Network Reconfiguration And Distributed Generation Sizing In Radial Distribution System Using Improved Evolutionary Particle Swarm Optimization
The present of Distributed Generation (DG) with suitable Distribution Network Reconfiguration (DNR) in the distribution system may lead to several advantages such as voltage support, power losses reduction, deferment of new transmission line and distribution structure and improved system stability....
Saved in:
Main Author: | |
---|---|
Format: | Thesis |
Language: | English English |
Published: |
2017
|
Subjects: | |
Online Access: | http://eprints.utem.edu.my/id/eprint/20718/1/Network%20Reconfiguration%20And%20Distributed%20Generation%20Sizing%20In%20Radial%20Distribution%20System%20Using%20Improved%20Evolutionary%20Particle%20Swarm%20Optimization%20-%20Nur%20Faziera%20Napis%20-%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/20718/2/Network%20Reconfiguration%20And%20Distributed%20Generation%20Sizing%20In%20Radial%20Distribution%20System%20Using%20Improved%20Evolutionary%20Particle%20Swarm%20Optimization.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my-utem-ep.20718 |
---|---|
record_format |
uketd_dc |
institution |
Universiti Teknikal Malaysia Melaka |
collection |
UTeM Repository |
language |
English English |
topic |
Q Science (General) QC Physics |
spellingShingle |
Q Science (General) QC Physics Napis, Nur Faziera Network Reconfiguration And Distributed Generation Sizing In Radial Distribution System Using Improved Evolutionary Particle Swarm Optimization |
description |
The present of Distributed Generation (DG) with suitable Distribution Network Reconfiguration (DNR) in the distribution system may lead to several advantages such as voltage support, power losses reduction, deferment of new transmission line and distribution structure and improved system stability. However, installation of the DG unit at non-optimal sizes with non-optimal DNR can incur higher power losses, power quality problem, voltage instability and amplification of operational cost. To overcome the power losses and voltage stability problems, an appropriate planning of DG units and DNR are considered. Thus, the first objective of this research is to develop an optimization technique named Improved Evolutionary Particle Swarm Optimization (IEPSO). The objective function is formulated to minimize the total power losses and to improve the voltage stability index. The load flow algorithm and voltage stability index calculation are integrated in the MATLAB environment to solve the optimization problem. Recently, the power system networks are being operated closer to the stability boundaries due to economic and environmental constraints. The heavier loading in the highly developed networks leads to voltage stability problems. However, the voltage stability problem of the distribution system can be improved if the loads are rescheduled efficiently with optimal DNR and DG sizing. Thus, the second objective of this research is to analyze the voltage stability index with three load demand levels; light load, nominal load, and heavy load with optimal DNR and DG sizing. The third objective of this research is to validate the performance of the proposed technique with other optimization techniques, namely Particle Swarm Optimization (PSO) and Iteration Particle Swarm Optimization (IPSO). Four case studies on 33-bus and 69-bus distribution system have been conducted to validate the effectiveness of the IEPSO. The optimization results show that, the best achievement of IEPSO technique for power losses reduction is up to 79.26%, and 58.41% improvement in the voltage stability index for three load conditions, light load, nominal load and heavy load. The proposed optimal DG sizing and DNR algorithm will provide a solution for independent power producer and power utility in terms of technical issues which beneficial for future electricity especially in integrating DG for the distribution network. |
format |
Thesis |
qualification_name |
Master of Philosophy (M.Phil.) |
qualification_level |
Master's degree |
author |
Napis, Nur Faziera |
author_facet |
Napis, Nur Faziera |
author_sort |
Napis, Nur Faziera |
title |
Network Reconfiguration And Distributed Generation Sizing In Radial Distribution System Using Improved Evolutionary Particle Swarm Optimization |
title_short |
Network Reconfiguration And Distributed Generation Sizing In Radial Distribution System Using Improved Evolutionary Particle Swarm Optimization |
title_full |
Network Reconfiguration And Distributed Generation Sizing In Radial Distribution System Using Improved Evolutionary Particle Swarm Optimization |
title_fullStr |
Network Reconfiguration And Distributed Generation Sizing In Radial Distribution System Using Improved Evolutionary Particle Swarm Optimization |
title_full_unstemmed |
Network Reconfiguration And Distributed Generation Sizing In Radial Distribution System Using Improved Evolutionary Particle Swarm Optimization |
title_sort |
network reconfiguration and distributed generation sizing in radial distribution system using improved evolutionary particle swarm optimization |
granting_institution |
Universiti Teknikal Malaysia Melaka |
granting_department |
Faculty of Electrical Engineering |
publishDate |
2017 |
url |
http://eprints.utem.edu.my/id/eprint/20718/1/Network%20Reconfiguration%20And%20Distributed%20Generation%20Sizing%20In%20Radial%20Distribution%20System%20Using%20Improved%20Evolutionary%20Particle%20Swarm%20Optimization%20-%20Nur%20Faziera%20Napis%20-%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/20718/2/Network%20Reconfiguration%20And%20Distributed%20Generation%20Sizing%20In%20Radial%20Distribution%20System%20Using%20Improved%20Evolutionary%20Particle%20Swarm%20Optimization.pdf |
_version_ |
1747833993168945152 |
spelling |
my-utem-ep.207182022-03-15T08:17:12Z Network Reconfiguration And Distributed Generation Sizing In Radial Distribution System Using Improved Evolutionary Particle Swarm Optimization 2017 Napis, Nur Faziera Q Science (General) QC Physics The present of Distributed Generation (DG) with suitable Distribution Network Reconfiguration (DNR) in the distribution system may lead to several advantages such as voltage support, power losses reduction, deferment of new transmission line and distribution structure and improved system stability. However, installation of the DG unit at non-optimal sizes with non-optimal DNR can incur higher power losses, power quality problem, voltage instability and amplification of operational cost. To overcome the power losses and voltage stability problems, an appropriate planning of DG units and DNR are considered. Thus, the first objective of this research is to develop an optimization technique named Improved Evolutionary Particle Swarm Optimization (IEPSO). The objective function is formulated to minimize the total power losses and to improve the voltage stability index. The load flow algorithm and voltage stability index calculation are integrated in the MATLAB environment to solve the optimization problem. Recently, the power system networks are being operated closer to the stability boundaries due to economic and environmental constraints. The heavier loading in the highly developed networks leads to voltage stability problems. However, the voltage stability problem of the distribution system can be improved if the loads are rescheduled efficiently with optimal DNR and DG sizing. Thus, the second objective of this research is to analyze the voltage stability index with three load demand levels; light load, nominal load, and heavy load with optimal DNR and DG sizing. The third objective of this research is to validate the performance of the proposed technique with other optimization techniques, namely Particle Swarm Optimization (PSO) and Iteration Particle Swarm Optimization (IPSO). Four case studies on 33-bus and 69-bus distribution system have been conducted to validate the effectiveness of the IEPSO. The optimization results show that, the best achievement of IEPSO technique for power losses reduction is up to 79.26%, and 58.41% improvement in the voltage stability index for three load conditions, light load, nominal load and heavy load. The proposed optimal DG sizing and DNR algorithm will provide a solution for independent power producer and power utility in terms of technical issues which beneficial for future electricity especially in integrating DG for the distribution network. 2017 Thesis http://eprints.utem.edu.my/id/eprint/20718/ http://eprints.utem.edu.my/id/eprint/20718/1/Network%20Reconfiguration%20And%20Distributed%20Generation%20Sizing%20In%20Radial%20Distribution%20System%20Using%20Improved%20Evolutionary%20Particle%20Swarm%20Optimization%20-%20Nur%20Faziera%20Napis%20-%2024%20Pages.pdf text en public http://eprints.utem.edu.my/id/eprint/20718/2/Network%20Reconfiguration%20And%20Distributed%20Generation%20Sizing%20In%20Radial%20Distribution%20System%20Using%20Improved%20Evolutionary%20Particle%20Swarm%20Optimization.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=106154 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Electrical Engineering 1. Abri, R. S. Al, El-saadany, E. F., Member, S., and Atwa, Y. M. 2012. Optimal Placement and Sizing Method to Improve the Voltage Stability Margin in a Distribution System Using Distributed Generation. IEEE Transactions on Power Systems, 28(1), pp. 1–9. 2. Abu-Mouti, F. S., and El-Hawary, M. E. 2011. Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm. Power Delivery, IEEE Transactions on, 26(4), pp. 2090–2101. 3. Ackermann, T. 2001. Distributed Generation : A Definition. Electric Power System Research 57, pp. 195–204. 4. Agency, IE. 2012. Electricity Information. Available from: http://www.iea.org/media/training/presentations/statisticsmarch/ElectricityInformation.pdf. 5. Alamos, L., Division, T., and Alamos, L. 1986. The Immune System, Adaptation, and Machine Learning. Physica D: Nonlinear Phenomena, 22(1-3), pp. 187-204. 6. AlHajri, M. F., & El-Hawary, M. E. 2010. Exploiting the Radial Distribution Structure in Developing a Fast and Flexible Radial Power Flow for Unbalanced Three-Phase Networks. IEEE Transactions on Power Delivery, 25(1), pp. 378–389. 7. Alonso, F. R., & Oliveira, D. Q. 2015. Artificial Immune Systems Optimization Approach for Multiobjective Distribution System Reconfiguration, IEEE Transactions on Power System, 30(2), pp. 840–847. 8. Alonso, M., & Amarís, H. 2009. Voltage Stability in Distribution Networks with DG. IEEE Bucharest PowerTech: Innovative Ideas Toward the Electrical Grid of the Future, pp. 1–6. 9. Aman, M. M., Jasmon, G. B., Bakar, A. H. A., & Mokhlis, H. 2013. A New Approach for Optimum DG Placement and Sizing based on Voltage Stability Maximization and Minimization of Power Losses. Energy Conversion and Management, 70, pp. 202–210. 10. Ameli, A., Member, S., Bahrami, S., Member, S., & Khazaeli, F. 2014. A Multiobjective Particle Swarm Optimization for Sizing and Placement of DGs from DG Owner ’ s and Distribution Company ’ s Viewpoints. IEEE Transactions on Power Delivery, 29(4), pp. 1831–1840. 11. Arun, M., & Aravindhababu, P. 2009. A New Reconfiguration Scheme for Voltage Stability Enhancement of Radial Distribution Systems. Energy Conversion and Management, 50(9), pp. 2148–2151. 12. Balamurugan, K., Srinivasan, D., & Reindl, T. 2012. Impact of Distributed Generation on Power Distribution Systems. Energy Procedia, 25, pp. 93–100. 13. Barker, P. P., & Mello, R. W. De. 2000. Determining the Impact of Distributed Generation on Power System: Part 1-Radial Distribution Systems. Power Engineering Society Summer Meeting, 2000 IEEE, 00(c), pp. 1645–1656. 14. Beheshti, Z., Mariyam, S., & Shamsuddin, H. 2013. A Review of Population-based Meta-Heuristic Algorithm, Int. J. Advance Soft Comptu. Appl., 5(1), pp. 1–35. 15. Borges, C. L. T., & Falcão, D. M. 2003. Impact of Distributed Generation Allocation and Sizing on Reliability, Losses and Voltage Profile. IEEE Bologna PowerTech - Conference Proceedings, 2, pp. 396–400. 16. Boussaïd, I., Lepagnot, J., & Siarry, P. 2013. A Survey on Optimization Metaheuristics, Infor mation Sciences, 237, pp. 82–117. 17. C. L. Master. 2002. Voltage Rise: The Big Issue when Connecting Embedded Generation to Long 11kV Overhead Lines. Power Engineering Journal, 16(1), pp. 5–12. 18. California Public Utilities Commission, & Black & Veatch. 2013. Biennial Report on Impacts of Distributed Generation. 19. Chakravorty, M., & Das, D. 2001. Voltage Stability Analysis of Radial Distribution Networks. International Journal of Electrical Power & Energy Systems, 23(2), pp. 129–135. 20. Chambers, A., Schnoor, B., & Hamilton, S. 2001. Distributed Generation - A Nontechnical Guide. PennWell. Available at: 21. https://app.knovel.com/web/toc.v/cid:kpDGANG009/viewerType:toc/root_slug:distributed-generation/url_slug:kt00C45Q22 22. Chaweewat, P., & Singh, J. G. 2016. Economic and Environmental Impact Assessment with Network Reconfiguration in Microgrid by using Artificial Bee Colony. International Conference on Cogeneration; Small Power Plants and District Energy, pp. 14–16. 23. Chen, H., Chen, J., Shi, D., & Duan, X. 2006. Power Flow Study and Voltage Stability Analysis for Distribution Systems with Distributed Generation. IEEE, pp. 1–8. 24. Cheng, S., Chen, M., & Fleming, P. J. 2015. Neurocomputing Improved Multi-objective Particle Swarm Optimization with Preference Strategy for Optimal DG Integration into the Distribution System. Neurocomputing, 148, pp. 23–29. 25. Chiradeja, P. 2005. Benefit of Distributed Generation: A Line Loss Reduction Analysis. IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific, pp. 1–5. 26. Chiradeja, P., Ramakumar, R., & Fellow, L. 2004. An Approach to Quantify the Technical Benefits of Distributed Generation, IEEE Transactions on Energy Conversion, 19(4), pp. 764–773. 27. Chuang, L. Y., Tsai, S. W., & Yang, C. H. 2011. Chaotic Catfish Particle Swarm Optimization for Solving Global Numerical Optimization Problems. Applied Mathematics and Computation, 217(16), pp. 6900–6916. 28. Chuong, T. T. 2008. Distributed Generation Impact on Voltage Stability in Distribution Networks. System, IEEE Transaction, 25(c), pp. 1–5. 29. Dahalan, W. M., & Mokhlis, H. 2012. Network Reconfiguration for Loss Reduction with Distributed Generations using PSO. IEEE International Conference on Power and Energy, pp. 823–828. 30. Das, S., Das, D., & Patra, A. 2016. Distribution Network Reconfiguration using Distributed Generation Unit considering Variations of Load. IEEE International Conference on Power Electronics, Intelligent Control and Energy System, pp. 10–14. 31. Delfanti, M., Falabretti, D., & Merlo, M. 2013. Dispersed Generation Impact on Distribution Network Losses. Electric Power Systems Research, 97, pp. 10–18. 32. Dondi, P., Bayoumi, D., Haederli, C., Julian, D., & Suter, M. 2002. Network Integration of Distributed Power Generation. Journal of Power Sources, 106(1-2), pp. 1–9. 33. Dong, C., Wang, G., Chen, Z., & Yu, Z. 2008. A Method of Self-Adaptive Inertia Weight for PSO. Proceedings - International Conference on Computer Science and Software Engineering, CSSE 2008, 1(60788402), pp. 1195–1198. 34. Dorigo, M., Maniezzo, V., & Colorni, A. 1996. Ant System : Optimization by a Colony of Cooperating Agents, IEEE Transactions on Systems, Man, and Cybernetics-Part B, 26(1), pp. 29-41. 35. Dugan, R. C., & Price, S. K. 2002. Issues for Distributed Generation in the US. IEEE Power Engineering Society Winter Meeting. Conference Proceeding. pp. 121-126. 36. El-Zonkol, A. M. 2010. Optimal Placement of Multi DG Units Including Different Load Models Using PSO. Smart Grid and Renewable Energy, 01, pp. 160–171. 37. Esmaili, M. 2013. Placement of Minimum Distributed Generation Units observing Power Losses and Voltage Stability with Network Constraints. IET Generation, Transmission & Distribution, pp. 813–821. 38. Esmaili, M., Firozjaee, E. C., & Shayanfar, H. A. 2014. Optimal Placement of Distributed Generations Considering Voltage Stability and Power Losses with observing Voltage-Related Constraints. Applied Energy, 113, pp. 1252–1260. 39. Essallah, S., Bouallegue, A., & Khedher, A. 2015. Optimal Placement of PV-Distributed Generation Units in Radial Distribution System based on Sensitivity Approaches. 16th International Conference on Sciences and Techniques of Automatic Control & Computer Engineering, pp. 513-520. 40. Fan, W. F. W., Cui, Z. C. Z., Chen, Y. C. Y., & Tan, Y. T. Y. 2010. Nonlinear Time-Varying Stability Analysis of Particle Swarm Optimization. Computational Aspects of Social Networks (CASoN), 2010 International Conference on, (0), pp. 2–5. 41. Ferdavani, A. K., Bin Mohd Zin, A. A., Bin Khairuddin, A., & Naeini, M. M. 2011. A review on Reconfiguration of Radial Electrical Distribution Network through Heuristic Methods. 4th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO. pp. 102-106. 42. Firas, M. F. F. 2016. Distribution System Reconfiguration for Power Loss Minimization and Voltage Profile Improvement Using Modified Particle Swarm Optimization. IEEE PES Asia-Pacific Power and Energy Conference, pp. 120–124. 43. Freitas, W., Morelato, A., Xu, W., & Sato, F. 2005. Impacts of AC Generators and DSTATCOM Devices on the Dynamic Performance of Distribution Systems. IEEE Transactions on Power Delivery, 20(2 II), pp. 1493–1501. 44. Freitas, W., Silva, L. C. P. Da, & Morelato, A. (2005). Systems With Induction Generators. IEEE Transactions on Power Systems, 20(3), pp. 1653–1654. 45. Freitas, W., Vieira, J. C. M., Suva, L. C. P. Da, Affonso, C. M., & Morelato, a. 2005. Long-Term Voltage Stability of Distribution Systems with Induction Generators. IEEE Power Engineering Society General Meeting, 2005, (1), pp. 3–6. 46. Geem, Z. W., Kim, J. H., & Logonathan, G. V. 2001. A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 78(2), pp. 60–68. 47. Ghatak, S. R. 2016. Optimal Allocation of DG Using Exponential PSO With Reduced Search Space. Second International Conference on Computational Intelligence & Communication Technology, pp. 489-494. 48. Gil, H. A., El Chehaly, M., Joos, G., & Cañizares, C. 2009. Bus-Based Indices for Assessing the Contribution of DG to the Voltage Security Margin of the Transmission Grid. IEEE Power and Energy Society General Meeting, PES ’09, pp. 1–7. 49. Gitizadeh, M., Vahed, A. A., & Aghaei, J. 2013. Multistage Distribution System Expansion Planning Considering Distributed Generation using Hybrid Evolutionary Algorithms. Applied Energy, 101, pp. 655–666. 50. Griffin, T., Tomsovic, K., Secrest, D., & Law, a. 2000. Placement of Dispersed Generation Systems for Reduced Losses. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, pp. 1–9. 51. Grigsby, L. L. 2001. The Electric Power Engineering Handbook. IEEE Press (Vol. 2). 52. Gubina, F., & Strmcnik, B. 1997. A Simple Approach to Voltage Stability Assessment in Radial Networks. IEEE Transactions on Power Systems, 12(3), pp. 1121–1128. 53. Hedayati, H., Nabaviniaki, S. A., & Akbarimajd, A. 2008. A Method for Placement of DG Units in Distribution Networks. IEEE Transactions on Power Delivery, 23(3), pp. 1620–1628. 54. Hien, N. C., Mithulananthan, N., & Bansal, R. C. 2013. Location and Sizing of Distributed Generation Units for Loadabilty Enhancement in Primary Feeder. IEEE Systems Journal, 7(4), pp. 797–806. 55. Hu, Y., Hua, N., & Wang, C. 2010. Research on Distribution Network Reconfiguration. International Conference on Computer, Mechatronics, Control and Electronic Engineering, pp. 176–180. 56. Huang, Y.-C. 2002. Enhanced Genetic Algorithm-based Fuzzy Multi-Objective Approach to Distribution Network Reconfiguration. Generation, Transmission and Distribution, IEEE Proceedings, pp. 615 – 620. 57. Hung, D. Q., Mithulananthan, N., & Bansal, R. C. 2013. Analytical Strategies for Renewable Distributed Generation Integration Considering Energy Loss Minimization. Applied Energy, 105, pp. 75-85. 58. IEEE Standards, 2004. IEEE Standard for Interconnecting Distributed Resources with Electric Power Systems. 59. Jackson, S., Peterson, P., & Hurley, D. 2013. Forecasting Distributed Generation Resources in New England : Distributed Generation must be Properly Accounted for in Regional System Planning. 60. Jamian, J. J., Abdullah, M. N., Mokhlis, H., Mustafa, M. W., & Bakar, A. H. A. 2014. Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis. Journal of Applied Mathematics, pp. 1-14. 61. Jamil, M. 2015. Ant Colony Optimization for Restoration of Distribution System, IEEE Trsnsactions, pp. 1–6. 62. Jasmon, G. B., & Lee, L. H. C. C. 1991. Distribution Network Reduction for Voltage Stability Analysis and Loadflow Calculations. International Journal of Electrical Power & Energy Systems, 13(1), pp. 9–13. 63. Jasmon, G. B., & Lee, L. H. C. C. 1993. New Contingency Ranking Technique Incorporating a Voltage Stability Criterion. IEEE Proceedings-C, 140(1), pp. 87–90. 64. Jie, J., Zeng, J., Han, C., & Wang, Q. 2008. Knowledge-based Cooperative Particle Swarm Optimization. Applied Mathematics and Computation, 205(2), pp. 861–873. 65. Juanuwattanakul, P., & Masoum, M. a. S. 2012. Increasing Distributed Generation Penetration in Multiphase Distribution Networks Considering Grid Losses, Maximum Loading Factor and Bus Voltage Limits. IET Generation, Transmission & Distribution, 6(12), pp. 1262–1271. 66. K. Dang, J. Yu, T. Dong, B. H. 2006. Benefit of Distribution Generation on Line Loss Reduction. IEEE Transactions, pp. 1–4. 67. Karaboga, D., & Akay, B. 2009. A Comparative Study of Artificial Bee Colony Algorithm. Applied Mathematics and Computation, 214(1), pp. 108–132. 68. Kashem, M. a., Ganapathy, V., & Jasmon, G. B. 2000. Network Reconfiguration for Enhancement of Voltage Stability in Distribution Networks. IEE Proceedings - Generation, Transmission and Distribution, 147(3), pp. 171-175. 69. Kennedy, J., & Eberhart, R. 1995. Particle Swarm Optimization. IEEE Transactions, pp. 1942–1948. 70. Kerleta, V. D., & Popovi, D. S. 2014. Hybrid Simulated Annealing and Mixed Integer Linear Programming Algorithm for Optimal Planning of Radial Distribution Networks with Distributed Generation, 108, pp. 211–222. 71. Kotamarty, S., Khushalani, S., & Schulz, N. 2008. Impact of Distributed Generation on Distribution Contingency Analysis. Electric Power Systems Research, 78(9), pp. 1537–1545. 72. Kundur, P., & Balu, N. J. 1994. Power system stability and control, Power System Dynamics and Stability, 4. 73. Lasseter, R. H., & Paigi, P. 2004. Microgrid : A Conceptual Solution. 35th Annual IEEE Power Electronics Specialist Conference, pp. 4285–4290. 74. Lee, K. S., & Geem, Z. W. 2005. A New Meta-Heuristic Algorithm for Continuous Engineering Optimization: Harmony Search Theory and Practice. Computer Methods in Applied Mechanics and Engineering, 194(36-38), pp. 3902–3933. 75. Lee, S. H., & Park, J. W. 2009. Selection of Optimal Location and Size of Multiple Distributed Generations by using Kalman Filter Algorithm. IEEE Transactions on Power Systems, 24(3), pp. 1393–1400. 76. Lee, T. Y., & Chen, C. L. 2007. Unit Commitment with Probabilistic Reserve: An IPSO Approach. Energy Conversion and Management, 48(2), pp. 486–493. 77. Li, Q., Ding, W., Zhang, J., & Liu, A. 2009. A New Reconfiguration Approach for Distribution System with Distributed Generation. International Conference on Energy and Environment Technology, 2, pp. 23–26. 78. Li, Z., Han, Y., Zhang, Y., Bao, Y., Guo, C., Member, S., & Zhang, J. 2016. An Adaptive Harmony Search Algorithm based on Positive Feedback for Network Reconfiguration. IEEE Transactions on Power System. pp. 1–5. 79. Liao, W., Wang, J., & Wang, J. 2011. Nonlinear Inertia Weight Variation for Dynamic Adaptation in Particle Swarm Optimization. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6728 LNCS(PART 1), pp. 80–85. 80. Lin, W. 2004. The Identification Research of Nonlinear System Based on PSO with Fuzzy Adaptive Inertia Weight. Proceeding of the 5th World Congress on Intelligent Control and Automation, pp. 267–271. 81. Lin, W.-M., Gow, H.-J., & Tsai, M.-T. 2011. Hybridizing Particle Swarm Optimization with Signal-to-Noise Ratio for numerical optimization. Expert Systems with Applications, 38(11), pp. 14086–14093. 82. Liu, H., Abraham, A., & Clerc, M. 2007. An Hybrid Fuzzy Variable Neighborhood Particle Swarm Optimization Algorithm for Solving Quadratic Assignment Problems. Journal of Universal Computer Science, 13(9), pp. 1309–1331. 83. Lof, P. a, Smed, T., Andersson, G., & Hill, D. J. 1992. Fast Calculation of a Voltage Stability Index. IEEE Transactions on Power Systems, 7(1), pp. 54–64. 84. Londero, R. R., Affonso, C. M., & Nunes, M. V. A. 2009. Impact of Distributed Generation in Steady State, Voltage and Transient Stability . IEEE Bucharest PowerTech, pp. 1–6. 85. Marwali, M. N., Jung, J., Member, S., & Keyhani, A. 2004. Control of Distributed Generation Systems — Part II : Load Sharing Control. IEEE Transaction on Power Electronics, 19(6), pp. 1551–1561. 86. Menhas, M. I., Fei, M., Wang, L., & Qian, L. 2012. Real/Binary Co-Operative and Co-Evolving Swarms based Multivariable PID Controller Design of Ball Mill Pulverizing System. Energy Conversion and Management, 54(1), pp. 67–80. 87. Michael, C., Vincent, M., & Rogelio, F. 2016. Effect of Widespread Variation of Distributed Generation (DG) on the Line Performance of a Radial Distribution Network. 6th IEEE International Conference on Control System, Computing and Engineering, pp. 354-359. 88. Mo, L., & Zheng, H. 2009. Improved PSO Algorithm with Adaptive Inertia Weight and Mutation. WRI World Congress on Computer Science and Information Engineering, 4, pp. 622–625. 89. Mohd Ali, N. Z., Musirin, I., Khalidin, Z., & Ahmad, M. R. 2011. Index-Based Placement and Distributed Generation Sizing based on Heuristic Search. 5th International Power Engineering and Optimization Conference, PEOCO 2011 - Program and Abstracts, pp. 364–368. 90. Moradi, M. H., Reza Tousi, S. M., & Abedini, M. (2014). Multi-Objective PFDE Algorithm for Solving the Optimal Siting and Sizing Problem of Multiple DG Sources. International Journal of Electrical Power & Energy Systems, 56, pp. 117–126. 91. Morkos, S., & Kamal, H. 2012. Optimal Tuning of PID Controller using Adaptive Hybrid Particle Swarm Optimization Algorithm, International Journal of Computers, Communications & Control, VII(1), pp. 101–114. 92. Musa, H. 2013. Enhanced PSO Based Multi-Objective Distributed Generation Placement and Sizing for Power Loss Reduction and Voltage Stability Index Improvement, IEEE Transaction on Power System, pp. 1–6. 93. Nickabadi, A., Ebadzadeh, M. M., & Safabakhsh, R. 2011. A Novel Particle Swarm Optimization Algorithm with Adaptive Inertia Weight. Applied Soft Computing Journal, 11(4), pp. 3658–3670. 94. Paliwal, P., Patidar, N. P., & Nema, R. K. (2012). A Comprehensive Survey of Optimization Techniques used for Distributed Generator Siting and Sizing. Proceedings of IEEE, pp. 1-7. 95. Rao, R. S., Ravindra, K., Satish, K., & Narasimham, S. V. L. 2013. Power Loss Minimization in Distribution System Using Network Reconfiguration in the Presence of Distributed Generation, IEEE Transactions on Power Systems, 28(1), pp. 317–325. 96. Ratnaweera, A., Halgamuge, S. K., & Watson, H. C. 2004. Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients. IEEE Transactions on Evolutionary Computation, 8(3), pp. 240–255. 97. Reddy, A. V. S., & Redd, M. D. 2016. Optimization of Network Reconfiguration by using Particle Swarm Optimization. 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, pp. 1–6. 98. Reza, M., Schavemaker, P. H., Slootweg, J. G., Kling, W. L., & Sluis, L. Van Der. 2004. Impacts of Distributed Generation Penetration Levels on Power Systems Transient Stability. IEEE Power Engineering Society General Meeting, pp. 1–6. 99. Shamshiri, M., Gan, C. K., Jusoff, K., Hasan, I. J., Ruddin, M., Yusoff, M., Jaya, T. 2013. Using Particle Swarm Optimization Algorithm in the Distribution System Planning Perdana School of Science , Technology & Innovation Policy ( UTM Perdana School ), 6th Floor , Foundation of Technical Education , Al-mansour , Baghdad , Iraq Centre for Langu, 7(3), pp. 85–92. 100. Shin, J.-R., Kim, B.-S., Park, J.-B., & Lee, K. Y. 2007. A New Optimal Routing Algorithm for Loss Minimization and Voltage Stability Improvement in Radial Power Systems. IEEE Transactions on Power Systems, 22(2), pp. 648–657. 101. Slootweg, J. G., & Kling, W. L. 2002. Impacts of Distributed Generation on Power System Transient Stability. IEEE Power Engineering Society Summer Meeting, 2(C), pp. 862–867. 102. Song, Y. H., Wang, G. S., Johns, A. T., & Wang, P. Y. 1997. Distribution Network Reconfiguration for Loss Reduction using Fuzzy Controlled Evolutionary Programming. IEEE Trans. Gener., Trans., Distri., 144(4), pp. 345–350. 103. Thakur, T. 2006. Study and Characterization of Power Distribution Network Reconfiguration, IEEE Transaction on Power System, pp. 1–6. 104. Voropai, N. I., & Bat-Undraal, B. 2012. Multicriteria Reconfiguration of Distribution Network with Distributed Generation. Journal of Electrical and Computer Engineering, pp. 1-8. 105. Walling, R. A., Saint, R., Dugan, R. C., Burke, J., & Kojovic, L. A. (2008). Summary of Distributed Resources Impact on Power Delivery Systems. IEEE Transactions on Power Delivery, 23(3), pp. 1636–1644. 106. Wang, C., & Nehrir, M. H. 2004. Analytical Approaches for Optimal Placement of Distributed Generation Sources in Power Systems. IEEE Transactions on Power Systems, 19(4), pp. 2068–2076. 107. Willis, H. L., Tram, H., Engep, M. V, & Finley, L. 1996. Selecting an Applying Distribution Optimize Methods. IEEE Computer Applications in Power, 9(1), pp. 12–17. 108. Wolpert, D. H., & Macready, W. G. 1997. No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation, 1(1), pp. 67–82. 109. Wu, L., & Hao, X. 2013. A Modified Honey Bee Mating Optimization Algorithm for Multi-Objective Voltage Control of Distributed Hybrid Wind and PV Systems. Journal of Theoretical and Applied Information Technology, 47(3), pp. 1223–1230. 110. Wu, Y. K., Lee, C. Y., Liu, L. C., & Tsai, S. H. 2010. Study of Reconfiguration for the Distribution System with Distributed Generators. IEEE Transactions on Power Delivery, 25(3), pp. 1678–1685. 111. Wu, Z., & Zhou, J. 2007. A Self-Adaptive Particle Swarm Optimization Algorithm with Individual Coefficients Adjustment. Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS, pp. 133–136. 112. Xi, M., Sun, J., & Xu, W. 2008. An Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Weighted Mean Best Position. Applied Mathematics and Computation, 205(2), pp. 751–759. 113. Yang, X. S. 2009. Firefly Algorithms for Multimodal Optimization. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5792 LNCS, pp. 169–178. 114. Zeineldin, H. H., El-saadany, E. F., & Salama, M. M. A. 2006. Impact of DG Interface Control on Islanding Detection and Nondetection Zones, IEEE Transactions on Power Delivery, 21(3), pp. 1515–1523. 115. Zeineldin, H. H., El-Saadany, E. F., & Salama, M. M. a. 2006. Distributed Generation Micro-Grid Operation: Control and Protection. Power Systems Conference: Advanced Metering, Protection, Control, Communication, and Distributed Resources, pp. 105–111. 116. Zhang, S., Cheng, H., Li, K., Bazargan, M., & Yao, L. 2014. Optimal Siting and Sizing of Intermittent Distributed Generators in Distribution System, 26(4), pp. 628–635. 117. Zheng, Y., Ma, L., Zhang, L., & Qian, J. 2003. Empirical Study of Particle Swarm Optimization with an Increasing Inertia Weight. The 2003 Congress on Evolutionary Computation, 2003, pp. 221–226. |