An experimental study had been carried out to investigate the mechanical properties, expansion and shrinkage of fibre reinforced concrete composite (FRC). However, instead of using single type fibre of either steel (SF) or polypropylene (PPF), this study also combined the two types in one mix.The mechanical properties investigated in this study include compressive strength, splitting tensile strength and flexural strength. Three different FRC mix proportions and one normal concrete (control) were casted which includes (a) 75% SF, (b) 75% SF + 25% PPF, (c) 25% PPF, and (d) 0% fibre for control (PC). Meanwhile, the volume fraction, Vf for the FRC was fixed at 1.5% and the concrete strength was designed to achieve grade C60 at 28 days. The results show that the use of fibres in concrete decreased the workability of concrete. In addition, concrete mix with both SF and PPF produced the highest splitting tensile and flexural strengths by an increase of 75.9% and 86.5%, respectively as compared with the control. Furthermore, expansion and shrinkage of FRC was found to be less than the control. It can be concluded that the combined SF and PPF in concrete gives the most appropriate combination as regards to the highest flexural and splitting tensile strengths, and also reduced the shrinkage strain

Mohd Sam, Abdul Rahman

Sarbini, Noor Nabilah

Syahrizal Ibrahim, Izni

Wan Jusoh, Wan Amizah

UTHM Institutional Repository

Optimal Location and Sizing of SVC Using Particle Swarm Optimization Technique.   1,2Siti Amely Jumaat  1Faculty of Elect. and Engineering,  Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia 1sitiamely1979@gmail.com   2Ismail Musirin, 3Muhammad Mutadha Othman, 4Hazlie Mokhlis 2Faculty of Electrical Engineering, Universiti Teknologi MARA Malaysia, 40450 Shah Alam,  Selangor, Malaysia 2ismailbm1@gmail.com 3mamat505my@yahoo.com   4Department of Electrical Engineering, Faculty of engineering, University of Malaya,  Kuala Lumpur, Malaysia  4hazli@um.edu.m  Abstract— This paper describes optimal location and sizing of static var compensator (SVC) based on Particle Swarm Optimization for minimization of transmission losses considering cost function. Particle Swarm Optimization (PSO) is population-based stochastic search algorithms approaches as the potential techniques to solving such a problem. For this study, static var compensator (SVC) is chosen as the compensation device. Validation through the implementation on the IEEE 30-bus system indicated that PSO is feasible to achieve the task. The simulation results are compared with those obtained from Evolutionary Programming (EP) technique in the attempt to highlight its merit.    Keywords-component; optimal location; optimal sizing; Particle Swarm Optimization; transmission loss minimization; static var compensator.   I.  INTRODUCTION  The FACTS is a concept proposed by N.G. Hingorani [1] a well-known term for higher controllability in power systems by means of power electronics devices. FACTS devices can provide benefits in increasing system transmission capacity and power flow control flexibility and rapidity [2]. Population base, cooperative and competitive stochastic search algorithms are very popular in the recent year in the research area of computational intelligence. PSO algorithm was developed by Kennedy and Eberhart based on the social behaviors of animal swarms (e.g. bird blocks and fish schools) [17]. PSO is also applied for solving various optimization problems in electrical engineering [2, 3, 18-20]. Optimal locations of different types of FACTS devices in  the power system has been attempted using different Evolutionary Programming (EP) techniques such as Hybrid Tabu Search and Simulated Annealing (TS/SA), GA, Repetitive Power Flow method (RPF), BA and Fuzzy decision making and PSO. The maximum increase in system loadability is achieved by GA and PSO techniques with an optimal numbers of five TCSCs devices in the system. In [9], GA and PSO are used to optimize the parameters of TCSC. However, PSO have more advantageous than that of GA. PSO gives a better balanced mechanism and better variation to the global and local exploration abilities. Moreover, it can be applied to solve various optimization problems in power system such as power system stability enhancement and capacitor placement problems [10].This paper presents PSO technique for loss minimization in power system by using SVC. PSO was adopted to optimize the SVCs location and sizing to be installed in power transmission network. The PSO and EP techniques were performed on the IEEE 30-bus system have indicated that the proposed methods are worth in loss minimization scheme.  II. FACTS DEVICE  Flexible AC Transmission Systems (FACTS) devices have several types namely: thyristor controlled static compensator (TCSC), static var compensator (SVC), unified power flow controller (UPFC), static compensator (STATCOM), and thyristor controlled phase shifter transformer (TCPST) [11-12].  The SVC is a shunt type FACTS device defined as a shunt connected static var generator or absorber whose output is adjusted to exchange capacitive or inductive current so as to maintain or control specific parameters of the power system, typically the bus voltage [13]. The SVC can inject or absorb its reactive power (QSVC) at a chosen bus. It injects reactive power into the system it QSVC < 0 and absorbs reactive power from the system if QSVC > 0 [14].  The working range of SVC is between 0MVar and +100MVar [21]. The SVC is modeled as a generator or absorber of reactive power as shown in Figure 1.a.  It is modeled as an ideal reactive power injection at bus i, as shown in Figure 1.b. The injected 2011 First International Conference on Informatics and Computational Intelligence978-0-7695-4618-6/11 $26.00 © 2011 IEEEDOI 10.1109/ICI.2011.58312power at bus i is: [15 - 16].            Figure 1.a Block diagram of SVC            Figure 1.b Mathematical model of SVC   III. OPTIMIZATIONS TECHNIQUE  A. Particle Swarm Optimization (PSO)   The PSO provides a population-based search procedure in which individuals called particles and changes their positions. The position of each particle is presented in X-Y plane. Each particle moves to the new position using velocity according to its own experience, called as Pbest. Gbest is the overall best value obtained so far by any particle in the population. By time to time, the PSO consists of velocity changes of each particle towards its Pbest and Gbest [18-19]. Each particle tries to modify its current position and velocity according to the distance between its current position and Pbest, and the current position and Gbest. After finding the best values the particle updates its velocity and position. Velocity of each particle can be modified. [2,3, 20].  The flowchart of PSO is shown in Figure 2.     B. Evolutionary Programming (EP)   The Evolutionary Programming (EP) is one of the artificial intelligent method is introduction by David B. Fogel in 1960 [21] was inspired from natural selection process to find the global optimum of complex problem [22]. It is evolutionary algorithms are based on computational models of fundamental evolutionary processes such as initialization, mutation, selection and reproduction. In [50], proposed EP to define the optimal placement of FACTS device for maximization the total transfer capability (TTC) of power system. EP also searches for FACTS parameters, FACTS locations, and the real power generations except the slack bus in power system, the real power loads in sink area and generation bus voltages. In [22] proposed a loss sensitivity approach for placement of Phase Shifter Series Capacitors (PSSC) and Static VAR Compensators.  In this research, EP technique was used to optimal the sizing of UPFCs with objective function to minimize the loss and improve the voltage profile. The flowchart of EP is shown in Figure 3.  Start Set the loading Find the Qload before SVCs installationFind the Qload before SVCs installationGenerate random no.x as a control variable of SVC (x1, x2, … xn), n = no. of SVCs Calculate Fitness IPbest and Gbest value Update the velocity and position Calculate Fitness IIPbest_new and Gbest_new value Converge?Calculate the cost of installation END NOYES  Figure 2: A Flowchart of Particle Swarm Optimization (PSO)  IV. TEST RESULTS A. Transmission Loss Reduction and Cost of Installation   In order to realize the effectiveness of the proposed PSO technique, the IEEE 30-bus system was tested to find the optimal location and sizing of SVC. The parameters of the optimization algorithm can be referred to [2, 3, 14, 20]. Results for transmission loss reduction when load i.e. buses  maxmin QSVCQQ ≤≤ (1)                             SVCQisQ =Δ31326 and 29 are subjected to load variation are tabulated in Table I, and II. The location and sizing of SVC to achieve loss reduction at several loading conditions can be referred to the same table. For instance in Table I with loading condition of 20MVar, the transmission loss has been reduced to 17.5478MW. In order to achieve this, the location of SVC at Bus 26 and the sizing of SVC is 20.1679MVar as indicated in the table. The cost of installation at this scenario is US$1,083,300. From Table II it is observed that the value of transmission losses decrease rapidly and the cost of installation increase accordingly as the reactive power loading increase. Figure 4 shown the cost of installation SVC when load variation on Bus 26.      Figure 3: A Flowchart of Evolutionary Programming (EP)        TABLE I: TRANSMISSION LOSS REDUCTION LOAD VARIATION AT BUS 26 Loading  SVC  SVC sizing  Loss  IC Condition  location bus  (Mvar) (MW)  (US$) Qd26(Mvar)   s1  5 26 6.2032 17.5415           88,284  10 26 10.2864 17.5466         429,440  15 26 16.6998 17.5415         697,340  20 26 20.1679 17.5478      1,083,300 25 26 27.1565 17.5432      1,869,000 30 26 27.9195 17.5919      2,916,700    Figure 3: Results for Transmission Loss Reduction at Bus 29   Figure 4: Cost of Installation SVC When Load Variotion at Bus 26   The results for location and sizing of SVC to achieve optimal loss reduction at with load variation at Bus 29 are tabulated in Table II. For instance, at loading condition of 20MVar the transmission loss has been reduced 17.5582MW. In order to achieve this, the location of SVC is Bus 29 and the sizing of SVC is 23.7697MVAr as indicated in Table II. 314The cost of installation at this scenario is US$1,004,800. It is also shown the installation of SVC has significantly reduced the transmission loss in the system at all loading conditions as shown in Figure 5. Figure 6 shown the cost of installation SVC when load variation at Bus 29 is subjected to the system. Result shows that the implementations of PSO have reduced the transmission loss of the system indicating it as a feasible technique to perform optimization process in practical system.   TABLE II: TRANSMISSION LOSS REDUCTION LOAD VARIATION AT BUS 29 Loading  SVC  SVC sizing  Loss  IC Condition  location (Bus) (Mvar) (MW)  (US$) Qd29(Mvar)   s1  5 29 9.0711 17.5591            195,055  10 29 13.0427 17.5577            448,910  15 29 16.6507 17.5635            664,840  20 29 23.7697 17.5582         1,004,800  25 29 27.2707 17.5598            996,490  30 29 32.8218 17.5580         2,338,300      Figure 5: Results for Transmission Loss Reduction at Bus 29   B. Comparative Studies with Other Technique.   Comparative studies were conducted with respect to the results obtained using EP. The results are tabulated in Table III - VIII for load subjected to buses 26 and 29.     Figure 6: Cost of Installation SVC When Load Variotion at Bus 29    TABLE III: TRANSMISSION LOSS REDUCTION LOAD VARIATION  AT BUS 26 PERFORMED USING PSO AND EP.   In Table III at loading condition of 15MVar; PSO managed to reduce the transmission loss from 19.0625MW to 17.5415MW (7.98%), while EP managed to reduce the transmission loss to 18.5395MW (2.74%).  The same scenarios can be observed as well with 20MVar and 30MVar. It is shown that, PSO technique can be optimizing the transmission loss lower than EP. In Table IV at loading condition 15MVar; PSO managed to increase the voltage profile form 0.8896p.u to 1.0290pu (16%), while EP managed to increase the voltage profile to 0.9576pu (8%). The same scenarios can be observed as well with 20MVar and 30MVar. On other hand, Table V shown the cost of installation FACTS device when load variation at Bus 26. At loading condition of 15MVar; PSO managed the cost of installation FACTS device is US$697,340, while EP managed the cost of installation FACTS device is US$456,960. The cost of installation is related with reduction of reactive power in the system.         Loading  Pre-Installation Post-Installation  Condition  Loss (MW) PSO  EP Qd26(Mvar) Loss (MW) % Loss (MW) % 5 17.7175 17.5415 0.99% 17.6625 0.31% 15 19.0625 17.5415 7.98% 18.5395 2.74% 20 20.3393 17.5478 13.72% 19.4151 4.54% 30 26.5184 17.5919 33.66% 17.6128 33.58% 315TABLE IV: VOLTAGE PROFILE IMPROVEMENT LOAD VARIOTION AT BUS 26 PERFORMED USING PSO AND EP.   TABLE V: COST OF INSTALLATION FACTS DEVICES LOAD VARIOTION AT BUS 26 PERFORMED USING PSO AND EP.   In Table VI at loading condition of 15MVar; PSO managed to reduce the transmission loss from 18.6839MW to 17.5648MW (5.99%), while EP managed to reduce the transmission loss to 17.5636MW (6%).  The same scenarios can be observed as well with 20MVar and 30MVar. It is shown that, Both PSO and EP technique are comparable to optimizing the transmission loss. In Table VII at loading condition 15MVar; PSO managed to increase the voltage profile form 0.9p.u to 1.0412pu (16%), while EP managed to increase the voltage profile to 1.0214pu (13%). The same scenarios can be observed as well with 20MVar and 30MVar. On other hand, Table VII shown the cost of installation FACTS device when load variation at Bus 29. At loading condition of 15MVar; PSO managed the cost of installation FACTS device is US$681,960, while EP managed the cost of installation FACTS device is US$664,840.   IV. CONCLUSION  This paper has presented the application of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) technique for minimize the transmission loss and monitoring the voltage profile and SVC installation cost. In this study, PSO and EP methods are applied on bus 26 and 29 of IEEE 30-Bus system. From the simulation results demonstrated that the proposed PSO technique is feasible for loss minimization scheme in power system network.   However, PSO is superior that EP in terms of loss minimizations. For the future work, other FACTS devices such as TCSC can be incorporated together to achieve similar task.   TABLE VI: TRANSMISSION LOSS REDUCTION LOAD VARIOTION AT BUS 29 USING PSO AND EP Loading  Pre-Installation Post-Installation  Condition  Loss (MW) PSO  Loss (MW) Qd29(Mvar) Loss (MW) % Loss (MW) % 5 17.7284 17.5576 0.96% 17.5591 0.95% 15 18.6839 17.5648 5.99% 17.5635 6.00% 20 19.4699 17.5577 9.82% 17.5582 9.82% 30 22.7158 17.5604 22.70% 17.5580 22.71%  TABLE VII: VOLTAGE PROFILE IMPROVEMENT LOAD VARIOTION AT BUS 29 PERFORMED USING PSO AND EP. Loading  Pre-Installation   Post- Installation  Condition  Voltage (p.u) PSO  EP Qd29(Mvar) Voltage  (p.u) % Voltage (p.u) %  5 0.9800 1.0307 5% 1.0296 5% 15 0.9000 1.0412 16% 1.0214 13% 20 0.8582 1.0292 20% 1.0338 20% 30 0.7423 1.0373 40% 1.0283 39%  TABLE VIII:  COST OF INSTALLATION FACTS DEVICES LOAD VARIOTION AT BUS 29 PERFORMED USING PSO AND EP.  Loading  Post-Installation  Condition  PSO  EP Qd29(Mvar) Cost (US$) Cost (US$) 5 $111,170 $195,055 15 $681,960 $664,840 20 $1,000,900 $1,004,800 30 $2,344,900 $2,338,300   REFERENCES  [1] G.Hingorani, “Power electronics in electrical utilities: role of power electronics in future power systems”, in Proc 1988 IEEE, Vol. 76 No, 4 April 1988, pp.481-482. 1988  [2] Saravanan. M, Slochanal. S.M.R, Venkatesh. P, Abraham, P.S, “Applications of PSO Technique for Optimal Location of FACTS Devices Considering System Loadability and Cost of Installation,” in Proc 2005 7th International Power Engineering Conference (IPEC). Pp. 716 - 721 Vol. 2 2005 [3] G.I.Rashed, H.I.Shaheen, S.J.Cheng, “Optimal Locations and Parameters Settings of Multiple TSCSs for Increasing Power System Loadability Based on GA and PSO techniques, in Proc 2007 Third IEEE International Conference on Natural Computation (ICNC 2007), 2007. 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K, Hariharan. B, Parasseri. F.P, Antony. D.S, and  Subair, B, “Optimal Placement of Static VAr Compensators (SVC’s) Using Particle Swarm Optimization,” in Proc 2010, International Conference on Power, Control and Embedded Systems (ICPCES), 2010 , pp. 1 – 4, 2010.  [21] G. Wei, “Comparison Study of Genetic Algorithm and Evolutionary  Programming”, Proc. of the Third International Conference on           Machine Learning and Cybernetics, Vol. 1, No. 1, 2004, pp. 204-209. [22] G. B. Fogel and D.B. Fogel, “Continuous Evolutionary Programming:          Analysis and Experiments”, An International Journal of Cybernetics          and Systems, Vol. 26, 1995, pp. 79-90.              317

Mechanical and shrinkage properties of hybrid steel and polypropylene fibre reinforced concrete composite

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