Multiobjective reconfiguration of unbalanced distribution networks utilizing improved transient search optimization algorithm contemplating energy high quality and reliability metrics

[ad_1]

  • Sultana, B., Mustafa, M. W., Sultana, U. & Bhatti, A. R. Assessment on reliability enchancment and energy loss discount in distribution system by way of community reconfiguration. Renew. Maintain. Power Rev. 66, 297–310 (2016).

    Article 

    Google Scholar
     

  • Nowdeh, S. A. et al. Fuzzy multi-objective placement of renewable vitality sources in distribution system with goal of loss discount and reliability enchancment utilizing a novel hybrid methodology. Appl. Smooth Comput. 77, 761–779 (2019).

    Article 

    Google Scholar
     

  • Moghaddam, M. J. H. et al. A brand new mannequin for reconfiguration and distributed era allocation in distribution community contemplating energy high quality indices and community losses. IEEE Syst. J. 14, 3530–3538 (2020).

    Article 
    ADS 

    Google Scholar
     

  • Raposo, A. A. M., Rodrigues, A. B. & da Silva, M. D. G. Sturdy meter placement for state estimation contemplating distribution community reconfiguration for annual vitality loss discount. Electr. Energy Syst. Res. 182, 106233 (2020).

    Article 

    Google Scholar
     

  • Izci, D., Ekinci, S., Zeynelgil, H. L. & Hedley, J. Fractional order PID design primarily based on Novel improved slime Mould algorithm. Electr. Energy Parts Syst. 49(9–10), 901–918 (2021).

    Article 

    Google Scholar
     

  • Ekinci, S. et al. Logarithmic spiral search primarily based arithmetic optimization algorithm with selective mechanism and its software to practical electrical stimulation system management. Smooth Comput. 1–13 (2022).

  • Izci, D., Ekinci, S., Eker, E. & Kayri, M. Augmented starvation video games search algorithm utilizing logarithmic spiral opposition-based studying for perform optimization and controller design. J. King Saud Univ.-Eng. Sci. (2022).

  • Izci, D., Hekimoğlu, B. & Ekinci, S. A brand new synthetic ecosystem-based optimization built-in with Nelder-Mead methodology for PID controller design of buck converter. Alex. Eng. J. 61(3), 2030–2044 (2022).

    Article 

    Google Scholar
     

  • Ekinci, S., Izci, D. & Kayri, M. An efficient controller design method for magnetic levitation system utilizing novel improved manta ray foraging optimization. Arab. J. Sci. Eng. 1–22 (2021).

  • Mahela, O. P., Khan, B., Alhelou, H. H. & Siano, P. Energy high quality evaluation and occasion detection in distribution community with wind vitality penetration utilizing stockwell rework and fuzzy clustering. IEEE Trans. Ind. Inform. 16, 6922–6932 (2020).

    Article 

    Google Scholar
     

  • Murty, V. V. V. S. N. & Kumar, A. Optimum DG integration and community reconfiguration in microgrid system with lifelike time various load mannequin utilizing hybrid optimisation. IET Sensible Grid 2, 192–202 (2019).

    Article 

    Google Scholar
     

  • Badran, O., Mekhilef, S., Mokhlis, H. & Dahalan, W. Optimum reconfiguration of distribution system linked with distributed generations: A assessment of various methodologies. Renew. Maintain. Power Rev. 73, 854–867 (2017).

    Article 

    Google Scholar
     

  • Quintero Duran, M. J., Candelo Becerra, J. E. & Sousa Santos, V. Current traits of essentially the most used metaheuristic strategies for distribution community reconfiguration (2017).

  • Abdelaziz, A. Y., Osama, R. A. & El-Khodary, S. M. Reconfiguration of distribution methods for loss discount utilizing the hyper-cube ant colony optimisation algorithm. IET Gen. Transm. Distrib. 6, 176–187 (2012).

    Article 

    Google Scholar
     

  • Wu, W.-C. & Tsai, M.-S. Utility of enhanced integer coded particle swarm optimization for distribution system feeder reconfiguration. IEEE Trans. Energy Syst. 26, 1591–1599 (2011).

    Article 
    ADS 

    Google Scholar
     

  • Nguyen, T. T. & Truong, A. V. Distribution community reconfiguration for energy loss minimization and voltage profile enchancment utilizing cuckoo search algorithm. Int. J. Electr. Energy Power Syst. 68, 233–242 (2015).

    Article 

    Google Scholar
     

  • Flaih, F. M. F., Xiangning, L., Dawoud, S. M. & Mohammed, M. A. Distribution system reconfiguration for energy loss minimization and voltage profile enchancment utilizing Modified particle swarm optimization. In 2016 IEEE PES Asia-Pacific Energy and Power Engineering Convention (APPEEC) 120–124 (IEEE, 2016).

  • Das, D. A fuzzy multiobjective method for community reconfiguration of distribution methods. IEEE Trans. Energy Deliv. 21, 202–209 (2005).

    Article 
    ADS 

    Google Scholar
     

  • Abul’Wafa, A. R. A brand new heuristic method for optimum reconfiguration in distribution methods. Electr. Energy Syst. Res. 81, 282–289 (2011).

    Article 

    Google Scholar
     

  • Su, C.-T., Chang, C.-F. & Chiou, J.-P. Distribution community reconfiguration for loss discount by ant colony search algorithm. Electr. Energy Syst. Res. 75, 190–199 (2005).

    Article 

    Google Scholar
     

  • Abdelaziz, A. Y., Mohamed, F. M., Mekhamer, S. F. & Badr, M. A. L. Distribution system reconfiguration utilizing a modified Tabu Search algorithm. Electr. Energy Syst. Res. 80, 943–953 (2010).

    Article 

    Google Scholar
     

  • Kumar, Ok. S. & Jayabarathi, T. Energy system reconfiguration and loss minimization for an distribution methods utilizing bacterial foraging optimization algorithm. Int. J. Electr. Energy Power Syst. 36, 13–17 (2012).

    Article 

    Google Scholar
     

  • Lotfipour, A. & Afrakhte, H. A discrete Educating–Studying-Based mostly Optimization algorithm to unravel distribution system reconfiguration in presence of distributed era. Int. J. Electr. Energy Power Syst. 82, 264–273 (2016).

    Article 

    Google Scholar
     

  • Jafari, A., Ganjehlou, H. G., Darbandi, F. B., Mohammadi-Ivatloo, B. & Abapour, M. Dynamic and multi-objective reconfiguration of distribution community utilizing a novel hybrid algorithm with parallel processing functionality. Appl. Smooth Comput. 90, 106146 (2020).

    Article 

    Google Scholar
     

  • Abdelaziz, A. Y., Mohammed, F. M., Mekhamer, S. F. & Badr, M. A. L. Distribution methods reconfiguration utilizing a modified particle swarm optimization algorithm. Electr. Energy Syst. Res. 79, 1521–1530 (2009).

    Article 

    Google Scholar
     

  • Jakus, D., Čađenović, R., Vasilj, J. & Sarajčev, P. Optimum reconfiguration of distribution networks utilizing hybrid heuristic-genetic algorithm. Energies 13, 1544 (2020).

    Article 

    Google Scholar
     

  • Gupta, N., Swarnkar, A. & Niazi, Ok. R. Distribution community reconfiguration for energy high quality and reliability enchancment utilizing Genetic Algorithms. Int. J. Electr. Energy Power Syst. 54, 664–671 (2014).

    Article 

    Google Scholar
     

  • Kavousi-Fard, A. & Niknam, T. Multi-objective stochastic distribution feeder reconfiguration from the reliability standpoint. Power 64, 342–354 (2014).

    Article 

    Google Scholar
     

  • Rajaram, R., Kumar, Ok. S. & Rajasekar, N. Energy system reconfiguration in a radial distribution community for decreasing losses and to enhance voltage profile utilizing modified plant progress simulation algorithm with Distributed Technology (DG). Power Rep. 1, 116–122 (2015).

    Article 

    Google Scholar
     

  • Jazebi, S. & Vahidi, B. Reconfiguration of distribution networks to mitigate utilities energy high quality disturbances. Electr. Energy Syst. Res. 91, 9–17 (2012).

    Article 

    Google Scholar
     

  • Ch, Y., Goswami, S. Ok. & Chatterjee, D. Impact of community reconfiguration on energy high quality of distribution system. Int. J. Electr. Energy Power Syst. 83, 87–95 (2016).

    Article 

    Google Scholar
     

  • Hadidian-Moghaddam, M. J., Arabi-Nowdeh, S., Bigdeli, M. & Azizian, D. A multi-objective optimum sizing and siting of distributed era utilizing ant lion optimization method. Ain Shams Eng. J. 9, 2101–2109 (2018).

    Article 

    Google Scholar
     

  • Wu, M., Li, Ok., Kwong, S. & Zhang, Q. Evolutionary multiobjective optimization primarily based on adversarial decomposition. IEEE Trans. Cybern. 50, 753–764 (2018).

    Article 

    Google Scholar
     

  • Qais, M. H., Hasanien, H. M. & Alghuwainem, S. Transient search optimization: A brand new meta-heuristic optimization algorithm. Appl. Intell. 50, 3926–3941 (2020).

    Article 

    Google Scholar
     

  • Kennedy, J. Naked bones particle swarms. In Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS’03 (Cat. No. 03EX706) 80–87 (IEEE, 2003).

  • Mirjalili, S., Mirjalili, S. M. & Lewis, A. Gray wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014).

    Article 

    Google Scholar
     

  • Zhao, W., Zhang, Z. & Wang, L. Manta ray foraging optimization: An efficient bio-inspired optimizer for engineering purposes. Eng. Appl. Artif. Intell. 87, 103300 (2020).

    Article 

    Google Scholar
     

  • Yang, X. & Gandomi, A. H. Bat algorithm: A novel method for international engineering optimization. Eng. Comput. 29(5), 464–483 (2012).

  • Mirjalili, S. The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015).

    Article 

    Google Scholar
     

  • Ewees, A. A., Abd Elaziz, M. & Houssein, E. H. Improved grasshopper optimization algorithm utilizing opposition-based studying. Knowledgeable Syst. Appl. 112, 156–172 (2018).

    Article 

    Google Scholar
     

  • Jahannoush, M. & Nowdeh, S. A. Optimum designing and administration of a stand-alone hybrid vitality system utilizing meta-heuristic improved sine–cosine algorithm for Leisure Heart, case research for Iran nation. Appl. Smooth Comput. 96, 106611 (2020).

    Article 

    Google Scholar
     

  • Gupta, S. & Deep, Ok. A hybrid self-adaptive sine cosine algorithm with opposition primarily based studying. Knowledgeable Syst. Appl. 119, 210–230 (2019).

    Article 

    Google Scholar
     

  • Carrasco, J., García, S., Rueda, M. M., Das, S. & Herrera, F. Current traits in the usage of statistical checks for evaluating swarm and evolutionary computing algorithms: Sensible pointers and a vital assessment. Swarm Evol. Comput. 54, 100665 (2020).

    Article 

    Google Scholar
     

  • Derrac, J., García, S., Hui, S., Suganthan, P. N. & Herrera, F. Analyzing convergence efficiency of evolutionary algorithms: A statistical method. Inf. Sci. 289, 41–58 (2014).

    Article 

    Google Scholar
     

  • Kasaei, M. J. & Nikoukar, J. DG allocation with consideration of prices and losses in distribution networks utilizing ant colony algorithm. Majlesi J. Electr. Eng. 10, (2016).

  • Karimianfard, H. & Haghighat, H. An initial-point technique for optimizing distribution system reconfiguration. Electr. Energy Syst. Res. 176, 105943 (2019).

    Article 

    Google Scholar
     

  • Zhang, D., Fu, Z. & Zhang, L. An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution methods. Electr. energy Syst. Res. 77, 685–694 (2007).

    Article 

    Google Scholar
     

  • Zhu, J. Z. Optimum reconfiguration {of electrical} distribution community utilizing the refined genetic algorithm. Electr. Energy Syst. Res. 62, 37–42 (2002).

    Article 

    Google Scholar
     

  • [ad_2]

    Source_link

    Leave a Reply

    Your email address will not be published. Required fields are marked *