Fuel cost minimisation and line loadability enhancement using the PMBCA evolutionary technique

References

• Abido, M. A. 2002a. “Optimal Power Flow Using Particle Swarm Optimization.” Electrical Power and Energy Systems 24 (7): 563–571. doi: 10.1016/S0142-0615(01)00067-9
   Web of Science ®Google Scholar
• Abido, M. A. 2002b. “Optimal Power Flow Using Tabu Search Algorithm.” Electric Power Components and Systems 30 (5): 469–483. doi: 10.1080/15325000252888425
   Web of Science ®Google Scholar
• Arrif, Toufik, Adel Benchabane, Mawloud Germoui, Badreddine Bezza, and Abdelfettah Belaid. 2018. “Optimization of Heliostat Field Layout for Solar Power Tower Systems Using Iterative Artificial Bee Colony Algorithm: A Review and Case Study.” International Journal of Ambient Energy, doi:10.1080/01430750.2018.1525581.
   Google Scholar
• Asija, D., P. V. Astick, and P. Choudekar. 2018. “Minimizing Fuel Cost of Generators Using GA-OPF.” In Paper presented at the First International Conference on Smart System, Innovations and Computing. Smart Innovation, Systems and Technologies, edited by A. Somani, S. Srivastava, A. Mundra, and S. Rawat. Singapore: Springer, 79.
   Google Scholar
• Basu, Mousumi. 2016. “Group Search Optimization for Solution of Different Optimal Power Flow Problems.” Electric Power Components and Systems 44 (6): 606–615. doi:10.1080/15325008.2015.1122109.
   Web of Science ®Google Scholar
• Bentouati, Bachir, Saliha Chettih, Lakhdar Chaib, and Victor Sreeram. 2017. “Interior Search Algorithm for Optimal Power Flow with non-Smooth Cost Functions.” Cogent Engineering 4 (1), doi:10.1080/23311916.2017.1292598.
   Web of Science ®Google Scholar
• Bhambu, Pawan, Sangeeta Sharma, and Sandeep Kumar. 2018. “Modified Gbest Artificial Bee Colony Algorithm.” Soft Computing: Theories and Applications 583: 665–677.
   Google Scholar
• Deveraj, D., and B. Yegnanarayana. 2005. “Genetic Algorithm Based Optimal Power Flow for Security Enhancement.” IEE Proceedings - Generation, Transmission and Distribution 152 (6): 899–905. doi: 10.1049/ip-gtd:20045234
   Google Scholar
• Dorigo, M., and L. M. Gambardella. 1997. “Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. Evolutionary Computation.” IEEE Transactions on Evolutionary Computation 1 (1): 53–66. doi: 10.1109/4235.585892
   Google Scholar
• Duman, S., U. Guvenc, Y. Sonmez, and N. Yorukere. 2012. “Optimal Power Flow Using Gravitational Search Algorithm.” Energy Conversion and Management 59: 86–95. doi: 10.1016/j.enconman.2012.02.024
   Web of Science ®Google Scholar
• Gao, Wei-feng, San-yang Liu, and Ling-ling Huang. 2014. “Enhancing Artificial bee Colony Algorithm Using More Information-Based Search Equations.” Information Sciences 270: 112–133. doi: 10.1016/j.ins.2014.02.104
   Web of Science ®Google Scholar
• Karaboga, D., and B. Basturk. 2008. “On the Performance of Artificial bee Colony (ABC) Algorithm.” Applied Soft Computing 8: 687–697. doi: 10.1016/j.asoc.2007.05.007
   Web of Science ®Google Scholar
• Kennedy, James, and Russell C. Eberhart. 2001. Swarm Intelligence. San Francisco, CA: Morgan Kaufmann Publishers Inc.
   Google Scholar
• Lee, Kwang Y., and Mohamed A. El-Sharkawi. 2008. Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems. Pennsylvania: Wiley-IEEE Press.
   Google Scholar
• Li, X.-L., Z.-J. Shao, and J.-X. Qian. 2002. “An Optimizing Method Based on Autonomous Animals: Fish-Swarm Algorithm.” System Engineering: Theory and Practice 22 (11): 32–38.
   Google Scholar
• Li, Zhiyong, Weiyou Wang, Yanyan Yan, and Zheng Li. 2015. “PS-ABC: A Hybrid Algorithm Based on Particle Swarm and Artificial bee Colony for High Dimensional Optimization Problems.” Expert Systems with Applications 42: 8881–8895. doi: 10.1016/j.eswa.2015.07.043
   Web of Science ®Google Scholar
• Matos, Luiza, Daisy Silva, and Edilaine Soler. 2017. “An Analysis of the Branch-and-Bound Method in Solving the Reactive Optimal Power Flow Problem.” Paper presented at the IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Cusco, Peru, 1–4.
   Google Scholar
• Niknam, T., M. R. Narimani, J. Aghaei, and R. Azizipanah-Abarghooee. 2012. “Improved Particle Swarm Optimization for Multi-Objective Optimal Power Flow Considering the Cost, Loss, Emission and Voltage Stability Index.” IET Generation, Transmission & Distribution 6 (6): 515–527. doi: 10.1049/iet-gtd.2011.0851
   Web of Science ®Google Scholar
• Passino, K. M. 2002. “Biomimicry of Bacterial Foraging for Distributed Optimization and Control. Control Systems.” IEEE 22 (3): 52–67.
   Google Scholar
• Preetha Roselyn, J., D. Devaraj, and Subhransu Sekhar Dash. 2018. “Voltage-based Reactive Power Pricing in Deregulated Environment Using Hybrid Multi-Objective Particle Swarm Optimisation.” International Journal of Ambient Energy 39 (3): 285–296. doi:10.1080/01430750.2017.1303628.
   Web of Science ®Google Scholar
• Rambabu, C., Y. P. Obulesu, and C. Saibabu. 2014. “PSO Based Optimal Power Flow with FACTS Devices for Security Enhancement Considering Credible Network Contingencies.” Journal of The Institution of Engineers (India): Series B 96 (2): 147–158. doi: 10.1007/s40031-014-0137-5
   Google Scholar
• Shuqin, Sun, Zhang Bingren, Wang Jun, Yang Nan, and Meng Qingyun. 2013. “Power System Reactive Power Optimization Based on Adaptive Particle Swarm Optimization Algorithm.” Paper presented at the IEEE Fourth International Conference on Digital Manufacturing & Automation, Qingdao, China, 935–939.
   Google Scholar
• Trivedi, Indrajit N., Pradeep Jangir, Siddharth A. Parmar, and Wei Meng. 2016. “Optimal Power Flow with Enhancement of Voltage Stability and Reduction of Power Loss Using ant-Lion Optimizer.” Cogent Engineering 3 (1). doi:10.1080/23311916.2016.1208942.
   Web of Science ®Google Scholar
• Varadarajan, M., and K. S. Swarup. 2008. “Solving Multi-Objective Optimal Power Flow Using Differential Evolution.” IET Generation Transmission Distribution 2 (5): 720–730. doi: 10.1049/iet-gtd:20070457
   Web of Science ®Google Scholar
• Wang, X., X.-Z., Gao, and K. Zenger. 2015. “An Introduction to Harmony Search Optimization Method.” Springer Briefs in Computational Intelligence. doi:10.1007/978-3-319-08356-8_2.
   Google Scholar