MULTI-AGENT APPROACH FOR RELIABILITY ASSESSMENT AND

Proceedings of The IRES International Conference, Moscow, Russia, 9th -10th February 2017, ISBN: 978-93-86083-34-0

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MULTI-AGENT APPROACH FOR RELIABILITY ASSESSMENT AND IMPROVEMENT OF POWER SYSTEM PROTECTION

1NADHEER A. SHALASH, 2ABU ZAHARIN AHMAD, 3AQEEL S. JABER

1Faculty of Engineering of Electrical power techniques, Al-Mamon University college Baghdad, Iraq

2Faculty of Electrical and Electronics Engineering; University Malaysia Pahang, 26600 Pekan, Malaysia E-mail: [email protected], [email protected], [email protected]

Abstract- Assessment of reliability is one of the important topics in a power system which needs a more decentralized mechanism to enable an electrical load to continue receiving the power in the event of its disconnection from the main power grid. Despite the huge remarkable breakthrough in software technologies, most judgments have to be based on human experts in most of the planning and operation in power system. Most of the techniques are used to address the power system reliability which consumes long computation time. Hence, an inaccurate result for system operators is inevitable as most of the variables affecting the reliability changes with time. As a result of this setback, Multi Agent System (MAS). MAS is a collection of agents that have been applied to several power system problems such as, those in operation, markets, diagnosis and protection. In this study, in order to assess system reliability, distribution protection system design and coordination, two models of MAS techniques to determine the suitability of the DG location based on power system reliability and new index reliability of the relay operating time are proposed. The simulation results for two models are created using the data obtained from Malaysia distribution network (DISCO-Net) and 69 bus test system that was implemented using Java Agent Development Framework package software. The simulation results show that the effectiveness of proposed MAS approaches for selecting the best Distributed generation (DG) location in a function of improved power system grounding and reliability. Meanwhile, the simulation results for second model shown that the failure rate decreased to approximately 40% for over current and earth fault relays. The fast reliability indices are also obtained in assessing the performance of the protection system from the selection of DISCO-Net. Index Terms- Multi-agent system, Generation Reliability Indices, Distributed generation , Jade package. I. INTRODUCTION DG provides many advantages in term of improvements in losses and reliability, or both [1]. In addition, there are many DGs locations which can lead to minimize fault current in the event of faults and provide the necessary effective grounding to solve bus voltage problems when unfault phases over voltage in bus are encountered. When DGs are connected into the distribution power grid, the grounding methods should be taken into account[2]. In effectively grounded system, all faults including grounds must be cleared by opening the line. For using a DG without a transformer, special attention should be paid to the zero sequence impedance design so that effective ground can be provided. Unlike the DG with a transformer providing the effective ground passively, the DG without a transformer must shape the zero sequence impedance characteristic using an active control approach [3]. Relay protection (over current relay and earth fault relay) of power system is based on three principles in its operation, the type of fault, the fault location and the number of interconnections. Therefore, DGs can supply an additional contribution to the fault level. The setting of protective relays that were formerly prepared for the system without DGs may not significantly manage faults [4]. The reliability indices determined by using life data analysis will provide more accurate or realistic information as it uses the past failure data of the relays. The protection relays (over current relay (OC) and earth fault relay (EF)) and the added DG produced a

reliable family, providing a set of probabilities of a reliable solution, for a bus burs power plants generation or distribution. Reliability assessment methods are now highly advanced and most electrical power system engineers and researchers have an implementation understanding of probabilistic techniques. Probabilistic techniques have been extensively employed in generation planning and distribution system design and some commercial software packages are available considerable progress has been made in power system reliability modeling and computation based on probability theory [5-6]. Many methods of techniques analysis have been developed and been widely used to evaluate the electric power system reliability indices. One of them is the recursive algorithm that more practical approach for large system analysis. The analysis can be used for two-state or multi-state unit and provides a fast technique for building capacity models (adding new units) and the algorithm can also be used to remove a unit[2]. Multi-Agent System (MAS) is a computerized system in which several agents cooperate to achieve a particular task. MAS are an essentially developed as a control and reconfigure the system when this occurs. The concept of single agent systems has to be understood before one starts looking into MAS. The agent has been defined in many different ways and a few definitions are provided as below. Agents were able to sense its local environment and to interact with other agents in its local environment. Java Agent Development Framework (JADE) is typically the most

Multi-Agent Approach for Reliability Assessment and Improvement of Power System Protection


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famous representative middleware which accessories an agent program and a development package[7]. In this present work, a new philosophy was developed for an improved reliability power system, making use of three different parameters, these parameters are the protection relays, an introduction of Distribution generators (DG) and calculations of reliability indices with varied probabilities depending upon Grounding conditions, the developed multi agents control system was used to select the best probability from these parameters by investigating the problems, detecting any type of disturbance(s) in the system, thereby controlling or solve them by interactions between these parameters and proffering solution with the best probability. II. THE PROBLEMS AND OBJECTIVES The changes in the operational and regulatory framework of electrical utilities are significantly impacting users of electricity. Thus, faults in the course of this affect the generation. Consequently technological developments in the form of technological innovations with relation to power system protection and distribution networks are important in order to enable an electrical load to continue to receive power in the event of disconnection of said load from the main power grid. The protective relay manufacturers only describe the reliability of their apparatus in accords of MTBF (mean time between failures), whereby the actual reliability figures of the relays may vary depending on the relay installation and operation over a period of time[8], for these problems we have two objectives A. Propose a new procedure to find DG location based on the optimum grounding location. B. Develop fast reliability indices to assess the performance of the protection system. III. DISTRIBUTION GENERATOR DG exploited is predicted to play an important role in the electric power system in term of reducing cost energy in the high load level, improved security, interconnection and increased reliability. When DGs are connected into the distribution power grid, the grounding methods should be taken into account. Historically, there have been gradual topics on the practice of grounding power system firstly from the ungrounded system then to impedance as resistance or reactance grounded, recently to solidly grounded and effectively grounded. For a system effectively grounded, all system points or in specified portion, the ratio of zero-sequence reactance to positive-sequence reactance is not greater than “three” while the ratio of zero-sequence resistance to positive-sequence reactance is not greater than “one” for any operational condition[2]. The third condition is unfaulted phases may be subjected to over-voltages in which the

absolute value of overvoltage in per unit is given as in the following Equation (1)[9]:

푉 = √ + + (1)

where; VD is overvoltage during single line to ground fault (L-G) in which voltage one phase varies from 0.866 pu to 1.732 pu. IV. NEW RELIABILITY INDEX FOR RELAY PROTECTION In this section a new effective grounding index reliability to determine time operating relay point based on bus sequence impedance (X1, X0) for improving power system reliability shall be considered. In time-inverse relay characteristics, the relays start to operate after an intended time delay (IEEE Committee Report, 1989)[10] and according to IEC 60255,there are different types of inverse characteristics . where, the TOP could be calculated by equation (2) below and substituting C and P : 푇푂푃 = ( ) 푇푀푆 (2) where, C and P is constant and depend on relay inverse

characteristic types.

Figure 1 Characteristics of OC relay for power transformers

(30&45) MVA

Figure 2 Characteristics of EF relay for power transformers

(30&45) MVA In most cases, fault current can be clearify by standard SI curve, but if then may use VI or EI curves to resolve the problem. As shown in Figure 1 and 2 for transformers (45 & 30)MVA. The characteristics

0 5000 10000 15000 20000

0.5

1.0

1.5

2.0

2.5

OTP

(Sec

)

fault Current (KA)

45MVA 30MVA

0 1000 2000 3000 4000 5000

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

OTP

(Sec

)

fault Current (KA)

45MVA 30MVA



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show an inverse relation between time operating and fault current assuming that IDMT relays with normal inverse (OC)and very inverse (EF) are available. The zero-sequence impedances of lines different from the positive and negative-sequence impedances since the magnetic field creating the positive and negative-sequence currents is different from that for the zero-sequence currents. The following ratios may be used in the absence of detailed information. [2]. For a single-circuit line, Z0/Z1 = 2 when no earth wire is present and 3.5 with an earth wire. For a double-circuit line Z0/Z1 = 5.5. For underground cables Z0/Z1 can be taken as 1 to 1.25 for single core, and 3 to 5 for three-core cables. From [9] it can be noted that proposed new index reliability by TOP calculated by the following equation

TMS

SP

f

CTOP

CTIII

P

1

where fault current If in range phfph III 336.0 .

Equation (4) is used to calculate the expected failure rate according to Equation (3):

N

mmTOP

Nt

1

)(

where, N is the total number of failures. V. RECURSIVE ALGORITHM WITH DG The reliability evaluation can be divided into two parts: modeling of the reliability characteristics of the components, and the calculation of the reliability of the system. The frequency and duration FAD method measured to evaluate customer point reliability. The FAD method utilizes the transition rate parameters λ and µ of generation units presented an equation (5).

the recursive algorithm with more practical approach for large system analysis. The analysis can be used for two-state or multi-state unit and provides a fast technique for building capacity models (installing or remove new units).The individual state probabilities and frequencies can be combined to form the cumulative state values using the following equations[11]: 푃 = 푝 + 푃 (6) 퐹 = 퐹 + 푝 휆 − 푝 휆 (7) where, n refers to the cumulative state with known probability and frequency and k is the state which is being combined to form the cumulative state n -1.

VI. THE MULTI-AGENT SYSTEM ARCHITECTURE Multi-Agent System (MAS) is a computerized system in which several agents cooperate to achieve a particular task. MAS was employed to resolve the probabilities of DGs configuration and regeneration distribution networks with the best setting for protection relays by: 1. Introducing a new method to modify the reliability by applying Grounding Indices combined with a recursive algorithm to find a best DGs location. 2. Introducing a new index protection for improving the reliability of the power system by the optimization time setting of OC and EF relay. The proposed MAS model can improves the system reliability based on the optimal DG location., which is divided into three agents of decision making and action. The first agent is the grounding DG conditions which is called as a technology indices grounding agent (IGA). The second agent is to calculate the frequency and duration indices reliability for each state is which as called recursive algorithm agent (RAG). Meanwhile, the third agent is the communication data center (CDC) agent that is considered the connecting agent between IGA and RAG. A thorough investigation on the various modeling, both protection and reliability indices are necessary for developing an effective detection technique. In order to achieve the reliability assessment, each agent must be developed as an effective communication within the platform and globally build a working relationship as they negotiate towards the laid criterion [12]. The proposed relay protection techniques based on MAS can be validated on a real practical power system to measure the MAS response, based on three areas of action and decision making. The first area is the grounding DG or transformer substation conditions which is called a technology indices grounding agent (IGA). The second area is a Bus bar Fault Current agent (BFA) used to determine fault current at all buses before and after modifying (0.6I3ph< If <I3ph).Meanwhile, the third area is the relay agent (RA) used to determine time operating relay before and after modifying fault current and the types of inverse characteristics for each relay. The operating times of OC and EF relays during fault are computed according to the type of ground in and operation characteristic of relays as illustrated in Figure 3. The accuracy of the developed reliability index and the sensitivity of the protected system is crucial importance in time setting relay development. Multi agents which could be used to recalculate the reliability indices before and after modifying fault current by the second agent was proposed in Figure 3 to calculate the frequency and duration indices reliability for each state which is called the recursive algorithm agent (RAG).

1f



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Figure 3 Multi agent based protection and DG location

The models of agents were developed in a JADE platform hosting. Also, this Chapter has focused on the application of MAS for reliability assessment by presenting a new approach to modify the time operating relay. VIII. THE DISCUSSION OF THE SIMULATION RESULTS The results of the proposed method are carried out through simulation to demonstrate the effectiveness of the proposed methods using JADE package that compatible with the FIPA standards. Details of the case studied are selected based on the network area in Malaysia (DISCO-Net) as shown in Figure 5 and test system (69-bus radial distribution network) as described in Figure 4. This test system is a 12.66 kV radial distribution system with69-bus reliability test system which consist of 7-lateral distribution feeder on radial distribution system,69 load points, 69 fuses, 69 transformers and 1 circuit breaker at the substation.

Figure 4 Single line diagram of the 69 bus test system

In IEEE 69 bus system, the 69-bus system has 68 sections with the total customer load of 4.014 MW and 2.845 Mvar. It has 12.66 kV base and line loading of distribution of 10 MVA and a load factor of 0.63.This

system was selected as shown in Figure 5.4 to study DG location. Previous experiments has advised to stay reserve margins in the range of 15-25% of the peak load, and also, the growth of the load is 0.01% for each year, which results in an increase of nearly (1.5 to 2) MW load demand. The new techniques for finding the DG location based on MAS model for power system reliability and the new index relays setting are presented in table 1. In Table 2, Ref[13] is from (Duong, 2014), Ref[14] is from (Naresh et. al, 2006), Ref[15] is from (Shukla et. al, 2008), Ref[16] is from (Kumar and Shankar, 2013), Ref[17]is from (Hosseini and Baghipour, 2013), Ref[18] is from (Saeid Soudi, 2013), Ref[19]is from (Amisha et. al, 2013) and last Ref[20]is obtained from (Sanjay, 2014) .

Figure 5 Single line diagram of Malaysia Network of DISCO-Net power system

The performance of the proposed suitable DG allocation method when applied to IEEE 69 bus radial distribution system is compared with the other methods in Table 2. All the methods, it is spotted that the real power loss (I2R) of distribution system is mentioning to be reduced after allocating a DG unit in the distribution system. In order to quantify the most effective DG unit, the improvement in the loss reduction is compared with the DG size in MVA injection. This gives loss savings in kW per unit DG MVA injection. A better value indicates the allocation results in high loss reduction, whereas a poor value for which indicates of allocation results in less loss reduction comparatively. It is very clear from Table 2 that, the suitable locations are buses number (Bus 33, Bus 34), (Bus 49, Bus 50) because the proposed method yields loss reduction of 41.95, 43.32 kW DG injections respectively. This problem can be resolved by installing a number of generators with a capacity of 0.5 MW. The simulations of suitable DG location were carried out in some cases and have been summarized in Table1 . the value of the index (X0/X1) changed from 1.0755 to 0.8815 due to the installation of one generator to the bus. While, Table 1 shows four buses (Bus 33 , Bus 34 , Bus 49 and Bus 50) which the value of the two grounding indices(X0/X1) and(VD)are limited to a range between 1 to 3 for index



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(X0/X1) and between 0.866 pu to 1.732 pu for the index of voltage (VD). Figure 6 shows all the communication between agents and reliability index for the best location to install DG.

Figure 6 Visualization of multi agents for installing DG

Tables 3 and 4 show the measurement of the reliability indices for both frequency and duration of the event. By using the recursive algorithm, it is obtained4 probabilities of the system of 2 MW for when the cases of failure one generator as in Table 3. While, 6 probabilities are obtained of the system 2 MW when the cases of failure two generator as in Table 4. Figure 7 shows the evaluation of TOP after modifying fault current for all bus bars.

Figure 7 Visualization of multi agents for for opreating time relay

CONCLUSION This paper presents the conclusions and recommendations of the work done on MAS to assess DG locations and protection relays effect on system reliability. The results suggest a new approach for finding DG location using a multi-agent system. In the simulation presented a lot of sites and possibilities to add DG by implementation grounding indexes through (IGA). At the same time, the use of indices for reliability evaluation in a power grid is one solution to

the power system problems, so the design shows the importance of a multi-agent system to determine the best location to add DG by sending data to give a reliable assessment of generation systems using RAG that allows use as a promising approach to large-scale networks. The use of agents for solving power system problems has been reviewed find new index reliability by TOP to improve the quality and efficiency of the power system according to failure rates has been decreased. Another focus the high speed of protection and control due to the use of MAS will facilitate the incorporation of smart grid in the power system for increasing the security of the power system based on the developed techniques and decreasing the economic loss due to damaged equipment. REFERENCES [1] Yousefian, R. and Monsef, H. 2011. DG-allocation based on

reliability indices by means of Monte Carlo simulation and AHP. In Environment and Electrical Engineering (EEEIC), 2011 10th International Conference on, pp. 1-4. IEEE.

[2] Shalash, N. A., & Ahmad, A. Z. 2013. Distributed generation location using multi-agent system for reliability assessment. Power and Energy Engineering Conference (APPEEC).

[3] Kroposki, B. 2003. DG Power Quality ,Protection and Reliability. Case Studies Report. GE Corporate Research and Development Niskayuna, New York, NREL/SR-560-34635.

[4] Wan, H., Wong, K.P. and Chung, C.Y. 2008.Multi-agent application in Protection Coordination of Power System with Distributed Generations.pp.1-6.

[5] Billinton, R and Kuruganty, P. R. S. 1981.Protection system modeling in a probabilistic assessment of transient stability.IEEETrans. Power App. Syst., vol. PAS-100, no. 5, pp. 2163-2170.

[6] Cheng, J.W.M., McGillis, D.T. and Galiana, F.D. 2000. Power System Reliability in a Deregulated Environment. Conference on Electrical and Computer Engineering, Vol. 2, pp. 765-768.

[7] Shalash, N. A., & Ahmad, A. Z. 2014. Power system generation reliability assessment based on multi-agents technique. International Journal of Smart Grid and Clean Energy, vol. 3, no. 2, April 2014: pp. 168-178

[8] Shalash, N. A., & Ahmad, A. Z. 2016.. 2013. New reliability index for power system protection based on multi-agent technique. 3rd International Conference on Electric Power and Energy Conversion Systems (EPECS).

[9] Balakrishnan, J. 2008. Review of the effects of transformer configuration on distributed generator interconnection, industrial and information systems, ICIIS 2008. IEEE Region 10 and the Third International Conference on,pp.1-6, 8-10 Dec. 2008.

[10] IEEE Committee Report.1989. Computer representation of overcurrent relay characteristics. IEEE Power Engineering Review, July 1989.

[11] Marko, C. 2011. Assessment of power system reliability methods and applications. Springer-Verlag.

[12] Shalash, N. A., & Ahmad, A. Z., and Jaber, A. S."Multi-Agent Approach to Reliability Assessment of Power System Generation Using Fuzzy logic." International Journal of Simulation--Systems, Science & Technology 17.26 (2016)..

[13] Anant, O. and Kenedy, A. 2009.Power system contingency analysis using multiagent systems, World Academy of Science, Engineering and Technology. pp. 355–360.

[14] Abedini, R., Pinto, T., Morais, H.and Vale, Z. 2013. Multi-agent approach for power system in a smart grid protection context.PowerTechConference IEEE on Grenoble 16-20 June. pp 1 - 6.

[15] Duong, Q.H. and Nadarajah, M. 2013. Multiple Distributed Generator Placement in Primary Distribution Networks for



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Loss Reduction, IEEE Transactions on Industrial Electronics, Vol. 60, No. 4.

[16] Naresh, A., Pukar, M. andMithulanathan, N. 2006.An analytical approach for DG allocation in primary distribution network. Electr

[17] Kumar P.A. and Shankar, B.2013. Loss Reduction in Radial Distribution Network by Optimal Placement of Type – I DG Using Modified Direct Search. Journal of Engineering Research and Applications Vol. 3, Issue 5, pp.1600-1603

[18] Fenghui, R., Minjie, Z. and Sutanto, D. 2013. A Multi-Agent Solution to Distribution System Management by Considering Distributed Generators. Power Systems, IEEE Transactions on Vol. 28, Issue 2, pp 1442 - 1451.

[19] Saeid, S. 2013. Distribution System Planning With DistributedGenerations Considering Benefits and

Costs.I.J.ModernEducation and Computer Science,vol.9, pp.45-52

[20] Amisha, G.P.,Priya, V.and Kalyani, S.2014.Optimal Design of Multitype DG Resources Using Particle Swarm Optimization. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 3, Issue 4.

Table 1 Probability grounding when install DG based on X0/X1

ratioand VD (IEEE 69 bus)

Table 2 Probability grounding when install DG based on X0/X1 ratioand VD (IEEE 69 bus)

Table 3 Cases of operating time of EF relay after modified



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Table 4 Cases of operating time of EF relay after modified

Table 5 FAD measured when in cases of failure two generator

Table 6 FAD measured when in cases of failure one generator

Table 7 Simulation results of reliability indices after fault current modification

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MULTI-AGENT APPROACH FOR RELIABILITY ASSESSMENT AND