The purpose of this project is to develop a system that can record the presence of students using QR code. Previously, teachers needed to use paper to record student attendance. There are many problems that arise when teachers use paper as a record of student attendance such as a loss of attendance record and have been taken a long time. The objective of the study is to design a prototype interface of student attendance system using QR code and casing of QR code scanner, to build up the design the prototype interface of student attendance system using QR code and casing of QR code scanner and test the functionality the prototype of interface student attendance system using QR code and casing of QR code scanner. The interface for this system will be integrated with the LabVIEW Software to develop a database. This system can record the attendance of the student to school and a warning letter will be automatically generated when the student does not come to school in 2 days repeatedly. The development process of the Student Attendance System Using QR Code is based on the Prototype Development Model that consists of a five-phase model, that is planning, analysis, design, Prototype development, and testing. The design of casing for QR code scanner was developed using Sketchup software. LabVIEW Software is used to generate interface displays and built-in databases using Microsoft Excel. Overall, the system that has been developed can work well and achieves the objectives set

Ariffin, Arihasnida

Hamzah, Norhasyimah

Mohd Nasir, Muhamad Alfie Yazid

Rubani, Siti Nur Kamariah

Zakaria, Normah

English

UTHM Institutional Repository

MOPSO Approach for FACTS Device Installation in Power System   1S. A. Jumaat 1Fac. of Electrical and Electronics Eng. Universiti Tun Hussein Onn Malaysia  86400 Parit Raja, Batu Pahat, Johor, Malaysia      2I. Musirin 3M. M. Othman         1,2,3Faculty of Electrical Engineering Universiti Teknologi MARA Malaysia 40450 Shah Alam, Selangor, Malaysia     4H. Mokhlis 4Department of Electrical Engineering Faculty of Eng., University of Malaya, 50603 Kuala Lumpur, Malaysia       Abstract- This paper presents an approach to sigma multi-objective optimization particle swarm (σ-MOPSO) technique for optimal allocation of Flexible AC Transmission System (FACTS) devices. For this study, Static Var Compensator (SVC) is selected as a compensation device. Proposal σ-MOPSO technique has been implemented to minimize the transmission losses and the cost of investment in the system. Simulations performed on standard IEEE RTS 30-bus and IEEE 118-bus RTS. Results are compared with those obtained from the programming of multi-objective evolutionary technique (MOEP) in order to highlight its advantages.   I.    INTRODUCTION  Lately, the researcher in optimal placement of FACTS device in power system is oriented towards technical, economic, or both concerns. In [1] FACTS devices at different locations installation has been proposed to improve loading margin. [2], using NSGA II (Non-dominated Sorting Genetic Algorithm) approach is used to find optimum location and sizes of TCSCs in transmission system. A multi-objective genetic algorithm (MOGA) approach in [3] has been implemented for FACTS device placement with multi-objective functions to reduce the voltage violation and line overload. The evolutionary algorithm approach consists of an MOGA (multi-objective genetic algorithm was used for solving the problem of optimal allocation of FACTS device in power system by maximizing of system security and minimizing of investment cost. On the other hand, fuzzy decision making and efficient genetic algorithm to optimal placement of multi-type FACTS device with multi-objective multi-case problem have been proposed.    This study basically focuses on solving the optimal placement and size SVCs into the power system, from a technical and economic point of view, to provide a better power system security. To implement these problems various optimization criteria, σ-MOPSO technique has worked.           II. PROBLEM FORMULATION   This part explains the problem formulation of multi-objective optimization.   A. Multi-Objective Optimization (MOO)  The aim of optimization was the determined of optimal placement and size of FACTS devices into a power system with objective functions to minimize the transmission loss, and minimize the cost of investment. For that reason, the presented problem becomes a multi-objectives problem and this can be expressed, as   Min  f(x) = {fa(x), fb(x),…, fz(x)}   (1) Under  gJ(x) =0  J=1, …,M hk(x)≤0  k=1, …K   where fz number of objectives; M, K are numbers of equality and inequality constraints, respectively; x is decision vector [4].   B. The Total Transmission Loss The first objective function is to minimize the total transmission loss in the system, as given by the following function:                            ∑ ∑= =−=G PQDIGIN1lN1lPP1f             (2) where NG is the number of generator buses and NPQ is the number of load buses.   C. The Cost of Investment     The second objective for this research is to reduce the cost of investment FACTS device where represented by the total investment cost of SVC, CSVC:                              orSVC2 rCf ×=        (3) 2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO2013), Langkawi, Malaysia. 3-4 June 2013554where ror is the operating rate [MVar]. The investment cost given in US$/kVar, are determined by (4) [5-6], [12-13]:                                   38.127r3051.0r0003.0Cor2orSVC +−= (4)  III. Sigma MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION (σ-MOPSO)   In modern years, PSO [5-6], [7-8] has been presented as an effective population-based heuristic techniques in a flexible and balanced mechanism to develop and adjust to the global and local exploration capabilities. On the other hand, changes to SOPSO various objectives requires clarity of global and local best individuals because, in MOPSO no absolute global best, but a set of solutions that are not dominated. Additionally, there may be no single individual local best for each particle of the population. Choosing the global best and local best to guide a particle to be a non-trivial task in the domain of MO [9]. From this problem, and Teich Mostagim proposed MOPSO with sigma method in [9]. This method can choose the best local guides for each particle. The general flowchart to optimal location of FACTS device installation using σ-MOPSO shown in Fig. 1.   Fig.1 The General flowchart Sigma Multi-objective Particle Swarm Optimization (σ-MOSPO).      In the MOPSO method, velocity vector of each particle is modified and updated by (5):   ( )( )itlbest,it22itibest11ki1kiXP()RandomcXP()RandomcVwV−××+−××+×=+                         (5)  For finding the best local guide for each particle sigma, the method proposed by [9]. Each particle is assigned value σ with coordinate (fa, fb) for two objectives. Therefore, for two objectives, σ is written as  ( )( )                          2bf2af2bf2af+−=σ              (6) where fa and fb are the objective function 1 and objective function 2, respectively [10].    IV.   RESULTS AND DISCUSSION   With the purpose of understand the usefulness of the proposed MOPSO and MOEP techniques, IEEE-30 bus RTS and IEEE-118 bus RTS were tested to find the allocation of FACTS device. The FACTS device installation in power system the transmission loss and cost of investment minimization has been conducted at several load conditions subjected to bus 26 and 29 for IEEE-30 bus RTS while, bus 20 and bus 53 for IEEE-118 bus RTS.   A. Case 1: Qd26=20MVar in IEEE-30 bus RTS   Result for MOO problem when bus 26 is subjected 20MVar using MOPSO and MOEP are illustrated in Fig.1, Fig. 2, Table I and Table II. It observed that the transmission loss value has been minimized at this loading condition using MOPSO and MOEP techniques. The optimal value of transmission loss and cost of installation using MOPSO technique is 18.00790MW and US$869,460. Besides that, the best value of transmission loss and cost of installation using MOEP technique is 17.7994MW and US$569,910 as tabulated in Table I. Also, with the SVCs installation at load bus system the voltage profile has been increased greater than 0.95p.u. as shown in Table II. Besides that, Fig. 2 illustrates the single-line diagram of IEEE-30 Bus RTS with optimal location of SVCs installation when Qd26=20MVar using MOPSO. It can be observed that the placements of SVCs installation are the load bus and the generator bus in the system.        TABLE I RESULTS OF LOSS AND COST USING MOPSO AND MOEP WHEN Qd26=20MVAR Method Pre-Installation Post-Installation  Loss (MW) Loss (MW) Cost (US$) MOPSO  20.3393 18.0790       869,460  MOEP 17.7994       569,910    2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO2013), Langkawi, Malaysia. 3-4 June 2013555 Fig. 1: Sigma front for loss and cost minimization obtained using MOPSO and MOEP when Qd26=20MVar.  TABLE II RESULTS OF VOLTAGE USING MOPSO AND MOEP WHEN Qd26=20MVAR   Method Pre-Installation Post-Installation Voltage (p.u) Voltage (p.u) MOPSO  0.8383 0.9511 MOEP 0.9560  GG GGGG123456789101112131415161718 1920212223 24252627282930Loaded bus  Fig. 2 Single line diagram of IEEE-30 bus RTS with optimal location when Qd26=20MVar using MOPSO.  B. Case 2: Qd29=20MVar in IEEE-30 bus RTS.   Result for MOO when bus 29 is subjected to load of 20MVar using MOPSO and MOEP are illustrated in Fig. 3, Fig. 4, Table III and Table IV. It observed that the transmission loss values minimized with 20MVar connected at bus 29 using MOPSO and MOEP techniques. The optimal value of transmission loss and cost of installation using MOPSO technique is 17.5731MW and US$983,190 respectively. The best value of transmission loss and cost of installation using MOEP technique is 17.4899MW and US$1,072,900 as tabulated in Table IV. With the SVCs installation the voltage profile has been increased greater than 1.00p.u as shown in Table V. Besides that, Fig. 4 illustrates the single-line diagram of IEEE-30 Bus RTS with optimal location of SVCs installation when Qd29=20MVar using MOPSO. It can be observed that the placements of SVCs installation are at the generator bus.    Fig. 3 Sigma front for loss and cost minimization obtained using MOPSO and MOEP when Qd29=20MVar.  TABLE IV RESULTS OF LOSS AND COST USING MOPSO AND MOEP WHEN Qd29=20MVAR   Method Pre-Installation Post-Installation  Loss (MW) Loss (MW) Cost (US$) MOPSO 19.4699 17.5731           983,190  MOEP 17.4899        1,072,900   TABLE V RESULTS OF VOLTAGE USING MOPSO AND MOEP WHEN Qd26=20MVAR   Method Pre-Installation Post-Installation Voltage (p.u) Voltage (p.u) MOPSO 0.8582 1.0155 MOEP 1.0103  C. Case 3: Qd20=100MVar in IEEE-118 bus RTS.   Result for MOO when bus 20 is subjected to load of 100MVar using MOPSO and MOEP are illustrated in Fig. 5, Fig. 6, Table VI and Table VII. It observed that the transmission loss values reduced with 100MVar at bus 20 using MOPSO and MOEP techniques. The best value transmission loss and cost of installation using MOPSO and MOEP techniques are tabulated in Table VI. With the SVCs installation the voltage profile have been increased greater than 0.95p.u as shown in Table VII. Besides that, Fig. 6 illustrates the single-line diagram of IEEE-118 Bus RTS with optimal location of SVCs installation when Qd20=100MVar using MOPSO. It can be observed that the placements of SVCs installation are at the load bus.   2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO2013), Langkawi, Malaysia. 3-4 June 2013556GG GGGG123 456789101112131415161718 1920212223 24252627282930Loaded bus  Fig. 4 Single line diagram of IEEE-30 bus RTS with optimal location when Qd29=20MVar using MOPSO.   Fig. 5 Sigma front for loss and cost minimization obtained using MOPSO and MOEP when Qd20=100MVar.  TABLE IV RESULTS OF LOSS AND COST USING MOPSO AND MOEP WHEN Qd20=100MVAR   Method Pre-Installation Post-Installation  Loss (MW) Loss (MW) Cost (US$) MOPSO 143.7236 138.0091 3,973,000 MOEP 138.1706 4,090,300  TABLE V RESULTS OF VOLTAGE USING MOPSO AND MOEP WHEN Qd20=100MVAR   Method Pre-Installation Post-Installation  Voltage (p.u) Voltage (p.u) MOPSO 0.8291 0.9662 MOEP 0.9965   Fig. 6 Single line diagram of IEEE-118 bus RTS with optimal location when Qd20=100MVar using MOPSO.   D. Case 4: Qd53=100MVar in IEEE-118 bus RTS.   Result for MOO when bus 53 is subjected to load of 100MVar using MOPSO and MOEP are illustrated in Fig. 7, Fig. 8, Table VII and Table VII. It is observed that the transmission loss values reduce with 100MVar at bus 53 using MOPSO and MOEP techniques. The best value of transmission loss and cost of installation using MOPSO and MOEP techniques are tabulated in Table VI. With the SVCs installation the voltage profile have been increased greater than 0.95p.u. as shown in Table VII. Besides that, Fig. 8 illustrates the single-line diagram of IEEE-118 Bus RTS with optimal location of SVCs installation when Qd53=100MVar using MOPSO. It can be observed that the placements of SVCs installation are at the generator bus or load bus. It is same phenomena with Bus 26.   TABLE VI RESULTS OF LOSS AND COST USING MOPSO AND MOEP WHEN Qd53=100MVAR   Method Pre-Installation Post-Installation  Loss (MW) Loss (MW) Cost (US$) PSO 142.0012 138.3295 2,241,400 EP 138.0984 2,220,800  TABLE VII RESULTS OF VOLTAGE USING MOPSO AND MOEP WHEN Qd53=100MVAR   Method Pre-Installation Post-Installation  Voltage (p.u) Voltage (p.u) PSO 0.8536 0.9526 EP 0.9595  2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO2013), Langkawi, Malaysia. 3-4 June 2013557 Fig. 7 Sigma front for loss and cost minimization obtained using MOPSO and MOEP when Qd53=100MVar.   GGGGGGGGGGGG1 2345 6 789101112131714151918171629273132114 11530262523222120113GGGGG24727173707475118767782838485 88 898690GGGGGGGGGGGG97969291102 10193879594100G8098G GGGGG GGG1061071051041081091121101111039935 36343733G78 8179116686949474645484344GGGG3940 41 42G 53 545256 55 59636458575051GGGG6160GG3862656667Loaded Bus  Fig. 8 Single line diagram of IEEE-118 bus RTS with optimal location when Qd53=100MVar using MOPSO.   E. Case 5: Qd53=150MVar in IEEE-118 bus RTS.   Result for MOO problem when bus 53 is subjected to load of 150MVar using MOPSO and MOEP are illustrated in Fig. 9, Fig. 10, Table IX and Table X. It is observed that the transmission loss values reduce with 150MVar at bus 53 using MOPSO and MOEP techniques. The best value transmission loss and cost of installation using MOPSO and MOEP techniques are tabulated in Table IX. With the SVCs installation the voltage profile have been increased greater than 0.95p.u. as shown in Table X. Besides that, Fig. 10 illustrates the single-line diagram of IEEE-118 Bus RTS with optimal location of SVCs installation when Qd53=150MVar using MOPSO. It can be observed that the placements of SVCs installation are the generator bus or load bus as same phenomena with Qd53=100MVar.    Fig. 9 Sigma front for loss and cost minimization obtained using MOPSO and MOEP when Qd53=150MVar.   TABLE IX RESULTS OF LOSS AND COST USING MOPSO AND MOEP WHEN Qd53=150MVAR   Method Pre-Installation Post-Installation  Loss (MW) Loss (MW) Cost (US$) PSO 147.7453 138.3249 4,915,800 EP 138.6179 4,914,200  TABLE X RESULTS OF VOLTAGE USING MOPSO AND MOEP WHEN Qd53=150MVAR   Method Pre-Installation Post-Installation  Voltage (p.u) Voltage (p.u) PSO 0.7896 0.9771 EP 0.9753    GGGGGGGGGGGG1 2345 6 789101112131714151918171629273132114 11530262523222120113GGGGG24727173707475118767782838485 88 898690GGGGGGGGGGGG97969291102 10193879594100G8098G GGGGG GGG1061071051041081091121101111039935 36343733G78 8179116686949474645484344GGGG3940 41 42G 53 545256 55 59636458575051GGGG6160GG3862656667Loaded Bus  Fig. 10 Single line diagram of IEEE-118 bus RTS with optimal location when Qd53=150MVar using MOPSO. 2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO2013), Langkawi, Malaysia. 3-4 June 2013558 V. CONCLUSION   This paper has presented multi-objective optimization termed as MOPSO and MOEP in implementing the optimal placement of SVCs installation. The combination of transmission loss and minimization of installation cost as objective function has been solved for IEEE 30 bus RTS with bus 26 and 29 subjected to 20MVar. The study has been implemented on IEEE-118 bus RTS with bus 20 and 53 are subjected to 100MVar and 150MVar respectively. Both the MOPSO and MOEP techniques performed well in most cases. Simulations results demonstrated that the proposed MOPSO technique is flexible for multi-objective optimization problem in other power system network.    ACKNOWLEDGMENT  The authors would like to thank the Research Management Institute (RMI), Universiti Teknologi MARA, Malaysia and the Ministry of Higher Education, Malaysia (MOHE) under Grant 600-RMI/ERGS 5/3 (18/2012), for the financial support of this research.   REFERENCES  [1] A. A Athamneh, W. J. Lee, “Benefits of FACTS Devices for power exchange among Jordanian Interconnection with other countries”, IEEE PES General Meeting, Jun 2006.  [2] T. T. Nguyen, A. Yousefi, “Multi-Objective approach for optimal location of TCSC using NSGA II”, 2010 International Conference on Power System Technology, 2010. [3] D. Radu, Y. Besangar, “A Multi-objective Genetic Algorithm Approach to Optimal Allocation of Multi-type FACTS devices for Power Systems Security”, IEEE Power Engineering Society Meeting, 18-22 June, 2006.   [4] M. Bellazzoug, M. Boudour, “FACTS Placement Multi-objective Optimization for Reactive Power System Compensation”, 7th International Multi-Conference on Systems, Signals and Devices, 2010.  [5] S. A. Jumaat, I. Musirin, M.M. Othman, H. Mokhlis, “Comparison of FACST Device Installation in Power Transmission System”, International Review of Modelling and Simulations (IREMOS), Vol. 5, No. 4, August 2012. pp. 1679-1689.  [6] S.A. Jumaat, I. Musirin, M.M. Othman, H. Mokhlis, “Computational Intelligence Based Technique for Power System Security”, Journal of Theoretical and Applied Information Technology, 31st December 2012, Vol. 46, No.2 pp.537-549.  [7] J. Kennedy, R. Eberhant, “Particle Swarm Optimization”, IEEE International Conference Neural Network, Perth, Australia, 1995, pp. 1942-1948.  [8] S. Mostaghim, J.Teich, “Strategies for Finding Good Local Guides in Multi-objective Particle Swarm Optimization (MOPSO)”, IEEE Symposium Swarm Intelligence, 2003, pp.26-33. [9] S. Mostaghim, J.Teich, “Strategies for Finding Good Local Guides in [10] Multi-objective Particle Swarm Optimization (MOPSO)”, IEEE Symposium Swarm Intelligence, 2003, pp.26-33. [11] J. Hazra, A.K.Sinha, “Congestion Management Using Multi-objective Particle Swarm Optimization”, IEEE Transactions on Power Systems, Vol. 22, No. 4, November 2007.    [12] S. A. Jumaat, I. Musirin, M.M. Othman, H. Mokhlis, Optimal Placement and sizing of Multiple FACTS Devices Installation”,  Power and Energy (PECon), 2012 IEEE International Conference on, pp. 145-150. [13] S. A. Jumaat, I. Musirin, M.M. Othman, H. Mokhlis, Evolutionary Particle Swarm Optimization (EPSO) based Technique for Multiple SVCs Optimization”,  Power and Energy (PECon), 2012 IEEE International Conference on, pp. 183-188.    2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO2013), Langkawi, Malaysia. 3-4 June 2013559

Student’s attendance system using QR code

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