair rates relevant to component and fuse cutout in normal (main) protection zone; sum of repair rates relevant to component and fuse cutout in backup protection zone; normal tripping rate to isolate component; backup tripping rate to isolate component; failure rate of component; failure rate of fuse cutout to expose to ‘‘undesired trip’’; failure rate of fuse cutout to state of ‘‘failure to operate’’. rate of mis-coordination of protection
577
the fuse cutouts in a distribution system considering the economical and technical features. Human errors are not included in the study hence, the mis-coordination factor is neglected. The miscoordination effect can be studied in each utility by applying the related kMC in the model of fig. 2. The remaining parts will be the same as the approach presented in the following sections of the paper. In general, in normal operating condition, load currents are normally distributed in the network branches and fuse cutouts conduct the current in a normal manner. In this situation, fuse cutout is in state 1 (Fig. 2) where constitutes most time of operation period. When a fault occurs in the distribution system, huge amount of current passes through the fuses and makes it start melting (state 2). If the fuse cutout trips correctly, the fault will be isolated and fuse will be placed in state 3. Otherwise, if the fuse fails to operate in a rational time (state 4) then backup protection has to trip immediately (state 5). After isolating the fault, either in normal zone or backup zone, the repair action starts and returns the system to a good state (state 1). In some instances, the fuse trips erroneous and makes the system to be de-energized unreasonably. In such situations, system goes directly from state 1 to state 3. In this way, the proposed model in Fig. 2 could model the different states which could happen in the fuse cutout protected distribution network. This model will be used in reliability analysis for the fuse allocating methodology.
4. Fuse cutout allocation The reason of selecting two different coefficients for main and backup tripping and repair rates of protection system is the differences that exist between the operation speed and fault locating time in these two protection zones in reality. The backup protection (relay/fuse) should act with delay when the main protection (fuse) has no action. After tripping the backup protection, fault locating process takes longer time because the zone of backup protection is more extensive than the main protection zone. The hidden failure is not considered for the backup protection because the occurring of hidden failures in the set of main and backup protections is rare. In the model depicted in Fig. 2, the mode of influencing the miscoordination of protection system on the reliability model has been shown. As shown in this figure, the mis-coordination makes the backup protection operate before the main protection. The rate of mis-coordination events greatly depends on the skill of protection staff. In this paper, we want to locate the proper place for
Fig. 2. Markov model of fuse cutout.
4.1. Fuse cutout allocation methodology Regardless the overcurrent relays which exist in sub-transmission substations and being installed at the beginning of the medium voltage feeders, the MV overhead lines are also protected by fuse cutouts. These fuses are mainly placed at the joint points along the feeders. As mentioned before, determining the number of fuses that should be installed along a feeder is a problem. This problem is solved through the cost and benefit balancing. Costs contain capital expenses to be invested for fuse cutouts and benefits contain revenues obtained from the outage and maintenance cost reduction. In order to estimate the outage costs of a distribution system, the reliability analysis is required. This estimation could be done by Monte Carlo iterative approach. The Markov Model of a fuse cutout which is offered in Fig. 2 has been used in this Monte Carlo simulation. It is necessary to mention that the reliability analysis should be done for each fuse cutout removal on candidate places of the related MV network. In this way, the analysis provides the amount of energy not supplied that fuse could decrease. Then, the worth of this additional sold energy is compared with the investments that are required for the fuse cutout installation. If the fuse cutout installation has the economical justification, the benefit of outage reduction during the life time of the network will compensate the investments for fuse cutouts at installation time and will obtain some financial benefits for utility owners. Otherwise, the related place is not proper for the fuse cutout placement. Since in real networks, multiple faults may concurrently occur in different parts of the network, the reliability analysis should enable to model this type of failures. However, the reliability calculations should only consider one failure whenever the multiple failed components devote to a unique protective device. Furthermore, the magnitude of the energy not supplied (ENS), that occurs in a feeder because of the feeder failures, depends on two main factors: (a) the feeder load, and (b) the feeder length. The manner of affecting the load amplitude is that, whatever the
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feeder load increases, the occurrence of any outage makes more customers be de-energized and the amount of energy not supplied increases. The other factor, ‘‘the feeder length’’, causes that whatever the length increases, the probability of fault and outage occurrence increases which will lead to energy not supplied increment. These two factors obviously show that making decision about placing or not placing a fuse cutout along a feeder strongly depends on the feeder and load configuration. Based on the above discussions, it is proposed a method for the fuse cutout allocation as the following steps: a. Fuse cutouts are placed at every beginning and middle points of medium voltage overhead lines. b. For each fuse removal, the reliability analysis is performed and the rate of ENS changes is calculated. c. If the cost of ENS increment due to fuse cutout removal was less than the investment that is required for the fuse installation therefore, the fuse cutout should not be installed. Otherwise, fuse cutout must be installed. The middle point of the feeders is considered as a typical place for sectionalizing type of fuse cutouts. This type of fuse can be installed anywhere between start and ending points of the feeder. 4.2. Formulation of fuse Markov model in Monte Carlo simulation The probability of residing in each state of Markov model shown in Fig. 2 in an analytical way can be obtained via the theory of Continuous Markov Processes [1]. The steady state probabilities of the proposed Markov model can be calculated by solving the following equation:
Pa¼P
ð1Þ
where a is the state transitional probability matrix:
2
0 6 0 6 6 a¼6 6 lI1 6 4 0
k
kp1
0
0
wN
kp2
0
0
0
0
0
0
lI2 0
0
0
0
3
kMC 7 7 7 0 7 7 7 wB 5 0
ð2Þ
And P is the row vector of state probabilities:
P ¼ ½Pstate1 Pstate2 Pstate3 Pstate4 Pstate5
ð3Þ
Using Eqs. (1)–(3), it is possible to obtain the average values of the probabilities devoted to reside in each state. In our approach, however, for closing the results to reality (real values, not average values), the non-sequential Monte Carlo simulation is used to achieve the more accurate results. Since the failure rates of the network devices and protection systems are supposed to be constant, their failure probability corresponds to the exponential probability density function. The failure probability of a network component according to exponential density function is as follows:
pðtÞ ¼ kekt ;
tP0
ð4Þ
where t is the age of the network component in year. The probability of multiple faults for a single component during a year is fulfilled by the Poisson process as follows:
pðkÞ ¼
kk ek ; k!
failure probability based on Eqs. (4) and (5), the related component is considered as a failed component. This procedure is also accomplished in this paper for modeling the component repair process as well as the protection system malfunctioning in Monte Carlo simulation; through replacing k in Eqs. (4) and (5) with related failure or repair rates (e.g. lI1, lI2, kp1, etc.). 4.3. Formulation of allocation criteria If annual failure rate of an overhead line is shown with k then, the average cost of annual energy not supplied CENS-a can be calculated through the following equation:
C ENS-a ¼ kLP av e T outage C ENS
ð6Þ
where L is the length of feeder, Pave is the average active power of loads which have experienced the outages, Toutage is the average outage time and CENS is the worth of energy not supplied unit (i.e. $/KW h). As mentioned before, Eq. (6) is an average value and only takes into account the failure rate of the feeder. In order to involve the failure rates of protection system in each state of Markov model shown in Fig. 2, a Monte Carlo simulation which utilizes random number generators to model stochastic failure occurrences in component and protection areas is employed and the related outage time and energy not supplied are calculated simultaneously. Therefore, the annual cost of ENS is calculated as follows:
C ENS-a-tot ¼ ENS C ENS
ð7Þ
where ENS is total energy not supplied which consists of energy not supplied because of occurring faults in overhead lines and energy not supplied because of failures in protection system and CENS-a-tot is its related costs. Since CENS-a-tot is a cost that happens every year during the network lifetime so, it should be converted to the present value. With the aid of this conversion, it makes possible compare ENS cost with the investment cost which is being spent at the present time. To achieve this aim, the following equation is used [21]:
P:W: of C ENS ¼
ð1 þ RORÞn 1 C ENS-a-tot ROR ð1 þ RORÞn
ð8Þ
where P.W._of_CENS is the present worth of the whole energy not supplied during the lifetime of the network, ROR is the rate of investment return that the utility owner expects (rate of interest) and n is the lifetime of the network in years. Proper installation of fuse cutouts along the feeders could reduce CENS-a-tot and make some benefit for the utilities. In general, if the following equation has a positive value, the fuse installation at the relevant point is not required. Otherwise, if it has a negative value, the fuse cutout is required.
NB ¼ C cap ½P:W: of C ENS ð2Þ P:W: of C ENS ð1Þ
ð9Þ
where NB is the net benefit of fuse removal and Ccap is the capital costs required for fuse cutout placement. Also (1) and (2) indices represent the total worth of feeder ENS before and after fuse removal, respectively. In the next section, the proposed methodology is implemented in sample networks and the results are discussed in detail. 5. Study results
k ¼ 0; 1; . . .
ð5Þ
where k is the number of component failures in a year. In Monte Carlo method, a random number generator that produces random numbers between 0 and 1 is used for assigning a random failure probability to each network component. Whenever the random assigned number was smaller than the calculated
5.1. Simple test case The allocation methodology is demonstrated in a constituted system of seven branches and seven transformers which is also used in [22]. In this system, each transformer is defined with the load power of 150 KW, 1-km branch length, 13.8 kV of operation
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M. Gilvanejad et al. / Electrical Power and Energy Systems 42 (2012) 575–582 Table 3 Network ENS results. Branch no. of removed fuse
No fuse removal
1–2
3–4
3–5
1–6
6–7
ENS (kW h) (Monte Carlo) Priority of removal
2815
3630
3230
3230
3490
2960
–
4
2
2
3
1
Fig. 3. System example.
and a failure rate equal to one fault a year (k = 1). The re-establishment time (Toutage) is 55 min. Fig. 3 shows the described system. In Fig. 3, triangles contain electrical loads and circles are fuse cutouts. The failure and repair probability distribution functions of the network devices are formed according to their related failure and repair rates and then are used in Monte Carlo simulation. For example, the probability distribution functions related to each branch failure will be equal to:
pðtÞ ¼ et ;
tP0
And,
pðkÞ ¼
e1 ; k!
k ¼ 0; 1; . . .
ð11Þ
In [22], the aforementioned distribution system is used for implementing the protection coordination algorithm and the average energy not supplied of the system is calculated as a criterion for priority of fuse removal. The calculated ENS has not considered the hidden failures of the fuses. In that paper, since the goal is the coordination of protection devices, an estimate of average values of ENS is useful for prioritizing of fuse removal; and fuse allocation is not the subject of the study. In our study, however, in order to economically evaluate the fuse cutout allocation, the real values of ENS are needed to be taken into account. Thus, it is required that more real parameters affecting the operation of real network be considered in reliability studies such as undesired trip and failure to operate of protection systems, re-establishment time of main and backup protection, etc. In this study, the re-establishment time of the network in backup zone of the protection system is considered as twice (110 min) of outage time in the main protection system (55 min). The value of other parameters for the cutout fuse Markov model which is used in simulations is presented in Table 1. In Table 2, to compare the results of analytical (Ref. [22]) and iterative Monte Carlo approaches, the quantities of ENS for different fuse removals of sample network are presented. Table 1 Fuse cutout reliability data [9]. kp1 (failure/year)
kp2 (failure/year)
WN (operation/h)
WB (operation/h)
0.00374
0.00876
36,000
18,000
Table 2 Comparison of ENS results.
a
Fig. 4. First candidate of fuse cutout removal.
ð10Þ In Table 2, in order to keep similarity with assumptions in [22], the protection failure rates are not considered for the Monte Carlo simulations; therefore, no backup protection exists. As can be seen, the results are close to each other. But there are some differences between the quantities which despite low magnitude, is important when the size of the network extends and the number of cutout fuses increases. Monte Carlo estimates the reliability indices based on the stochastic approach which corresponds to the network behavior essence. Therefore, its results are more useful for technical–economical evaluations. The ENS values resulting from the proposed Markov model (Fig. 2) in Monte Carlo simulations which include all of the parameters shown in Fig. 2, are presented in Table 3. According to the table, the first priority of fuse cutout removal is fuse No. 6 (Fig. 4). The quantities that have been inserted in Table 3 are annual values and to implement the proposed methodology for the fuse allocation, the present worth of ENS cost during the life time of the system should be calculated before and after fuse cutout removal. So
PW of C ENS ð1Þ ¼
1:120 1
0:1 1:120 PW of C ENS ð1Þ ¼ 2396:6$ PW of C ENS ð2Þ ¼
1:120 1
0:1 1:120 PW of C ENS ð2Þ ¼ 2520:0$
2815 0:1 ð12Þ 2960 0:1
Eq. (12) is computed according to (8), where ROR and n are assumed 0.1 and 20. CENS is also assumed 0.1 $/kW h. The capital cost of the installation a series of three phase fuse cutouts is supposed 170 $. Now, it is possible to calculate the introduced criteria (Eq. (9)) to judge about preserving or removing the fuse cutout which can be placed on branch (6–7):
NB ¼ 170 ½2520:0 2396:6 ¼ 46:6$
Branch no. of removed fuse
No fuse removal
1–2
3–4
3–5
1–6
6–7
ENSa (kW h/year) (analytical) ENS (kW h/year) (Monte Carlo) Priority of removal
2755.5
3580.2
3169.6
3169.6
3444.2
2893.1
2730
3555
3160
3160
3400
2880
–
4
2
2
3
1
The values of this row is obtained from [18].
ð13Þ
Therefore, Eq. (13) declares that the fuse cutout is not required in branch (6–7) and should be removed. After removing this fuse cutout, the process should be repeated and the removal priorities should be determined again. Results of this stage are presented in Table 4. Two points can be achieved from Table 4. The first one is that the next candidate for fuse removal is fuse No. (3–4) or (3–5). The second one which is more important is that the sequence of fuse removal priorities has changed comparing to Table 3.
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Table 4 Network ENS results after the first fuse removal. Branch no. of removed fuse
No fuse removal
1–2
3–4
3–5
1–6
ENS (kW h/year) (Monte Carlo) Priority of removal
2960
3770
3370
3370
4280
–
2
1
1
3
Now, in order to make decision about the second fuse removal, the worth of ENS increment duo to the eliminating of the fuse of the highest removal priority (fuse No. (3–4)) is again calculated:
PW of C ENS ð2Þ ¼
1:120 1
0:1 1:120 PW of C ENS ð2Þ ¼ 2869:1$
3370 0:1
ð14Þ
Therefore, NB equals to: Table 5 Network ENS results after taking new rates for fuse cutout undesired trip (kp1).
ENS (kW h/ year)
Fuse cutout No. (3–4) preserved Fuse cutout No. (3–4) removed
NB ($)
kp1 = 0.095 (failure/year)
kp1 = 0.95 (failure/year)
3030
3980
3410
4160
153.4
16.8
Table 6 Network ENS results after taking new rates for fuse cutout failure to operate (kp2).
ENS (kW h/ year)
NB ($)
Fuse cutout No. (3–4) preserved Fuse cutout No. (3–4) removed
kp2 = 0.015 (failure/ year)
kp2 = 0.15 (failure/ year)
2950
3590
3320
3780
144.9
8.3
In Table 3, fuse No. (1–6) has higher priority than fuse No. (1–2). In Table 4 fuse No. (1–2) has higher priority for removal. Therefore, it can be concluded that the sequence of fuse removal should be re-evaluated after any fuse cutout elimination.
NB ¼ 170 ½2869:1 2520:0 ¼ 179:1$
ð15Þ
The fuse removal has not any justification this time and fuse cutout No. (3–4) (or (3–5)) should be preserved. Since this fuse was the first priority for fuse removal in the network, the other candidates certainly will not have justification for the removal. In order to more clarifying the influence of a factor which affects the removal or preserving the fuse cutouts during the allocation procedure, some complementary studies are performed here. This important factor is the magnitude of hidden failure rates. The undesired trip or failure to operate of fuse cutouts will result in ENS augmentation in distribution systems. Bigger values of hidden failure rates will result in bigger values of total network ENS. Therefore, the hidden failure rate increment in fuse cutouts may result in more fuse cutout removal action to achieve less ENS value of the network in optimum placement procedure. For example, the second fuse cutout removal action for the test system has not economic justification (Eq. (15)) when its hidden failure rate was according to Table 1. However, its removal will have economic justification if the fuse cutout hidden failure rate has large enough value to make the answer of Eq. (15) positive. In order of evaluation the effect of hidden failure rate magnitude on the economic justification which is investigated in fuse cutout allocation process, some complementary simulations were performed to assess this issue and their results have been reported in Tables 5 and 6. Tables 5 consists the ENS results for changing the undesired trip value of fuse cutouts in the system of Fig. 3 and Table 6 consists the
Fig. 5. Distribution system for RBTS bus 5.
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Fig. 6 illustrates the configuration of the fuse cutout and the disconnector at the joint points [24]. In [23,24], the fuses and disconnectors are assumed to be 100% reliable. But, here in our study, the fuses are not 100% reliable and have failure rates corresponding to the mentioned quantities in Table 1. The reliability data for the 11 kV lines are shown in Table 7 [23,24]. The failure and repair probability distribution function for the network components and protection systems are formed and they are used in Monte Carlo simulation. The failure probability function of the network overhead lines will be equal to as follows:
pðtÞ ¼ ð0:065 lÞ eð0:065lÞt ;
tP0
ð16Þ
k ¼ 0; 1; . . .
ð17Þ
And,
pðkÞ ¼
where l is the length of overhead line in both aforementioned relationships. Now, the fuse cutout allocation methodology is implemented on the feeder F1 of the system shown in Fig. 5. Comparing to the previous sample test, in this case, the disconnectors have been added to distribution system and are included in the reliability simulations. The feeder lengths and loading data are according to [23]. Similarly, the first step is the determination of the removal priorities. This data has been collected in Table 8. The present worth of ENS before and after removing the first candidate cutout fuse (i.e. cutout fuse No. 11) equals to:
Fig. 6. Joint point configuration.
Table 7 Overhead lines reliability data. Failure rate (failure/year km)
Repair time (h)
Switching time (h)
0.065
5
1.0
ð0:065 lÞk eð0:065lÞ ; k!
ENS results for changing the failure to operate value of fuse cutouts in the system of Fig. 3. The first row of ENS results in Tables 5 and 6 devotes to the energy not supplied values of the network which are calculated with new values of kp1 and kp2 before removing the fuse cutout No. (3–4). As can be seen in these two tables, removal of fuse cutout No. (3–4) will have economical justification whenever kp1 and kp2 take large enough values and causes the NB takes a positive value. Furthermore, it is seen that the influence of kp2 (failure to operate) variation on the energy not supplied value of the system is much more than kp1 (undesired trip). In other words, the fuse cutout allocation procedure is more sensitive to kp2 rather than kp1. The ENS is also influenced by two other factors such as load magnitude and feeder length. Changing each of them could change the final decision about the fuse. Hence, in order to show the generality of the proposed methodology, IEEE distribution reliability test system will be studied in the next section.
PW of C ENS ð1Þ ¼
1:120 1
0:1 1:120 PW of C ENS ð1Þ ¼ 2341:3$ PW of C ENS ð2Þ ¼
2750 0:1 ð18Þ
1:120 1
2920 0:1 0:1 1:120 PW of C ENS ð2Þ ¼ 2486:0$ For this state, the NB factor for cutout fuse removal is calculated as:
NB ¼ 170 ½2486:0 2341:3 ¼ 25:3$
ð19Þ
Therefore, cutout fuse 11 should be removed. Prioritizing the fuse cutouts at the next stage after removing the cutout fuse 11 is demonstrated in Table 9. In the case of evaluating the elimination of cutout fuse No. 9, the NB will be equal to 17.3$ therefore, it should be preserved. As the results show, in every distribution system, the problem of placing or removing the fuse cutout on the beginning of the branches should be evaluated case by case. In the two samples which are studied in this paper, one fuse cutouts was been installed improperly. In this way, all of MV overhead lines can be evaluated for the fuse cutout allocation and only the fuse cutouts with technical and economical justifications are installed. The procedure which is described in this paper can help to better modeling of the fuse cutout operation in distribution systems and the network operators can better manage the network investments through optimal placement of fused cutouts in distribution systems where, large numbers of fused cutouts are usually installed.
5.2. IEEE 6-bus test system (RBTS) The IEEE test system for reliability assessment is a 6 bus test system with five load buses (bus 2–bus 6). The distribution network at bus 5 represents a typical urban type network consisting of residential, government and institutional, office and buildings, and commercial customers. The peak load of the distribution system at bus 5 is 20 MW. The distribution network at bus 5 is labeled in detail in Fig. 5 [23]. In this network, a fuse cutout is placed at any join point. Furthermore, a disconnector has been installed after each join point. Table 8 Network ENS results and removal priorities. Branch No. of removed fuse
No fuse removal
ENS (kW h/year) Priority of removal
2750 –
2
3
5
6
8
9
11
3680 6
3680 6
3410 5
3140 4
3010 3
2960 2
2920 1
582
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Table 9 Network ENS results and removal priorities. Branch No. of removed fuse
No fuse removal
ENS (kW h/year) Priority of removal
2920 –
2
3
5
6
8
9
3790 5
3790 5
3540 4
3320 3
3220 2
3140 1
6. Conclusion In this paper, a new Markov model for the reliability analysis of the fuse cutouts has been proposed which considers the hidden failures of the fuses. Also, a methodology has been suggested for the fuse cutout allocation along the medium voltage overhead distribution lines. This methodology gives a simple and useful criterion for decision making about fuse cutout placement. The approach has been tested on two different networks. One of these was a simple constituted network which the methodology is implemented and its concepts were explained. Another one was the IEEE reliability test system and the methodology was discussed for one of its feeders. This network had also disconnectors whose effects are included in the reliability simulations. Fuse cutout allocation results showed that in both sample networks, there was a fuse cutout which was been installed improperly. Therefore, using the proposed methodology in this paper, the reliability of MV overhead lines is maintained at a reasonable level while preserving the protection system adequacy. The results of the implementing the proposed methodology in two sample networks show its capability and simplicity to be applied in real MV overhead distribution lines. References [1] Anderson PM, Chintaluri GM, Magbuhat SM, Ghajar RF. An improved reliability model for redundant protective systems – Markov models. IEEE Trans Power Syst 1997;12(2):573–8. [2] Billinton R, Fotuhi-Firuzabad M, Sidhu TS. Determination of the optimum routine test and self-checking intervals in protective relaying using a reliability model. IEEE Trans Power Syst 2002;17(3):663–9. [3] Yu X, Singh C. A practical approach for integrated power system vulnerability analysis with protection failures. IEEE Trans Power Syst 2004;19(4):1811–20. [4] Mazlumi K, Askarian Abyaneh H. Relay coordination and protection failure effects on reliability indices in an interconnected sub-transmission system. Electric Power Syst Res 2009;79(7):1011–7. [5] Graziano RP, Kruse VJ, Rankin GL. Systems analysis of protection system coordination: a strategic problem for transmission and distribution reliability. In: Transmission and distribution conf, proc IEEE Power Eng. Society; 1991. p. 363–9. [6] Anderson PM, Agarwal SK. An improved model for protective-system reliability. IEEE Trans Reliab 1992;41(3):422–6. [7] APM Task Force on Protection Systems Reliability. Effect of protection systems on bulk power reliability evaluation. IEEE Trans Power Syst 1994;9(1):198–205.
[8] Tuitemwong K, Premrudeepreechacharn S. Expert system for protection coordination of distribution system with distributed generators. Int J Electr Power Energy Syst 2011;33(3):466–71. [9] Aslan Y, Ture S. Location of faults in power distribution laterals using superimposed components and programmable logic controllers. Int J Electr Power Energy Syst 2011;33(4):1003–11. [10] Dong J, Koval DO, Zuo MJ. Impact of circuit breaker failure modes on the reliability of the gold book standard network. IEEE Trans Ind Appl 2005;41(5):1323–8. [11] Cadwallader LC. Electrical power distribution system operating experience review for fusion applications. Fusion Eng Des 2002;63–64:327–31. [12] Birla D, Maheshwari RP, Gupta HO. Time-overcurrent relay coordination: a review. Int J Emerging Electr Power Syst. 2005;2(2):1–13. [13] Bakar AHA, Mokhlis H, Lllias HA, Chong PL. The study of directional overcurrent relay and directional earth-fault protection application for 33 kV underground cable system in Malaysia. Int J Electr Power Energy Syst 2012;40(1):113–9. [14] Lin X, Ke S, Gao Y, Wang B, Liu P. A selective single-phase-to-ground fault protection for neutral un-effectively grounded systems. Int J Electr Power Energy Syst 2011;33(4):1012–7. [15] Razavi F, Askarian Abyaneh H, Al-Dabbagh M, Mohammadi R, Torkaman H. A new comprehensive genetic algorithm for optimal overcurrent relays coordination. Electr Power Syst Res 2008;78(4):713–20. [16] Karegar HK, Askarian Abyaneh H, Ohis V, Meshkin M. Pre-processing of the optimal coordination of overcurrent relays. Electr Power Syst Res. 2005;75(2– 3):134–41. [17] Keil T, Jager J. Advanced coordination method for overcurrent protection relays using nonstandard tripping characteristics. IEEE Trans Power Deliv 2008;23(1):52–7. [18] Nepveux F. In a flash: use of instantaneous trip functions with current-limiting fuses to reduce arc energy without losing coordination. In: IEEE industry application mag; 2007; p. 68–72. [19] Pregelj A, Begovic M, Rohatgi A. Recloser allocation for improved reliability of DG-enhanced distribution networks. IEEE Trans Power Syst 2006;21(3):1442–9. [20] Application guide for the selection of high-voltage current-limiting fuse-links for transformer circuits. PD IEC/TR 60787, ed1.0; 2007–03, ISBN:9780580508011. [21] Oskounejad MM. Engineering economy or economic evaluation of industrial projects. 7th ed. Tehran: Publication Center of Amirkabir University of Technology; 1996. [22] Comassetto L, Bernardon DP, Canha LN, Abaide AR. Automatic coordination of protection devices in distribution system. Electr Power Syst Res 2008;78:1210–6. [23] Billinton R, Jonnavithula S. A test system for teaching overall power system reliability assessment. IEEE Trans Power Syst 1996;11(4):1670–6. [24] Allan RN, Billinton R, Sjarief L, Goel L, So KS. A reliability test system for educational purposes-basic distribution system data and results. IEEE Trans Power Syst 1991;6(2):813–20.
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Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes
Fuse cutout allocation in radial distribution system considering the effect of hidden failures Mojtaba Gilvanejad a,b, Hossein Askarian Abyaneh a,⇑, Kazem Mazlumi c a
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran Substation & Transmission Department, Niroo Research Institute, Tehran, Iran c Department of Electrical Engineering, University of Zanjan, Zanjan, Iran b
a r t i c l e
i n f o
Article history: Received 23 May 2010 Received in revised form 5 April 2012 Accepted 20 April 2012 Available online 5 June 2012 Keywords: Fuse cutout Hidden failure Markov model
a b s t r a c t Among the several components of distribution systems, protection devices present a fundamental importance, since they aim at keeping the physical integrity not only of the system equipment, but also of the electricians team and the population in general. One of the protective devices playing a vital role in overhead distribution lines is fused cutout. At the era of privatized utilities, the protection devices should be allocated and coordinated optimally to reduce capital investments and the system outage costs. Amid this situation, the mentioned type of the protection device (fuse cutout) has not been studied for economical allocation up to now. In this paper, responding to this need, an accurate reliability model of fuse cutout containing hidden failures is figured out in the shape of a new Markov model. This model is used for economical allocation of the fuse cutouts. On the basis of the proposed model, a methodology for economic allocation of fuse cutouts is presented. This methodology involves the worth of energy not supplied (ENS) of the network and makes a balance between the cost of fuse cutout installation and the benefit of ENS decrease because of the minimizing the faulty zone by using more fuse cutouts. The methodology is tested on a sample distribution network as well as on IEEE 6-bus distribution test system (RBTS). Moreover, its capability on decision making about the fuse cutout placement and its simplicity for implementing on MV overhead lines are displayed. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Protective systems are designed to recognize certain types of power system disturbances and to isolate those parts of the system on which the disturbances occur [1]. These systems play a vital role in maintaining the high degree of service reliability required in present day power systems [2]. In reality, however, protection systems may be exposed to two main failure modes, since they can fail either by not responding when they should or by operating when they should not. The reliability of protection relay can be improved by carrying out routine maintenance or by including built-in monitoring and self-checking facilities during the design stages. In recent years, considerable efforts have been devoted to evaluate the extent up which these failure modes can affect the power system reliability [1–7]. In [2], a Markov model which is used to examine the effect of routine tests and self-checking intervals on system reliability is described. The proposed method has been
⇑ Corresponding author. Address: Department of Electrical Engineering, Amirkabir University of Technology, Hafez Street, Tehran, Iran. Tel.: +98 21 64543300; fax: +98 21 66406469. E-mail address: [email protected] (H.A. Abyaneh). 0142-0615/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijepes.2012.04.038
further improved in [3] by dividing the Markov model into two main parts which are devoted to express different situations of power component and protective devices. In [4], the effect of relay coordination methods on reliability indices of an interconnected power system has been studied. The authors evaluated the effect of protection failures on the reliability indices using two separate Markov models, one of them was dedicated to power system components and the other was considered for protective devices. In [1], a Markov model has been proposed which is capable to consider redundant protection systems. Most of the aforementioned papers have been based of two main assumptions. First, the power system has been assumed to be interconnected. Furthermore, protective relays have been considered as the main protection device to protect such systems [8,9]. However, the mentioned assumptions are of limited relevance in case of distribution systems which have usually radial configuration with fuse cutouts as the main protective devices. This device is more economical and, therefore, is favored on a distribution level. The fuse cutouts have some different features comparing to the protection relays which made it special in reliability modeling. For example, the rate of hidden failures of the fuses is lower than the rate of hidden failures of relays. The reason is the difference between the nature of relays and fuses. The relay protection
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system contains several parts such as transducers (CT1, VT2), circuit breaker, relay, etc. Each of them has its own failure rate and makes the total failure rate noticeable [10]. However, the fuse only operates as a single element which is in series with the other components of the circuit. Therefore, only its own failure rate is taking into account [11]. Therefore, there is a knowledge gap to find an appropriate Markov model applicable for these devices. This paper tries to focus on this topic by bringing two main contributions into the existing literature. First, a Markov model is proposed which is capable to consider the distinct reliability features of fuse cutouts. Then, the proposed Markov model is used to allocate fuse cutouts in MV overhead lines by using the well-known reliability index of energy not supplied (ENS). Finally, an economic analysis is presented to investigate cost-saving benefits of the proposed model in protection device allocation procedure. 2. Problem statement The ultimate purpose of protection is to provide power system reliability. It might seem that protection of equipments is the purpose of protection systems, but this misses the global picture. It is the integrity of the system which is being protected [5]. To design protection scheme of a specific system, various devices become available and may have significant cost. Thus, the evaluation of the impact of applying new device as well as its cost effectiveness, is required. Overcurrent relays (OCRs) and directional overcurrent relays (DOCRs) are widely used for the protection of radial and ring sub-transmission and distribution systems [12]. In some cases, differential protection scheme is also used in distribution feeder protection system due to its fast operation [13]. In distribution systems, these relays have relatively considerable cost comparing to other devices and mainly are used in the beginning of the feeders at the primary substations [14]. They have been studied in literature in several areas of research such as optimum coordination and advanced coordination methods, optimum routine test and self checking intervals, coordination of instantaneous trip functions with other protective devices like current-limiting fuses, etc. [2,15–18]. Reclosers and sectionalizers, which are other distribution protective devices, also have relatively high expense and are usually installed on the beginning of distribution branches which serve sensitive loads. They are also used for improving the reliability of the system especially in the DG-enhanced distribution networks with preserving the protection system coordination [19]. But, there is a protective device in distribution system which is inexpensive and widely used along the distribution feeders and branches and also for protecting the pole-mounted transformers. This device is fuse cutout and as mentioned above, has two different usages; one for protecting the pole-mounted transformers. Therefore, it is located at the primary side of each aforementioned transformer. The other is in feeder protection where the fuse cutout is placed at the joint points and protects the main feeder from outages initiated at the feeder taps. These two usages are shown in Fig. 1. The location of fuse cutouts which are used for transformer protection is completely definite. They are installed at the primary side of MV3/LV4 transformers. The rating of these fuses is chosen based on the standard list according to its protected MV/LV transformer capacity [20]. Such fuses should be coordinated with the upstream fuses and overcurrent relay exists at the beginning of the MV feeder. But, here questions raise as: ‘‘Where is the proper and cost 1 2 3 4
Current transformer. Voltage transformer. Medium voltage. Low voltage.
Fig. 1. Types of fuse cutout usages: (a) at joint point and (b) primary side of transformer.
effective place for the joint point fuse cutouts?’’, ‘‘Should they be installed at the beginning of every joint point along the distribution feeders?’’ For example, if a branch has a few meters length and only one or two small transformers are being installed along this branch, does it need an individual fuse cutout for protecting itself or an upstream protection device could provide the required protection? In an increasingly competitive market environment where companies emphasize cost control, correct modeling and allocation of fuse cutouts which can lead to a cost-saving approach become important. To answer the aforementioned questions, it is necessary to evaluate the reduction of frequency and duration of outages happening in a specific period of time and for a specific set of customers. Placing the fuse cutout at the joint points leads to decrease the faulty zone; therefore, the resultant benefit should be compared to the investments that need to be expended for the required number of fuse cutouts. Achieving this aim, the reliability modeling of fuse cutouts should be studied and the performance of fuse for providing an economical reliable distribution network should be assessed. Markov model of a fuse is a suitable approach for reliability modeling in Monte-Carlo simulations which will be expressed in the following section.
3. Fuse Markov model The first step in determining the reliability model for any system is to understand its function, the constraints under which it operates, and the root cause of the failure [2]. Here, to better understand the reliability modeling of the fuse cutout, the distinctive features of the fuse in comparison with the protective relays are discussed. There have been a number of models established to facilitate the reliability evaluation including protective relay failures. The models of current-carrying component paired with its associated relay protection system are proposed in literature [1–3,6]. Also, a model that separates the component Markov model from the protection Markov model has been suggested in [4]. In that paper [4], the model is capable of analyzing the different reactions of overcurrent relays that have been installed on the both sides of a line, when a fault occurs in an interconnected system. If the fuse is placed as the protective device in an electrical system, there is a reason that makes the Markov modeling of the fuse differ. The important point is that the fuse has not an inspection state when the current-carrying component is up. This is an intrinsic feature of the fuse which is placed in series with the line while the network operator is not able to test this device during the operation period of the network. The reason is the need for load deenergizing during the test process and detrimental effects of fuse tests leading to fuse burning and fuse-link removal after the test completion. Thus, the inspection state will never exist in the
M. Gilvanejad et al. / Electrical Power and Energy Systems 42 (2012) 575–582
reliability model of the fuse. It leads to the fact that the probability of true or false operating of the fuse cutout can only be considered when the component fails. In other words, it is possible to construct a Markov model of the fuse cutout only involved when the current carrying component fails. The Markov model of fuse cutout having five states is shown in Fig. 2. As mentioned above, all of these states, except state 1, are probable to occur only when the current carrying component is down. The states in Fig. 2 are as follows:
State State State State State
1: 2: 3: 4: 5:
the fuse cutout is carrying the current. the fuse link of fuse cutout starts to melt. the fuse cutout trips. the fuse cutout has experienced ‘‘failure to operate’’. backup protection trips.
The notations in Fig. 2 are:
lI1 lI2 wN wB k kp1 kp2 kMC
sum of repair rates relevant to component and fuse cutout in normal (main) protection zone; sum of repair rates relevant to component and fuse cutout in backup protection zone; normal tripping rate to isolate component; backup tripping rate to isolate component; failure rate of component; failure rate of fuse cutout to expose to ‘‘undesired trip’’; failure rate of fuse cutout to state of ‘‘failure to operate’’. rate of mis-coordination of protection
577
the fuse cutouts in a distribution system considering the economical and technical features. Human errors are not included in the study hence, the mis-coordination factor is neglected. The miscoordination effect can be studied in each utility by applying the related kMC in the model of fig. 2. The remaining parts will be the same as the approach presented in the following sections of the paper. In general, in normal operating condition, load currents are normally distributed in the network branches and fuse cutouts conduct the current in a normal manner. In this situation, fuse cutout is in state 1 (Fig. 2) where constitutes most time of operation period. When a fault occurs in the distribution system, huge amount of current passes through the fuses and makes it start melting (state 2). If the fuse cutout trips correctly, the fault will be isolated and fuse will be placed in state 3. Otherwise, if the fuse fails to operate in a rational time (state 4) then backup protection has to trip immediately (state 5). After isolating the fault, either in normal zone or backup zone, the repair action starts and returns the system to a good state (state 1). In some instances, the fuse trips erroneous and makes the system to be de-energized unreasonably. In such situations, system goes directly from state 1 to state 3. In this way, the proposed model in Fig. 2 could model the different states which could happen in the fuse cutout protected distribution network. This model will be used in reliability analysis for the fuse allocating methodology.
4. Fuse cutout allocation The reason of selecting two different coefficients for main and backup tripping and repair rates of protection system is the differences that exist between the operation speed and fault locating time in these two protection zones in reality. The backup protection (relay/fuse) should act with delay when the main protection (fuse) has no action. After tripping the backup protection, fault locating process takes longer time because the zone of backup protection is more extensive than the main protection zone. The hidden failure is not considered for the backup protection because the occurring of hidden failures in the set of main and backup protections is rare. In the model depicted in Fig. 2, the mode of influencing the miscoordination of protection system on the reliability model has been shown. As shown in this figure, the mis-coordination makes the backup protection operate before the main protection. The rate of mis-coordination events greatly depends on the skill of protection staff. In this paper, we want to locate the proper place for
Fig. 2. Markov model of fuse cutout.
4.1. Fuse cutout allocation methodology Regardless the overcurrent relays which exist in sub-transmission substations and being installed at the beginning of the medium voltage feeders, the MV overhead lines are also protected by fuse cutouts. These fuses are mainly placed at the joint points along the feeders. As mentioned before, determining the number of fuses that should be installed along a feeder is a problem. This problem is solved through the cost and benefit balancing. Costs contain capital expenses to be invested for fuse cutouts and benefits contain revenues obtained from the outage and maintenance cost reduction. In order to estimate the outage costs of a distribution system, the reliability analysis is required. This estimation could be done by Monte Carlo iterative approach. The Markov Model of a fuse cutout which is offered in Fig. 2 has been used in this Monte Carlo simulation. It is necessary to mention that the reliability analysis should be done for each fuse cutout removal on candidate places of the related MV network. In this way, the analysis provides the amount of energy not supplied that fuse could decrease. Then, the worth of this additional sold energy is compared with the investments that are required for the fuse cutout installation. If the fuse cutout installation has the economical justification, the benefit of outage reduction during the life time of the network will compensate the investments for fuse cutouts at installation time and will obtain some financial benefits for utility owners. Otherwise, the related place is not proper for the fuse cutout placement. Since in real networks, multiple faults may concurrently occur in different parts of the network, the reliability analysis should enable to model this type of failures. However, the reliability calculations should only consider one failure whenever the multiple failed components devote to a unique protective device. Furthermore, the magnitude of the energy not supplied (ENS), that occurs in a feeder because of the feeder failures, depends on two main factors: (a) the feeder load, and (b) the feeder length. The manner of affecting the load amplitude is that, whatever the
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feeder load increases, the occurrence of any outage makes more customers be de-energized and the amount of energy not supplied increases. The other factor, ‘‘the feeder length’’, causes that whatever the length increases, the probability of fault and outage occurrence increases which will lead to energy not supplied increment. These two factors obviously show that making decision about placing or not placing a fuse cutout along a feeder strongly depends on the feeder and load configuration. Based on the above discussions, it is proposed a method for the fuse cutout allocation as the following steps: a. Fuse cutouts are placed at every beginning and middle points of medium voltage overhead lines. b. For each fuse removal, the reliability analysis is performed and the rate of ENS changes is calculated. c. If the cost of ENS increment due to fuse cutout removal was less than the investment that is required for the fuse installation therefore, the fuse cutout should not be installed. Otherwise, fuse cutout must be installed. The middle point of the feeders is considered as a typical place for sectionalizing type of fuse cutouts. This type of fuse can be installed anywhere between start and ending points of the feeder. 4.2. Formulation of fuse Markov model in Monte Carlo simulation The probability of residing in each state of Markov model shown in Fig. 2 in an analytical way can be obtained via the theory of Continuous Markov Processes [1]. The steady state probabilities of the proposed Markov model can be calculated by solving the following equation:
Pa¼P
ð1Þ
where a is the state transitional probability matrix:
2
0 6 0 6 6 a¼6 6 lI1 6 4 0
k
kp1
0
0
wN
kp2
0
0
0
0
0
0
lI2 0
0
0
0
3
kMC 7 7 7 0 7 7 7 wB 5 0
ð2Þ
And P is the row vector of state probabilities:
P ¼ ½Pstate1 Pstate2 Pstate3 Pstate4 Pstate5
ð3Þ
Using Eqs. (1)–(3), it is possible to obtain the average values of the probabilities devoted to reside in each state. In our approach, however, for closing the results to reality (real values, not average values), the non-sequential Monte Carlo simulation is used to achieve the more accurate results. Since the failure rates of the network devices and protection systems are supposed to be constant, their failure probability corresponds to the exponential probability density function. The failure probability of a network component according to exponential density function is as follows:
pðtÞ ¼ kekt ;
tP0
ð4Þ
where t is the age of the network component in year. The probability of multiple faults for a single component during a year is fulfilled by the Poisson process as follows:
pðkÞ ¼
kk ek ; k!
failure probability based on Eqs. (4) and (5), the related component is considered as a failed component. This procedure is also accomplished in this paper for modeling the component repair process as well as the protection system malfunctioning in Monte Carlo simulation; through replacing k in Eqs. (4) and (5) with related failure or repair rates (e.g. lI1, lI2, kp1, etc.). 4.3. Formulation of allocation criteria If annual failure rate of an overhead line is shown with k then, the average cost of annual energy not supplied CENS-a can be calculated through the following equation:
C ENS-a ¼ kLP av e T outage C ENS
ð6Þ
where L is the length of feeder, Pave is the average active power of loads which have experienced the outages, Toutage is the average outage time and CENS is the worth of energy not supplied unit (i.e. $/KW h). As mentioned before, Eq. (6) is an average value and only takes into account the failure rate of the feeder. In order to involve the failure rates of protection system in each state of Markov model shown in Fig. 2, a Monte Carlo simulation which utilizes random number generators to model stochastic failure occurrences in component and protection areas is employed and the related outage time and energy not supplied are calculated simultaneously. Therefore, the annual cost of ENS is calculated as follows:
C ENS-a-tot ¼ ENS C ENS
ð7Þ
where ENS is total energy not supplied which consists of energy not supplied because of occurring faults in overhead lines and energy not supplied because of failures in protection system and CENS-a-tot is its related costs. Since CENS-a-tot is a cost that happens every year during the network lifetime so, it should be converted to the present value. With the aid of this conversion, it makes possible compare ENS cost with the investment cost which is being spent at the present time. To achieve this aim, the following equation is used [21]:
P:W: of C ENS ¼
ð1 þ RORÞn 1 C ENS-a-tot ROR ð1 þ RORÞn
ð8Þ
where P.W._of_CENS is the present worth of the whole energy not supplied during the lifetime of the network, ROR is the rate of investment return that the utility owner expects (rate of interest) and n is the lifetime of the network in years. Proper installation of fuse cutouts along the feeders could reduce CENS-a-tot and make some benefit for the utilities. In general, if the following equation has a positive value, the fuse installation at the relevant point is not required. Otherwise, if it has a negative value, the fuse cutout is required.
NB ¼ C cap ½P:W: of C ENS ð2Þ P:W: of C ENS ð1Þ
ð9Þ
where NB is the net benefit of fuse removal and Ccap is the capital costs required for fuse cutout placement. Also (1) and (2) indices represent the total worth of feeder ENS before and after fuse removal, respectively. In the next section, the proposed methodology is implemented in sample networks and the results are discussed in detail. 5. Study results
k ¼ 0; 1; . . .
ð5Þ
where k is the number of component failures in a year. In Monte Carlo method, a random number generator that produces random numbers between 0 and 1 is used for assigning a random failure probability to each network component. Whenever the random assigned number was smaller than the calculated
5.1. Simple test case The allocation methodology is demonstrated in a constituted system of seven branches and seven transformers which is also used in [22]. In this system, each transformer is defined with the load power of 150 KW, 1-km branch length, 13.8 kV of operation
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M. Gilvanejad et al. / Electrical Power and Energy Systems 42 (2012) 575–582 Table 3 Network ENS results. Branch no. of removed fuse
No fuse removal
1–2
3–4
3–5
1–6
6–7
ENS (kW h) (Monte Carlo) Priority of removal
2815
3630
3230
3230
3490
2960
–
4
2
2
3
1
Fig. 3. System example.
and a failure rate equal to one fault a year (k = 1). The re-establishment time (Toutage) is 55 min. Fig. 3 shows the described system. In Fig. 3, triangles contain electrical loads and circles are fuse cutouts. The failure and repair probability distribution functions of the network devices are formed according to their related failure and repair rates and then are used in Monte Carlo simulation. For example, the probability distribution functions related to each branch failure will be equal to:
pðtÞ ¼ et ;
tP0
And,
pðkÞ ¼
e1 ; k!
k ¼ 0; 1; . . .
ð11Þ
In [22], the aforementioned distribution system is used for implementing the protection coordination algorithm and the average energy not supplied of the system is calculated as a criterion for priority of fuse removal. The calculated ENS has not considered the hidden failures of the fuses. In that paper, since the goal is the coordination of protection devices, an estimate of average values of ENS is useful for prioritizing of fuse removal; and fuse allocation is not the subject of the study. In our study, however, in order to economically evaluate the fuse cutout allocation, the real values of ENS are needed to be taken into account. Thus, it is required that more real parameters affecting the operation of real network be considered in reliability studies such as undesired trip and failure to operate of protection systems, re-establishment time of main and backup protection, etc. In this study, the re-establishment time of the network in backup zone of the protection system is considered as twice (110 min) of outage time in the main protection system (55 min). The value of other parameters for the cutout fuse Markov model which is used in simulations is presented in Table 1. In Table 2, to compare the results of analytical (Ref. [22]) and iterative Monte Carlo approaches, the quantities of ENS for different fuse removals of sample network are presented. Table 1 Fuse cutout reliability data [9]. kp1 (failure/year)
kp2 (failure/year)
WN (operation/h)
WB (operation/h)
0.00374
0.00876
36,000
18,000
Table 2 Comparison of ENS results.
a
Fig. 4. First candidate of fuse cutout removal.
ð10Þ In Table 2, in order to keep similarity with assumptions in [22], the protection failure rates are not considered for the Monte Carlo simulations; therefore, no backup protection exists. As can be seen, the results are close to each other. But there are some differences between the quantities which despite low magnitude, is important when the size of the network extends and the number of cutout fuses increases. Monte Carlo estimates the reliability indices based on the stochastic approach which corresponds to the network behavior essence. Therefore, its results are more useful for technical–economical evaluations. The ENS values resulting from the proposed Markov model (Fig. 2) in Monte Carlo simulations which include all of the parameters shown in Fig. 2, are presented in Table 3. According to the table, the first priority of fuse cutout removal is fuse No. 6 (Fig. 4). The quantities that have been inserted in Table 3 are annual values and to implement the proposed methodology for the fuse allocation, the present worth of ENS cost during the life time of the system should be calculated before and after fuse cutout removal. So
PW of C ENS ð1Þ ¼
1:120 1
0:1 1:120 PW of C ENS ð1Þ ¼ 2396:6$ PW of C ENS ð2Þ ¼
1:120 1
0:1 1:120 PW of C ENS ð2Þ ¼ 2520:0$
2815 0:1 ð12Þ 2960 0:1
Eq. (12) is computed according to (8), where ROR and n are assumed 0.1 and 20. CENS is also assumed 0.1 $/kW h. The capital cost of the installation a series of three phase fuse cutouts is supposed 170 $. Now, it is possible to calculate the introduced criteria (Eq. (9)) to judge about preserving or removing the fuse cutout which can be placed on branch (6–7):
NB ¼ 170 ½2520:0 2396:6 ¼ 46:6$
Branch no. of removed fuse
No fuse removal
1–2
3–4
3–5
1–6
6–7
ENSa (kW h/year) (analytical) ENS (kW h/year) (Monte Carlo) Priority of removal
2755.5
3580.2
3169.6
3169.6
3444.2
2893.1
2730
3555
3160
3160
3400
2880
–
4
2
2
3
1
The values of this row is obtained from [18].
ð13Þ
Therefore, Eq. (13) declares that the fuse cutout is not required in branch (6–7) and should be removed. After removing this fuse cutout, the process should be repeated and the removal priorities should be determined again. Results of this stage are presented in Table 4. Two points can be achieved from Table 4. The first one is that the next candidate for fuse removal is fuse No. (3–4) or (3–5). The second one which is more important is that the sequence of fuse removal priorities has changed comparing to Table 3.
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Table 4 Network ENS results after the first fuse removal. Branch no. of removed fuse
No fuse removal
1–2
3–4
3–5
1–6
ENS (kW h/year) (Monte Carlo) Priority of removal
2960
3770
3370
3370
4280
–
2
1
1
3
Now, in order to make decision about the second fuse removal, the worth of ENS increment duo to the eliminating of the fuse of the highest removal priority (fuse No. (3–4)) is again calculated:
PW of C ENS ð2Þ ¼
1:120 1
0:1 1:120 PW of C ENS ð2Þ ¼ 2869:1$
3370 0:1
ð14Þ
Therefore, NB equals to: Table 5 Network ENS results after taking new rates for fuse cutout undesired trip (kp1).
ENS (kW h/ year)
Fuse cutout No. (3–4) preserved Fuse cutout No. (3–4) removed
NB ($)
kp1 = 0.095 (failure/year)
kp1 = 0.95 (failure/year)
3030
3980
3410
4160
153.4
16.8
Table 6 Network ENS results after taking new rates for fuse cutout failure to operate (kp2).
ENS (kW h/ year)
NB ($)
Fuse cutout No. (3–4) preserved Fuse cutout No. (3–4) removed
kp2 = 0.015 (failure/ year)
kp2 = 0.15 (failure/ year)
2950
3590
3320
3780
144.9
8.3
In Table 3, fuse No. (1–6) has higher priority than fuse No. (1–2). In Table 4 fuse No. (1–2) has higher priority for removal. Therefore, it can be concluded that the sequence of fuse removal should be re-evaluated after any fuse cutout elimination.
NB ¼ 170 ½2869:1 2520:0 ¼ 179:1$
ð15Þ
The fuse removal has not any justification this time and fuse cutout No. (3–4) (or (3–5)) should be preserved. Since this fuse was the first priority for fuse removal in the network, the other candidates certainly will not have justification for the removal. In order to more clarifying the influence of a factor which affects the removal or preserving the fuse cutouts during the allocation procedure, some complementary studies are performed here. This important factor is the magnitude of hidden failure rates. The undesired trip or failure to operate of fuse cutouts will result in ENS augmentation in distribution systems. Bigger values of hidden failure rates will result in bigger values of total network ENS. Therefore, the hidden failure rate increment in fuse cutouts may result in more fuse cutout removal action to achieve less ENS value of the network in optimum placement procedure. For example, the second fuse cutout removal action for the test system has not economic justification (Eq. (15)) when its hidden failure rate was according to Table 1. However, its removal will have economic justification if the fuse cutout hidden failure rate has large enough value to make the answer of Eq. (15) positive. In order of evaluation the effect of hidden failure rate magnitude on the economic justification which is investigated in fuse cutout allocation process, some complementary simulations were performed to assess this issue and their results have been reported in Tables 5 and 6. Tables 5 consists the ENS results for changing the undesired trip value of fuse cutouts in the system of Fig. 3 and Table 6 consists the
Fig. 5. Distribution system for RBTS bus 5.
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Fig. 6 illustrates the configuration of the fuse cutout and the disconnector at the joint points [24]. In [23,24], the fuses and disconnectors are assumed to be 100% reliable. But, here in our study, the fuses are not 100% reliable and have failure rates corresponding to the mentioned quantities in Table 1. The reliability data for the 11 kV lines are shown in Table 7 [23,24]. The failure and repair probability distribution function for the network components and protection systems are formed and they are used in Monte Carlo simulation. The failure probability function of the network overhead lines will be equal to as follows:
pðtÞ ¼ ð0:065 lÞ eð0:065lÞt ;
tP0
ð16Þ
k ¼ 0; 1; . . .
ð17Þ
And,
pðkÞ ¼
where l is the length of overhead line in both aforementioned relationships. Now, the fuse cutout allocation methodology is implemented on the feeder F1 of the system shown in Fig. 5. Comparing to the previous sample test, in this case, the disconnectors have been added to distribution system and are included in the reliability simulations. The feeder lengths and loading data are according to [23]. Similarly, the first step is the determination of the removal priorities. This data has been collected in Table 8. The present worth of ENS before and after removing the first candidate cutout fuse (i.e. cutout fuse No. 11) equals to:
Fig. 6. Joint point configuration.
Table 7 Overhead lines reliability data. Failure rate (failure/year km)
Repair time (h)
Switching time (h)
0.065
5
1.0
ð0:065 lÞk eð0:065lÞ ; k!
ENS results for changing the failure to operate value of fuse cutouts in the system of Fig. 3. The first row of ENS results in Tables 5 and 6 devotes to the energy not supplied values of the network which are calculated with new values of kp1 and kp2 before removing the fuse cutout No. (3–4). As can be seen in these two tables, removal of fuse cutout No. (3–4) will have economical justification whenever kp1 and kp2 take large enough values and causes the NB takes a positive value. Furthermore, it is seen that the influence of kp2 (failure to operate) variation on the energy not supplied value of the system is much more than kp1 (undesired trip). In other words, the fuse cutout allocation procedure is more sensitive to kp2 rather than kp1. The ENS is also influenced by two other factors such as load magnitude and feeder length. Changing each of them could change the final decision about the fuse. Hence, in order to show the generality of the proposed methodology, IEEE distribution reliability test system will be studied in the next section.
PW of C ENS ð1Þ ¼
1:120 1
0:1 1:120 PW of C ENS ð1Þ ¼ 2341:3$ PW of C ENS ð2Þ ¼
2750 0:1 ð18Þ
1:120 1
2920 0:1 0:1 1:120 PW of C ENS ð2Þ ¼ 2486:0$ For this state, the NB factor for cutout fuse removal is calculated as:
NB ¼ 170 ½2486:0 2341:3 ¼ 25:3$
ð19Þ
Therefore, cutout fuse 11 should be removed. Prioritizing the fuse cutouts at the next stage after removing the cutout fuse 11 is demonstrated in Table 9. In the case of evaluating the elimination of cutout fuse No. 9, the NB will be equal to 17.3$ therefore, it should be preserved. As the results show, in every distribution system, the problem of placing or removing the fuse cutout on the beginning of the branches should be evaluated case by case. In the two samples which are studied in this paper, one fuse cutouts was been installed improperly. In this way, all of MV overhead lines can be evaluated for the fuse cutout allocation and only the fuse cutouts with technical and economical justifications are installed. The procedure which is described in this paper can help to better modeling of the fuse cutout operation in distribution systems and the network operators can better manage the network investments through optimal placement of fused cutouts in distribution systems where, large numbers of fused cutouts are usually installed.
5.2. IEEE 6-bus test system (RBTS) The IEEE test system for reliability assessment is a 6 bus test system with five load buses (bus 2–bus 6). The distribution network at bus 5 represents a typical urban type network consisting of residential, government and institutional, office and buildings, and commercial customers. The peak load of the distribution system at bus 5 is 20 MW. The distribution network at bus 5 is labeled in detail in Fig. 5 [23]. In this network, a fuse cutout is placed at any join point. Furthermore, a disconnector has been installed after each join point. Table 8 Network ENS results and removal priorities. Branch No. of removed fuse
No fuse removal
ENS (kW h/year) Priority of removal
2750 –
2
3
5
6
8
9
11
3680 6
3680 6
3410 5
3140 4
3010 3
2960 2
2920 1
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Table 9 Network ENS results and removal priorities. Branch No. of removed fuse
No fuse removal
ENS (kW h/year) Priority of removal
2920 –
2
3
5
6
8
9
3790 5
3790 5
3540 4
3320 3
3220 2
3140 1
6. Conclusion In this paper, a new Markov model for the reliability analysis of the fuse cutouts has been proposed which considers the hidden failures of the fuses. Also, a methodology has been suggested for the fuse cutout allocation along the medium voltage overhead distribution lines. This methodology gives a simple and useful criterion for decision making about fuse cutout placement. The approach has been tested on two different networks. One of these was a simple constituted network which the methodology is implemented and its concepts were explained. Another one was the IEEE reliability test system and the methodology was discussed for one of its feeders. This network had also disconnectors whose effects are included in the reliability simulations. Fuse cutout allocation results showed that in both sample networks, there was a fuse cutout which was been installed improperly. Therefore, using the proposed methodology in this paper, the reliability of MV overhead lines is maintained at a reasonable level while preserving the protection system adequacy. The results of the implementing the proposed methodology in two sample networks show its capability and simplicity to be applied in real MV overhead distribution lines. References [1] Anderson PM, Chintaluri GM, Magbuhat SM, Ghajar RF. An improved reliability model for redundant protective systems – Markov models. IEEE Trans Power Syst 1997;12(2):573–8. [2] Billinton R, Fotuhi-Firuzabad M, Sidhu TS. Determination of the optimum routine test and self-checking intervals in protective relaying using a reliability model. IEEE Trans Power Syst 2002;17(3):663–9. [3] Yu X, Singh C. A practical approach for integrated power system vulnerability analysis with protection failures. IEEE Trans Power Syst 2004;19(4):1811–20. [4] Mazlumi K, Askarian Abyaneh H. Relay coordination and protection failure effects on reliability indices in an interconnected sub-transmission system. Electric Power Syst Res 2009;79(7):1011–7. [5] Graziano RP, Kruse VJ, Rankin GL. Systems analysis of protection system coordination: a strategic problem for transmission and distribution reliability. In: Transmission and distribution conf, proc IEEE Power Eng. Society; 1991. p. 363–9. [6] Anderson PM, Agarwal SK. An improved model for protective-system reliability. IEEE Trans Reliab 1992;41(3):422–6. [7] APM Task Force on Protection Systems Reliability. Effect of protection systems on bulk power reliability evaluation. IEEE Trans Power Syst 1994;9(1):198–205.
[8] Tuitemwong K, Premrudeepreechacharn S. Expert system for protection coordination of distribution system with distributed generators. Int J Electr Power Energy Syst 2011;33(3):466–71. [9] Aslan Y, Ture S. Location of faults in power distribution laterals using superimposed components and programmable logic controllers. Int J Electr Power Energy Syst 2011;33(4):1003–11. [10] Dong J, Koval DO, Zuo MJ. Impact of circuit breaker failure modes on the reliability of the gold book standard network. IEEE Trans Ind Appl 2005;41(5):1323–8. [11] Cadwallader LC. Electrical power distribution system operating experience review for fusion applications. Fusion Eng Des 2002;63–64:327–31. [12] Birla D, Maheshwari RP, Gupta HO. Time-overcurrent relay coordination: a review. Int J Emerging Electr Power Syst. 2005;2(2):1–13. [13] Bakar AHA, Mokhlis H, Lllias HA, Chong PL. The study of directional overcurrent relay and directional earth-fault protection application for 33 kV underground cable system in Malaysia. Int J Electr Power Energy Syst 2012;40(1):113–9. [14] Lin X, Ke S, Gao Y, Wang B, Liu P. A selective single-phase-to-ground fault protection for neutral un-effectively grounded systems. Int J Electr Power Energy Syst 2011;33(4):1012–7. [15] Razavi F, Askarian Abyaneh H, Al-Dabbagh M, Mohammadi R, Torkaman H. A new comprehensive genetic algorithm for optimal overcurrent relays coordination. Electr Power Syst Res 2008;78(4):713–20. [16] Karegar HK, Askarian Abyaneh H, Ohis V, Meshkin M. Pre-processing of the optimal coordination of overcurrent relays. Electr Power Syst Res. 2005;75(2– 3):134–41. [17] Keil T, Jager J. Advanced coordination method for overcurrent protection relays using nonstandard tripping characteristics. IEEE Trans Power Deliv 2008;23(1):52–7. [18] Nepveux F. In a flash: use of instantaneous trip functions with current-limiting fuses to reduce arc energy without losing coordination. In: IEEE industry application mag; 2007; p. 68–72. [19] Pregelj A, Begovic M, Rohatgi A. Recloser allocation for improved reliability of DG-enhanced distribution networks. IEEE Trans Power Syst 2006;21(3):1442–9. [20] Application guide for the selection of high-voltage current-limiting fuse-links for transformer circuits. PD IEC/TR 60787, ed1.0; 2007–03, ISBN:9780580508011. [21] Oskounejad MM. Engineering economy or economic evaluation of industrial projects. 7th ed. Tehran: Publication Center of Amirkabir University of Technology; 1996. [22] Comassetto L, Bernardon DP, Canha LN, Abaide AR. Automatic coordination of protection devices in distribution system. Electr Power Syst Res 2008;78:1210–6. [23] Billinton R, Jonnavithula S. A test system for teaching overall power system reliability assessment. IEEE Trans Power Syst 1996;11(4):1670–6. [24] Allan RN, Billinton R, Sjarief L, Goel L, So KS. A reliability test system for educational purposes-basic distribution system data and results. IEEE Trans Power Syst 1991;6(2):813–20.