Hybrid Butterfly–Firefly Machine Learning Optimization for Enhanced Performance of IEEE Distribution Systems
DOI:
https://doi.org/10.13052/spee1048-5236.45311Keywords:
Butterfly optimization algorithm (BOA), firefly algorithm (FA), support vector regression (SVR), power quality improvement, reliability index optimization, environmental impact minimization, load balancing, demand-side management, hybrid FACTS controller, IEEE distribution systems, voltage stability, analytic hierarchy process (AHP), technique for order of preference by similarity to ideal solution (TOPSIS)Abstract
This paper suggests a new Hybrid Butterfly-Firefly Machine Learning Optimization (HBFL-OM) framework to enhance the operational performance of the IEEE distribution systems by optimizing the position and setting parameters of a Hybrid Static Compensator-Smart Voltage Stabilizer (HSVC). The HBFL-OM algorithm is a hybridization of Butterfly Optimization Algorithm (BOA) global exploration method and the Firefly Algorithm (FA) local refinement, where the Support Vector Regression (SVR) is added to learn the predictions and converge faster. They are optimized at the same time with multi-objective functions, such as minimization of power loss, power quality (THD), improvement of reliability (SAIFI/SAIDI), and balancing of loads. The AHP and TOPSIS are used to determine the best bus to place HSVC. The performances of the proposed HBFL-OM on IEEE 33-bus and 69-bus test systems prove that the given algorithm is better than GA and PSO algorithms with the subsequent results: 22.1% reduced power losses, 26% improved THD, and 20% increased indices of reliability with the preservation of balanced load distribution. The framework offers a strong and data driven solution that can be optimally utilized to optimize power systems in real-time.
Downloads
References
A. Allouhi, M. Benzakour Amine, K.A. Tabet Aoul, Metaheuristic multi-objective optimization with artificial neural networks surrogate modeling for optimal energy-economic performance for CSP technology, Energy and AI, Volume 20, 2025, 100488, ISSN 2666-5468, https://doi.org/10.1016/j.egyai.2025.100488.
Nassef, Ahmed M., Mohammad Ali Abdelkareem, Hussein M. Maghrabie, and Ahmad Baroutaji. 2023. “Review of Metaheuristic Optimization Algorithms for Power Systems Problems” Sustainability 15, no. 12: 9434. https://doi.org/10.3390/su15129434.
Arora, S., Singh, S. Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23, 715–734 (2019). https://doi.org/10.1007/s00500-018-3102-4.
Subhashree Choudhury, Gagan Kumar Sahoo, A critical analysis of different power quality improvement techniques in microgrid, e-Prime – Advances in Electrical Engineering, Electronics and Energy, Volume 8, 2024, 100520, ISSN 2772-6711, https://doi.org/10.1016/j.prime.2024.100520.
Miloš Pantoš, Lucija Lukas, Enhancing power system reliability through demand flexibility of Grid-Interactive Efficient Buildings: A thermal model-based optimization approach, Applied Energy, Volume 381, 2025, 125045, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2024.125045.
Linas Gelazanskas, Kelum A.A. Gamage, Demand side management in smart grid: A review and proposals for future direction, Sustainable Cities and Society, Volume 11, 2014, Pages 22-30, ISSN 2210-6707, https://doi.org/10.1016/j.scs.2013.11.001.
Abdul Hameed Soomro, Abdul Sattar Larik, Mukhtiar Ahmed Mahar, Anwer Ali Sahito, Mohsin Ali Koondhar, Yun-Su Kim, Zuhair Muhammed Alaas, Ezzeddine Touti, M.M.R. Ahmed, Enhancement of power quality based on dynamic voltage restorer matrix inverter-sliding mode control scheme, Electric Power Systems Research, Volume 241, 2025, 111408, ISSN 0378-7796, https://doi.org/10.1016/j.epsr.2025.111408.
Guocheng Song, Qiuwei Wu, Wenshu Jiao, Lina Lu, Distributed coordinated control for voltage regulation in active distribution networks based on robust model predictive control, International Journal of Electrical Power & Energy Systems, Volume 166, 2025, 110529, ISSN 0142-0615, https://doi.org/10.1016/j.ijepes.2025.110529.
Abu Shufian, Sowrov Komar Shib, Durjoy Roy Dipto, Md Tanvir Rahman, Nasif Hannan, Shaikh Anowarul Fattah, Fuzzy logic-controlled three-phase dynamic voltage restorer for enhancing voltage stabilization and power quality, International Journal of Electrical Power & Energy Systems, Volume 166, 2025, 110517, ISSN 0142-0615, https://doi.org/10.1016/j.ijepes.2025.110517.
Awais Farooqi, Muhammad Murtadha Othman, Mohd Amran Mohd Radzi, Ismail Musirin, Siti Zaliha Mohammad Noor, Izham Zainal Abidin, Dynamic voltage restorer (DVR) enhancement in power quality mitigation with an adverse impact of unsymmetrical faults, Energy Reports, Volume 8, Supplement 1, 2022, Pages 871-882, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2021.11.147.
A. Rezaee Jordehi, Particle swarm optimisation (PSO) for allocation of FACTS devices in electric transmission systems: A review, Renewable and Sustainable Energy Reviews, Volume 52, 2015, Pages 1260-1267, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2015.08.007.
Seyed Abbas Taher, Muhammad Karim Amooshahi, New approach for optimal UPFC placement using hybrid immune algorithm in electric power systems, International Journal of Electrical Power & Energy Systems, Volume 43, Issue 1, 2012, Pages 899-909, ISSN 0142-0615, https://doi.org/10.1016/j.ijepes.2012.05.064.
Ahmed A. Shehata, Mohamed A. Tolba, Ali M. El-Rifaie, Nikolay V. Korovkin, Power system operation enhancement using a new hybrid methodology for optimal allocation of FACTS devices, Energy Reports, Volume 8, 2022, Pages 217-238, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2021.11.241.
M. N. Dehaghani, T. Kor�tko and A. Rosin, “AI Applications for Power Quality Issues in Distribution Systems: A Systematic Review,” in IEEE Access, vol. 13, pp. 18346–18365, 2025, doi:10.1109/ACCESS.2025.3533702.
Veenus Kansal, J.S. Dhillon, Emended salp swarm algorithm for multiobjective electric power dispatch problem, Applied Soft Computing, Volume 90, 2020, 106172, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2020.106172.
S.K. Mishra, S.K. Bhuyan, P.V. Rathod, Performance analysis of a hybrid renewable generation system connected to grid in the presence of DVR, Ain Shams Engineering Journal, Volume 13, Issue 4, 2022, 101700, ISSN 2090-4479, https://doi.org/10.1016/j.asej.2022.101700.
Karthikeyan, M., Manimegalai, D. & RajaGopal, K. Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection. Sci Rep 14, 231 (2024). https://doi.org/10.1038/s41598-023-50554-x.
F. Rezazadeh and N. Bartzoudis, “A Federated DRL Approach for Smart Micro- Grid Energy Control with Distributed Energy Resources,” 2022 IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Paris, France, 2022, pp. 108–114, doi:10.1109/CAMAD55695.2022.9966919.
Willy Kriswardhana, Bladimir Toaza, Domokos Eszterg�r-Kiss, Szabolcs Duleba, “Analytic hierarchy process in transportation decision-making: A two-staged review on the themes and trends of two decades”, Expert Systems with Applications, Volume 261, 2025, 125491, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2024.125491.
Ravi Kumar Avvari, Vinod Kumar D.M., Anil Kumar Annamraju, Nareddy Nageswara Reddy, “A TOPSIS based multi-objective optimal power flow approach using the artificial hummingbird algorithm for renewable energy and PEV integrated system”, Measurement, Volume 256, Part C, 2025, 118337, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2025.118337.
Sarkar, P., Gayen, S.K. Application of Entropy-AHP and WASPAS methods for prioritizing the sub watersheds of Teesta River basin in terms of soil erosion susceptibility. Discov Environ 2, 135 (2024). https://doi.org/10.1007/s44274-024-00163-w.
Guo-Niu Zhu, Jie Hu, Hongliang Ren, A fuzzy rough number-based AHP- TOPSIS for design concept evaluation under uncertain environments, Applied Soft Computing, Volume 91, 2020, 106228, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2020.106228.
Fan, M., Chen, J., Xie, Z. et al. Improved multi-objective differential evolution algorithm based on a decomposition strategy for multi-objective optimization problems. Sci Rep 12, 21176 (2022). https://doi.org/10.1038/s41598-022-25440-7.
Ehab E. Elattar, Salah K. ElSayed,”Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement”, Energy, Volume 178, 2019, Pages 598–609, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2019.04.159.
Bouaouda A, Sayouti Y. Hybrid Meta-Heuristic Algorithms for Optimal Sizing of Hybrid Renewable Energy System: A Review of the State-of-the-Art. Arch Comput Methods Eng. 2022;29(6):4049–4083. doi:10.1007/s11831-022-09730-x. Epub 2022 Mar 16. PMID: 35313649; PMCID: PMC8926421.
Bruzzone, Agostino G., Marco Gotelli, Marina Massei, Xhulia Sina, Antonio Giovannetti, Filippo Ghisi, and Luca Cirillo. 2025. “Memetic Optimization of Wastewater Pumping Systems for Energy Efficiency: AI Optimization in a Simulation-Based Framework for Sustainable Operations Management” Sustainability 17, no. 14: 6296. https://doi.org/10.3390/su17146296.
Jinpeng Wu, Bo Zhang, Jinliang He, Rong Zeng, “Optimal design of tower footing device with combined vertical and horizontal grounding electrodes under lightning”, Electric Power Systems Research, Volume 113, 2014,, Pages 188–195, ISSN 0378-7796, https://doi.org/10.1016/j.epsr.2014.03.021.
Ngei UM, Nyete AM, Moses PM, Wekesa C. “Optimal sizing and placement of STATCOM, TCSC and UPFC using a novel hybrid genetic algorithm-improved particle swarm optimization”, Heliyon. 2024 Nov 23;10(23):e40682. doi:10.1016/j.heliyon.2024.e40682. PMID: 39687142; PMCID: PMC11647813. https://pmc.ncbi.nlm.nih.gov/articles/PMC11647813/.
V. Mukherjee and S. K. Parida, “Optimal Placement and Parameter Setting of SVC and TCSC using Artificial Bee Colony Algorithm for Damping Power System Oscillations,” Applied Soft Computing, vol. 26, pp. 346–360, 2015. DOI:10.1016/j.asoc.2014.10.019, https://doi.org/10.1016/j.asoc.2014.10.019.
M. Saravanan, S. M. R. Slochanal, P. Venkatesh, and S. J. P. Abraham, “Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability,” Electric Power Systems Research, vol. 77, no. 3–4, pp. 276–283, 2007. DOI:10.1016/j.epsr.2006.03.006, https://doi.org/10.1016/j.epsr.2006.03.006.
A. A. Alassmi and M. J. Rawa, “Optimum Location and Sizing of FACTS Devices Using Genetic Algorithm,” International Journal of Engineering Research and Technology, vol. 14, no. 8, pp. 816–822, 2021.
Y. Liu, D. �etenovi�, H. Li, E. Gryazina, and V. Terzija, “An optimized multi- objective reactive power dispatch strategy based on improved genetic algorithm for wind power integrated systems,” Electrical and Electronic Engineering, vol. 30, no. 6, pp. 4810–4822, June 2022. Available: https://doi.org/10.1016/j.ijepes.2021.107764.
S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46–61, Mar. 2014.
S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, May 2016.
S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili, “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems,” Advances in Engineering Software, vol. 114, pp. 163–191, Dec. 2017.
A. Nayak, P. K. Hota, and P. K. Dash, “Application of Grey Wolf Optimizer for optimal power flow in power systems with FACTS devices,” International Journal of Electrical Power & Energy Systems, vol. 74, pp. 404–419, Jan. 2016.
R. Kumar, P. Yadav, and S. K. Singh, “Optimal reactive power dispatch using Whale Optimization Algorithm,” International Journal of Electrical Power & Energy Systems, vol. 83, pp. 262–275, Dec. 2016.
M. A. E. Aziz, E. E. Elattar, and S. S. Ghoneim, “An improved Salp Swarm Algorithm for multi-objective optimal power flow incorporating renewable energy sources,” Applied Soft Computing, vol. 97, p. 106759, Dec. 2020.
https://aeeeuropeenergy.com/
