2012
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Corona Ring Optimization for Different Cases of Polymer Insulators Based on its Size and Distance
Corona Ring Optimization for Different Cases
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This paper describes the impact of the distance and the size of the corona ring, on the magnitude anddistribution of electrical potential across the polymer insulators. The procedure is based on finiteelement method numerical analysis and stochastic optimization algorithm of differential evolution(DE). The optimal selection of corona ring has a significant impact on the control of potential and,consequently, on dielectric strengths, both inside and outside the insulator. This method is used foroptimized determining of the size and the distance of corona ring from polymer insulators in existingstructures.
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7
Corona
electrical field
FEM
Optimization
polymer
Size
[B.Marungsri, H.Shinokubo, R.Matsuoka and##S.Kumagai, "Effect of Specimen Configuration##on Deterioration of Silicone Rubber for##Polymer Insulators in Salt Fog Ageing Test",##IEEE Transactions on Dielectrics and##Electrical Insulation, vol. 13, no. 1, February##[2] A. J. Phillips, J. Burnham, W. Chisholm, A.##Gillespie and T. Saha, "Electric Fields on AC##Composite Transmission Line Insulators",##IEEE Transactions on Power Delivery, vol. 23,##no. 2, April 2008.##[3] K. Kato, X. Han and H. Okubo, "Insulation##Optimization by Electrode Contour##Modification Based on Breakdown##Area/Volume Effects", IEEE Transactions on##Dielectrics and Electrical Insulation, vol. 8, no.##2, April 2001.##[4] H. Mei, G. Peng, H. Dai, L. Wang, Z. Guan,##and L. Cao "Installing Insulation Jacket to##Improve Outdoor Insulation Performance of##Composite Insulator", IEEE Transactions on##Dielectrics and Electrical Insulation, vol. 18,##no. 6, December 2011.##[5] P. Kitak, J. Pihler, I. Ti˘car, A. Stermecki, O.##Bíró, and K. Preis, "Potential Control Inside##Switch Device Using FEM and Stochastic##Optimization Algorithm", IEEE Transactions##]
Multiobjective Based Optimization Using Tap Setting Transformer, DG and Capacitor Placement in Distribution Networks
Multiobjective Based Optimization
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In this article, a multiobjective function for placement of Distributed Generation (DG) and capacitors with thetap setting of Under Load Tap Changer (ULTC) Transformer is introduced. Most of the recent articles have paidless attention to DG, capacitor placement and ULTC effects in the distribution network simultaneously. Insimulations, a comparison between different modes was carried out with, and without tap setting of ULTC.Simultaneous DG, capacitor placement, and ULTC transformer tap setting improve the voltage profile of loadbuses globally. In addition, they can also reduce loss and increase Available Transfer Capability (ATC). TheIEEE 41bus radial distribution network is used to illustrate the effectiveness and feasibility of the proposedapproach.
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DG placement
Capacitor Placement
ULTC Transformer
Loss reduction
Voltage profile
available transfer capability
multiobjective function
[[1] M. Ladjavardi and M. A. S. Masoum##“Genetically Optimized Fuzzy Placement and##Sizing of Capacitor Banks in Distorted##Distribution Networks,” IEEE Transactions on##Power Delivery, Vol. 23, No. 1, Jan 2008.##[2] Ch. Chang “Reconfiguration and Capacitor##Placement for Loss Reduction of Distribution##Systems by Ant Colony Search Algorithm,”##IEEE Transactions on Power Systems, Vol. 23,##No. 4, Nov 2008.##[3] I. C. Silva, S. Carneiro, E. J. Oliveira, J. S.##Costa, J. L. Pereira and P. A. Garcia “A##Heuristic Constructive Algorithm for Capacitor##Placement on Distribution Systems,” IEEE##Transactions on Power Systems, Vol. 23, No.##4, Nov 2008.##[4] H. Hedayati, S. A. Nabaviniaki and A.##Akbarimajd “A Method for Placement of DG##Units in Distribution Networks,” IEEE##Transactions on Power Delivery, Vol. 23, No.##3, Jul 2008.##[5] M. MoeiniAghtaie, P. Dehghanian and S. H.##Hosseini “Optimal Distributed Generation##Placement in a Restructured Environment via a##MultiObjective Optimization Approach,” 16th##Conf. on Electrical Power Distribution##Networks (EPDC), Bandar abbas, Iran, 1920##April 2011.##[6] O. Aliman1, I. Musirin, M. M. Othman and M.##H. Sulaiman,” 5th International Power##Engineering and Optimization Conference##(PEOCO), Shah Alam, Selangor, Malaysia, 67##[7] A. K. Singh and S. K. Parida “Selection of##Load Buses for DG placement Based on Loss##Reduction and Voltage Improvement##Sensitivity,” IEEE International Conf. on##Power Eng., Energy and Electric Drives, p.p.##16 May 2011.##[8] C. Wang and M. H. Nehrir “Analytical##Approaches for Optimal Placement of##Distributed Generation Sources in Power##Systems” IEEE Transactions on Power ##[9] M. Kalantari and A. Kazemi “Placement of##Distributed Generation unit and Capacitor##Allocation in Distribution Systems using##Genetic Algorithm,” 10th International Conf.##on Environment and Electrical Eng., EEEIC##p.p. 15, 2011.##[10] M. Wang, and J. Zhong “A Novel Method for##Distributed Generation and Capacitor Optimal##Placement considering Voltage Profiles,” IEEE##power and Energy Society General Meeting,##p.p. 16 Jul 2011.##[11] M. Tarafdarhagh, A. Sadighmanesh, and M. R.##Hesamzadeh “Improvement of Load Bus##Voltages Considering the Optimal Dispatch of##Active and Reactive Powers,” 43rd##International Universities Power Engineering##Conf. (UPEC), Padova, Italy, (on CD) 14 Sep##]
A Novel Reference Current Calculation Method for Shunt Active Power Filters using a Recursive Algebraic Approach
A Novel Reference Current Calculation Method
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This paper presents a novel method to calculate the reference source current and the referencecompensating current for shunt active power filters (SAPFs). This method first calculates theamplitude and phase of the fundamental load current from a recursive algebraic approach blockbefore calculating the displacement power factor. Next, the amplitude of the reference mains currentis computed with the corresponding phase voltage. Finally, the difference between the actual loadcurrent and the reference source current is considered the reference compensating current to bedelivered by the SAPF. The proposed method is presented and applied to the control system of thevoltage source converter of SAPFs. The performance of the proposed method in reducing harmonicsand improving the power factor is examined with a SAPF simulation model. The results are comparedwith the instantaneous active and reactive pq power theory as other reference generation method.
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Power Quality
pq theory
recursive algebraic approach
reference source current
shunt active power filter
[[1] A. Bhattacharya, C. Chakraborty, “A shunt##active power filter with enhanced performance##using ANNbased predictive and adaptive##controllers,” IEEE Trans. Ind. Electron., vol.##58, no. 2, pp. 421–428,Feb. 2011.##[2] S. Rahmani, N. Mendalek, and K. AlHaddad,##“Experimental design of a nonlinear control##technique for threephase shunt active power##filter,”IEEE Trans. Ind. Electron., vol. 57, no.##10, pp. 3364–3375, Oct. 2010.##[3] S.H. Fathi, M. Pishvaei, and G.B.##Gharehpetian, “A frequency domain method##for instantaneous determination of reference##current in shunt active filter,” TENCON, IEEE##Region 10 Conference,14, 2006.##[4] Z. Salam, P. C. Tan, and A. Jusoh,##“Harmonics mitigation using active power##filter: A technological review,” Elektrika##Journal of Electrical Engineering, 8: 1726,##[5] T. Komrska, J. Zák, and Z. Peroutka,“Control##strategy of active power filter with adaptive##FIR filterbased and DFTbased reference##estimation,” Power Electronics Electrical ##Drives Automation and Motion (SPEEDAM),##2010 International Symposium on, Page(s):##1524 – 1529, 2010.##[6] G. Chen, Y. Jiang, and H. Zhou, “Practical##Issues of Recursive DFT in Active Power Filter##Based on CPC Power Theory, “Power and##Energy Engineering Conference, APPEEC##2009. AsiaPacific, Page(s): 1 – 5, 2009.##[7] H. Akagi, Yoshihira Kanazawa, and Akira##Nabae,“Instantaneous Reactive Power##Compensators Comprising Switching Devices##Without Energy Storage Components, ” IEEE##Transactions On Industry Applications, Vol.##IA20, No.3, May/June1998.##[8] M.A Kabir, U. Mahbub,“ Synchronous##Detection and Digital control of Shunt Active##Power Filter in Power Quality Improvement,”##IEEE Power and Energy Conference at Illinois##(IEEE PECI), University of Illinois at Urbana##Champaign, USA, 2011.##[9] A. Khoshkbar Sadigh, M. Farasat, S.M.##Barakati,“Active power filter with new##compensation principle based on synchronous##reference frame,” North America Power##Symposium (NAPS),DOI:##10.1109/NAPS.2009.5484077,2009.##[10] B.S. Kumar, K.R. Reddy, V. Lalitha,“PI, fuzzy##logic controlled shunt active power filter for##threephase fourwire systems with balanced,##unbalanced and variable loads,” Journal of##Theoretical and Applied Information##Technology 23 (2), pp. 122130 0 ,2011.##[11] A. Peiravi, R. Ildarabadi ,“Recursive algebraic##method of computing power system##harmonics,” IEEJ Transactions on Electrical##and Electronic Engineering Volume 6, Issue##4, pages 338–344, July 2011.##[12] B. Berbaoui , C.Benachaiba,“Power Quality##Enhancement using Shunt Active Power Filter##Based on Particle Swarm##Optimization,” Journal of Applied Sciences,##11: 37253731, 2011.##[13] H.Akagi, Y.Kanazawa and##N.Nabae,”Generalized theory of the##instantaneous reactive power in threephase##circuits”, in Proc. Int. Power El. Conf., pp##13751386, Tokyo, Japan, 1983##[14] G. Bhuvaneswari, M.G. Nair, “Design,##Simulation, and Analog Circuit##Implementation of a ThreePhase Shunt Active##Filter Using the I cos Algorithm,” Power ##Delivery, IEEE Transactions on, Volume23,##Issue: 2, Page(s): 1222 – 1235, 2008.##[15] P. Karuppanan , K. K. Mahapatra , “PLL with##PI, PID and Fuzzy Logic Controllers based##Shunt Active Power Line Conditioners,”##IEEE International Conference on Power##Electronics, Drives and Energy SystemsDec##21 o 23, 2010.##[16] L. A. Zadeh, “The concept of a linguistic##variable and its application to approximate##reasoning1,” Inf. Sci., vol. 8, pp. 199249,##[17] J.M. Mendel, Uncertain RuleBased Fuzzy##Logic: Introduction and new directions,##Prentice Hall, USA, (2000).##[18] J.M. Mendel, R.I. John and F. Liu, “ Interval##type2 fuzzy logic systems made simple”,##IEEE Trans. Fuzzy Syst., 14: 808821. 2006.##[19] P . Karuppanan , K. K. Mahapatra , “PI and##fuzzy logic controllers for shunt active power##filter — A report,” ISA Transactions vol. 51##]
Improved Binary Particle Swarm Optimization Based TNEP Considering Network Losses, Voltage Level, and Uncertainty in Demand
Improved Binary Particle Swarm Optimization Based TNEP Considering Network Losses,
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Transmission network expansion planning (TNEP) is an important component of power system planning. Itdetermines the characteristics and performance of the future electric power network and influences the powersystem operation directly. Different methods have been proposed for the solution of the static transmissionnetwork expansion planning (STNEP) problem till now. But in all of them, STNEP problem considering thenetwork losses, voltage level and uncertainty in demand has not been solved by improved binary particle swarmoptimization (IBPSO) algorithm. Binary particle swarm optimization (BPSO) is a new populationbasedintelligence algorithm and exhibits good performance on the solution of the largescale and nonlinearoptimization problems. However, it has been observed that standard BPSO algorithm has prematureconvergence when solving a complex optimization problem like STNEP. To resolve this problem, in this study,an IBPSO approach is proposed for the solution of the STNEP problem considering network losses, voltagelevel, and uncertainty in demand. The proposed algorithm has been tested on a real transmission network of theAzerbaijan regional electric company and compared with BPSO. The simulation results show that consideringthe losses even for transmission expansion planning of a network with low load growth is caused thatoperational costs decreases considerably and the network satisfies the requirement of delivering electric powermore reliable to load centers. In addition, regarding the convergence curves of the two methods, it can be seenthat precision of the proposed algorithm for the solution of the STNEP problem is more than BPSO.
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STNEP
network losses
voltage level
uncertainty in demand
IBPSO
[[1] AR Abdelaziz, “Genetic algorithmbased##power transmission expansion planning,” 7th##IEEE Int Conf Electron Circuits and Syst,##Lebanon, vol. 78, pp. 642645, 2000.##[2] VA Levi and MS Calovic, “Linearprogramming##based decomposition method for##optimal planning of transmission network##investments,” IEE Proc Gener Transm Distrib,##vol. 140, pp. 516522, 1993.##[3] J Choi, TR Mount, “Thomas Transmission##system expansion plans in view point of##deterministic, probabilistic and security##reliability criteria,” The 39th Hawaii Int Conf##Syst Sci, vol. 10, pp.110, 2006.##[4] IDJ Silva, MJ Rider, R Romero, CA Murari##“Transmission network expansion planning##considering uncertainness in demand,” IEEE##Power Eng Soc Gen Meet, vol. 2, pp. 1424##1429, 2005.##[5] S Binato, MVF Periera, S Granville, “A new##Benders decomposition approach to solve##power transmission network design Problems,” ##IEEE Trans Power Syst, vol. 16, pp. 235240,##[6] LL Garver, “Transmission network estimation##using linear programming,” IEEE Trans Power##Appar Syst, vol. PAS89, pp.16881696, 1970.##[7] IDJ Silva, MJ Rider, R Romero, CA Murari,##“Transmission network expansion planning##considering uncertainness in demand,” IEEE##Power Eng Soc Gen Meet, vol. 2, pp. 1424##1429, 2005.##[8] P Maghouli, SH Hosseini, MO Buygi, M##Shahidehpour, “A scenariobased multiobjective##model for multistage transmission##expansion planning,” IEEE Trans Power Syst,##vol. 26, pp. 470478, 2011.##[9] AML Silva, LS Rezende, LAF Manso, LC##Resende, “Reliability worth applied to##transmission expansion planning based on ant##colony system,” Int J Electr Power and Energy##Syst, vol. 32, pp. 107710841, 2010 .##[10] NH Sohtaoglu, “The effect of economic##parameters on power transmission planning,”##9th Mediterr Electrotech Conf, vol. 2, pp. 941##945, 1998.##[11] B Graeber, “Generation and transmission##expansion planning in southern Africa,” 1999##IEEE Africon, vol. 14, pp. 983988, 1999.##[12] MS Kandil, SM ElDebeiky, NE Hasanien,##“Rulebased system for determining unit##locations of a developed generation expansion##plan for transmission planning,” IEE Proc##Gener Transm Distrib, vol. 147, pp. 6268,##[13] RS Chanda, PK Bhattacharjee, “A reliability##approach to transmission expansion planning##using minimal cut theory,” Electr Power Syst##Res, vol. 33, pp. 111117, 1995.##[14] RS Chanda, PK Bhattacharjee, “A reliability##approach to transmission expansion planning##using fuzzy faulttree model,” Electr Power##Syst Res, vol. 45, pp. 101108, 1998.##[15] S Granville, MVF Pereira, GB Dantzig, B Avi##Itzhak, M Avriel, A Monticelli, LMVG Pinto,##“Mathematical decomposition techniques for##power system expansion planninganalysis of##the linearized power flow model using the##Benders decomposition technique,” EPRI,##Technical Report, RP, pp. 24736, 1988.##[16] R Romero, A Monticelli, “A hierarchical##decomposition approach for transmission##network expansion planning,” IEEE Trans##Power Syst, vol. 9, pp. 373380, 1994.##[17] S Binato, GC de Oliveira, Araujo JL, “A##greedy randomized adaptive search procedure##for transmission expansion planning,” IEEE##Trans Power Syst, vol. 16, pp. 247253, 2001.##[18] STY Lee, KL Hocks, H Hnyilicza,##“Transmission expansion by branch and bound##integer programming with optimal cost ##capacity curves,” IEEE Trans Power Appar##Syst, vol. PAS93, pp. 13901400, 1974.##[19] MVF Periera, LMVG Pinto, “Application of##sensitivity analysis of load supplying capability##to interactive transmission expansion##planning,” IEEE Trans Power Appar Syst, vol.##PAS104, pp. 381 389, 1985.##[20] R Romero, RA Gallego, A Monticelli,##“Transmission system expansion planning by##simulated annealing,” IEEE Trans Power Syst,##vol. 11, pp. 364369, 1996.##[21] RA Gallego, AB Alves, A Monticelli, R##Romero, “Parallel simulated annealing applied##to long term transmission network expansion##planning,” IEEE Trans Power Syst, vol. 12, pp.##181188, 1997.##[22] T AlSaba, I ElAmin, “The application of##artificial intelligent tools to the transmission##expansion problem,” Electr Power Syst Res,##vol. 62, pp. 117126, 2002.##[23] J Contreras, FF Wu, “A kerneloriented##algorithm for transmission expansion##planning,” IEEE Trans Power Syst, vol. 15, pp.##14341440, 2000.##[24] ASD Braga, JT Saraiva, “A multiyear dynamic##approach for transmission expansion planning##and longterm marginal costs computation,”##IEEE Trans Power Syst, vol. 20, pp. 1631##1639, 2005.##[25] EL Silva, HA Gil, JM Areiza, “Transmission##network expansion planning under an##improved genetic algorithm,” IEEE Trans##Power Syst, vol. 15, pp. 11681174, 2000.##[26] EL Silva, JMA Oritz, GC Oleveria, S Binato,##“Transmission network expansion planning##under a Tabu search approach,” IEEE Trans##Power Syst, vol. 16, pp. 6268, 2001.##[27] S Jalilzadeh, A Kazemi, H Shayeghi, M##Mahdavi, “Technical and economic evaluation##of voltage level in transmission network##expansion planning using GA,” Energy##Convers Manag, vol. 49, pp. 11191125, 2008.##[28] H Shayeghi, S Jalilzadeh, M Mahdavi, H##Haddadian, “Studying influence of two##effective parameters on network losses in##transmission expansion planning using##DCGA,” Energy Convers Manag, vol. 49, pp.##30173024, 2008.##[29] H Shayeghi, M Mahdavi, “Studying the effect##of losses coefficient on transmission expansion##planning using decimal codification based##GA,” Int J Tech Phys Probl Eng, vol. 1, pp. 58##[30] H Shayeghi, M Mahdavi, “Genetic algorithm##based studying of bundle lines effect on##network losses in transmission network##expansion planning,” J Electr Eng, vol. 60, pp.##237245, 2009.##[31] JH Zhao, J Foster, ZY Dong, KP Wong, ##“Flexible transmission network planning##considering distributed generation impacts,”##IEEE Trans Power Syst, vol. 26, pp. 1434##1443, 2011.##[32] M Mahdavi, H Shayeghi, A Kazemi, “DCGA##based evaluating role of bundle lines in TNEP##considering expansion of substations from##voltage level point of view,” Energy Convers##Manag, vol. 50, pp. 20672073, 2009.##[33] H Shayeghi, M Mahdavi, A Kazemi, HA##Shayanfar, “Studying effect of bundle lines on##TNEP considering network losses using##decimal codification genetic algorithm,”##Energy Convers Manag, vol. 51, pp. 2685##2691, 2010.##[34] H Shayeghi, M Mahdavi, HA Shayanfar, A##Bagheri, “Application of binary particle swarm##optimization for transmission expansion##planning considering lines loading,” In##proceedings of the 2009 Int Conf Artif Intell,##USA, pp. 653659, 2009.##[35] H Shayeghi, A Jalili, HA Shayanfar, “Multistage##fuzzy load frequency control using PSO,”##Energy Convers Manag, vol. 49, pp. 2570##2580, 2008.##[36] M Clerc, J Kennedy, “The particle swarmexplosion,##stability, and convergence in a##multidimensional complex space,” IEEE Trans##Evol Comput, vol. 6, pp. 5873, 2002.##[37] N Jin, YR Samii, “Advances in particle swarm##optimization for antenna designs: realnumber,##binary, singleobjective and multiobjective##implementations,” IEEE Trans Antennas##Propag, vol. 55, pp. 556567, 2007.##[38] AAA Esmin, GL Torres, ACZ de Souza, “A##hybrid particle swarm optimization applied to##loss power minimization,” IEEE Trans Power##Syst, vol. 20, pp. 859866, 2005.##]
Optimal Location and Parameter Settings of UPFC Device in Transmission System based on Imperialistic Competitive Algorithm
Optimal Location and Parameter Settings of UPFC Device
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In this paper, we present a new method to determine the optimal location and parameter settings of UnifiedPower Flow Controller (UPFC) for removing voltage violations and transmission lines overloading. UPFC isconsidered as the most powerful member of the FACTS devices, that it can control shunt and series power flow.This option gives to UPFC the power to control the voltage profile and transmission lines flow simultaneously.We used the Imperialistic Competitive Algorithm (ICA) to determine the optimal location and optimal parametersettings of UPFC to improve the performance of the power system specially removing voltage violations in thebuses and solving transmission lines overloading to increase loadability in the power networks. This procedureis proposed to be applied on IEEE 14 bus system to show the validity of the method.
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Imperialistic Competitive Algorithm (ICA)
Loadability
Optimal Location
Optimal settings Unified Power Flow Controller (UPFC)
Voltage profile
[[1] N. Hingorani, "Power Electronics in electrical##utilities: role of power electronics in future##power systems", Proceedings of the IEEE, Vol.##76 No. 4 Apr. 1988, pp. 481482.##[2] L. Gyugyi, "A Unified Power Flow Control concept##for Flexible AC transmission system", IEEE Proc.,##PartC, Vol. 139, No.4 Jul. 92, pp. 323331.##[3] C. R. PuerleEsquivel and E. Acha, "A newtontype##algorithm for the control of power flow in electrical##power networks", IEEE Trans. Power System, Vol.##12, no. 4, Nov. 1997, pp. 14741480.##[4] S. N. Singh, I. Erlich, "Locating unified power flow##controller for enhancing power system loadability",##International Conference on Future Power System,##1618 Nov. 2005.##[5] Fang, W.L. and Ngen, H.W. "Control setting of##unified power flow controllers through the robust##load flow calculation", IEE Proc. Gener. Transm.##Distribution, 1999, 146, pp. 15.##[6] Ramirez, J.M. Davalos, R.J. and Valenzuela##Coronado, "FACTS based stabilizers coordination", ##Electr. Power Energy Sys. 2002, pp. 233243.##[7] L. Ippolito and P. Siano, " Selection of optimal##number and location of thyristorcontrolled phase##shifter using Genetic based algorithm", IEE Proc.##Gener. Transm. Distrib. Vol. 151, No. 5, Sept. 2004.##[8] D .Radu, etalr, "A multiobjective Genetic algorithm##approach to optimal allocation of FACTS devices##for power system security", IEEE power##engineering society general meeting, 1822, Jun##[9] F. DGaliana, K. Almeida, M.Toussiant, J. Griffin, D.##Atanackovice, "Assessment and control of FACTS##devices on power performance", System IEEE##Trans. Power Systems, Vol. 11 Nov. 96.##[10] S. H. Kim, J. U. lim, S. Moon, "Enhancement of##power system security level through the power flow##control of UPFC", proceeding of the IEEE/PES##summer meeting, pp. 3043, 2000.##[11] Ahmed M. Othman, Alexander Guan, Matti##Lehtonen, Mahdi ElArini, "Real world optimal##UPFC placement and it's impact on reliability",##Recent advances in energy & environment journal,##2010, pp. 9096.##[12] M. Z. ElSadek, M. AboZahhad, A. Ahmed, H. E.##Zidan, "Effect of load representation on UPFC##performance and optimal placement", The 11th##international meddle east power systems conference,##MEPCON, 2006, pp. 213220.##[13] Ahmed M. Othamn, Matti Lehtonen and Mahdi M.##ElArini, "Optimal UPFC based Genetics Algorithm##to improve the SteadyState Performance of##HELENSAHKOVVERKKO OY 110kv Network at##Increasing the Loading Pattern", Environment and##Electrical Engineering (EEEIC), , 2010.##[14] R. L. Haupt and S. E. Haupt, "Practical Genetic##Algorithms", Second Edition, New Jersey, John##Wiley & Sons, 2004, pp. 162166.##[15] M. Melanie,"An introduction to Genetic##Algorithms", Massachusetts, MIT Press, 1999.##]
NeuroOptimizer: A New Artificial Intelligent Optimization Tool and Its Application for Robot Optimal Controller Design
NeuroOptimizer: A New Artificial Intelligent Optimization Tool
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The main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. Recently there have been attempts for using artificial neural networks (ANNs) in optimizationproblems and some types of ANNs such as Hopfield network and Boltzmann machine have been applied incombinatorial optimization problems. However, ANNs cannot optimize continuous functions and discreteproblems should be mapped into the neural networks architecture. To overcome these shortages, we introduce anew procedure for stochastic optimization by a recurrent artificial neural network. The introduced neurooptimizer(NO) starts with an initial solution and adjusts its weights by a new heuristic and unsupervised rule tocompute the best solution. Therefore, in each iteration, NO generates a new solution to reach the optimal ornear optimal solutions. For comparison and detailed description, the introduced NO is compared to geneticalgorithm and particle swarm optimization methods. Then, the proposed method is used to design the optimalcontroller parameters for a five bar linkage manipulator robot. The important characteristics of NO are:convergence to optimal or near optimal solutions, escaping from local minima, less function evaluation, highconvergence rate and easy to implement.
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numerical optimization
Neural networks
Objective function
weight updating
five bar linkage manipulator robot
[[1] J. Hopfield, and D. Tank, Neural computation##of decisions in optimization problems,##Biological Cybernetics, Vol. 52, 1985, pp. 141##[2] G.E. Hinton, and T.J. Sejnowsky, Optimal##perceptual inference, Proceedings of the IEEE##Conference on Computer Vision and Pattern##Recognition, Washigton, 1983, pp. 448453.##[3] D. Amit, H. Gutfreund, and H. Sompolinsky,##SpinGlass models of neural networks,##Physical Review Letters A 32, 1985, pp. 1007##[4] Y. Akiyama, A. Yamashita, M. Kajiura, and H.##Aiso, Combinatorial optimization with##gaussian machines, Proceedings IEEE##International Joint Conference on Neural##Networks 1, 1989, pp. 533–540.##[5] T. Kohonen, SelfOrganized formation of##topologically correct feature maps, Biological##Cybernetics 43, 1982, pp. 59–69.##[6] A.H. Gee, and R. W. Prager, Limitations of##neural networks for solving traveling salesman##problems, IEEE Trans. Neural Networks, vol.##6, 1995, pp. 280–282.##[7] M. Goldstein, SelfOrganizing feature maps for##the multiple traveling salesman problem##(MTSP), Proceedings IEEE International##Conference on Neural Networks, Paris, 1990,##pp. 258–261.##[8] Y. P. S. Foo, and Y. Takefuji, Stochastic neural##networks for jobshop scheduling: parts 1 and##2, Proceedings of the IEEE International##Conference on Neural Networks 2, 1988, pp.##275–290.##[9] Y.P. S. Foo, and Y. Takefuji, Integer Linear##programming neural networks for job shop##scheduling, Proceedings of the IEEE##International Conference on Neural Networks##2, 1988, pp. 341–348.##[10] J.S. Lai, S.Y. Kuo, and I.Y. Chen, Neural##networks for optimization problems in graph##theory, Proceedings IEEE International##Symposium on Circuits and Systems 6, 1994,##pp. 269–272.##[11] D.E. Van Den Bout, and T.K. Miller, Graph##partitioning using annealed neural networks,##IEEE Transactions on Neural Networks 1,##1990, pp. 192–203.##[12] S. Vaithyanathan, H. Ogmen, and J. 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Optimization of Conventional Stabilizers Parameter of Two Machine Power System Linked by SSSC Using CHSA Technique
Optimization of Conventional Stabilizers Parameter
2
2
This paper presents a method for damping of low frequency oscillations (LFO) in a power system. The powersystem contains static synchronous series compensators (SSSC) which using a chaotic harmony searchalgorithm (CHSA), optimizes the leadlag damping stabilizer. In fact, the main target of this paper isoptimization of selected gains with the time domainbased objective function, which is solved by chaoticharmony search algorithm. The performance of the proposed twomachine power system equipped with SSSC isevaluated under various disturbances and operating conditions and compared to power system stabilizer (PSS).The effectiveness of the proposed SSSC controller to damp out of oscillations, over a wide range of operatingconditions and variation of system parameters is shown in simulation results and analysis.
1

70
77
power system stability
SSSC
Chaotic Harmony Search
Conventional Stabilizer
Two Machine System
LFO
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