A Novel iDNA-Inspired Mosquito Optimization Strategy for Simultaneous Economic Load Dispatch and Non- Technical Loss Detection in Smart Grids

Abstract

In the management of modern smart grids, the detection of non-technical losses (loads caused by unauthorized use or cyber-attacks) and the simultaneous minimization of production costs constitute a complex optimization problem. Classical meta-heuristic algorithms typically model the system under ideal conditions and disregard physical anomalies on the grid. This study proposes a new iDNA-Based Mosquito Optimization Strategy (iDNA-MOS), inspired by the ability of mosquitoes in biological research to analyze species based on blood samples taken from hosts (iDNA - invertebrate DNA). The proposed method combines standard mosquito movement mechanisms (Lévy flight) with a unique bio-diagnostic operator that diagnoses network imbalances. The performance of iDNA-MOS was evaluated on the IEEE 30-Bus test system, which includes five different mathematical benchmark functions and 25 MW of hidden theft load. Simulation results show that iDNA-MOS converges faster than its competitors in tests such as Sphere and Ackley. On the IEEE 30-Bus system, iDNA-MOS demonstrated statistically significant (p < 0.05, Wilcoxon Rank-Sum) superior performance compared to the standard Mosquito Algorithm in the literature (Lit-MFO: 942.24 $/h), with an hourly cost of 901.87 $/h. Although it produced results similar to the industry standard PSO in terms of cost (p ≈ 0.95), iDNA-MOS was the algorithm that most accurately compensated for the hidden load by reaching a total generation capacity of 312.13 MW, which is critical for system reliability.

Keywords

• Economic Load Dispatch
• iDNA-MOS
• Fault Detection
• Meta-heuristic Optimization
• IEEE 30-Bus

References

1. 1. Okelola MO, Akanbi SBA, Oyedokun JA, Oladeji O, Opoola NA. Economic dispatch techniques under varying load and renewable integration scenarios: a systematic review. New Energy Exploit Appl. 2025;4(2):283–301. doi:10.54963/neea. v4i2.1770
2. 2. Janefar S, Chowdhury P, Yeassin R, Hasan M, Chowdhury NUR. Comparative performance evaluation of economic load dispatch using metaheuristic techniques: a practical case study for Bangladesh. Results Eng. 2025;26:104720. doi:10.1016/j. rineng.2025.104720
3. 3. Ökten M. Enerji sistemlerinde metasezgisel optimizasyon teknikleri: yenilikçi algoritmalar ve uygulama alanları. Sürdürülebilir Mühendislik Uygulamaları ve Teknolojik Gelişmeler Dergisi. 2024;7(2):153–171. doi:10.51764/smutgd.1542508
4. 4. Gautam A, Paliwal P, Arya A. Efficient placement of distributed generation for loss minimization via PSO optimization. In: Proc IEEE ICEPES; 2024; Bhopal, India. p. 1–7. doi:10.1109/ICEPES60647.2024.10653475
5. 5. Zhuang Z, Wu M. Optimized estimation of future power losses in low voltage distribution systems based on particle swarm optimization and genetic algorithm. In: Proc Int Conf New Energy System Power Eng (NESP); 2025; Fuzhou, China. p. 716–721. doi:10.1109/NESP65198.2025.11041365
6. 6. Asabere P, Sekyere F, Ayambire P, Ofosu WK. Optimal capacitor bank placement and sizing using particle swarm optimization for power loss minimization in distribution network. J Eng Res. 2025;13(2):1307–1315. doi:10.1016/j.jer.2024.03.007
7. 7. Dev A, Bhatt K, Mondal B, et al. Enhancing load frequency control and automatic voltage regulation in interconnected power systems using the walrus optimization algorithm. Sci Rep. 2024;14:27839. doi:10.1038/s41598-024-77113-2
8. 8. Sharma A, Singh N. Load frequency control of connected multi-area multi-source power systems using energy storage and lyrebird optimization algorithm tuned PID controller. J Energy Storage. 2024;100(Pt B):113609. doi:10.1016/j.est.2024.113609

9. 9. Iqbal MS, Limon MFA, Kabir MM, Rabby MKM, Soeb MJA, Jubayer MF. A hybrid optimization algorithm for improving load frequency control in interconnected power systems. Expert Syst Appl. 2024;249(Pt B):123702. doi:10.1016/j.eswa.2024.123702
10. 10. Panthi M, Das T. Intelligent intrusion detection scheme for smart power-grid using optimized ensemble learning on selected features. Int J Crit Infrastruct Prot. 2022;39:100567. doi:10.1016/j. ijcip.2022.100567
11. 11. Khaleghi A, Karimipour H. Investigation of detection mechanisms against false data injection attacks based on machine learning approaches. In: Azad S, Nazari-Heris M, editors. Artificial Intelligence in the Operation and Control of Digitalized Power Systems. Cham: Springer; 2024. doi:10.1007/978-3-031-69358-8_9
12. 12. Irfan M, et al. A survey on detection and localisation of false data injection attacks in smart grids. IET Cyber-Phys Syst Theory Appl. 2024;9(4):313–333. doi:10.1049/cps2.12093
13. 13. Khacha A, Saadouni R, Harbi Y, Gherbi C, Aliouat Z, Harous S. Federated learning and convolutional autoencoder for robust anomaly detection in agricultural IoT. In: Proc IEEE AFROS; 2024. p. 1–5. doi:10.1109/AFROS62115.2024.11037027
14. 14. Luo X, Zhu W, Chang S, Wang X. Secure optimization dispatch framework with false data injection attack in hybrid-energy ship power system under the constraints of safety and economic efficiency. Electricity. 2025;6(3):38. doi:10.3390/electricity6030038
15. 15. Zadsar M, Abazari A, Ameli A, Yan J, Ghafouri M. Prevention and detection of coordinated false data injection attacks on integrated power and gas systems. IEEE Trans Power Syst. 2023;38(5):4252–4268. doi:10.1109/TPWRS.2022.3216118
16. 16. Le J, Lang H, Wang J, Wang W, Luo G. The information security issues of distributed economic dispatch for new generation power systems—present situation and forecast. Electronics. 2024;13(14):2680. doi:10.3390/electronics13142680
17. 17. Imran HK, Al-Kafaji MSA. Electricity theft detection in smart grids based on machine learning techniques. Al-Furat J Innov Electron Comput Eng. 2025;4(1):160–183. doi:10.46649/fjiece. v4.1.12a.25.3.2025
18. 18. Bhatnagar N, Johari S, Sehgal VK, Nandal A, Alhudhaif A, Polat K, Singh S. Identification of theft in a power distribution network: a tree-based approach. Appl Soft Comput. 2024;167(Pt C):112449. doi:10.1016/j.asoc.2024.112449
19. 19. Gbafore E, Segera DR, Kiruki CRM. Genetic artificial hummingbird algorithm–support vector machine for timely power theft detection. Sci World J. 2024;2024:5568922. doi:10.1155/2024/5568922
20. 20. Arias-Marín C, Barragán-Escandón A, Toledo-Orozco M, Serrano-Guerrero X. Methodological validation of machine learning models for non-technical loss detection in electric power systems: a case study in an Ecuadorian electricity distributor. Appl Sci. 2025;15(7):3912. doi:10.3390/app15073912
21. 21. Zhang W, Cao Q, Yang S, et al. Detection of power theft in sensitive stations based on generalized robust distance metric and multi-classification support vector machine. Discov Appl Sci. 2025;7:358. doi:10.1007/s42452-025-06834-4
22. 22. Niste DF, Tîrnovan R, Pavel S, Beleiu H, Andrei C, Misaros M. Electricity losses in focus: detection and reduction strategies—state of the art. Appl Sci. 2025;15:3517. doi:10.3390/app15073517
23. 23. Ravikumar G, Govindarasu M. Anomaly detection and mitigation for wide-area damping control using machine learning. IEEE Trans Smart Grid. 2020;11:1–10. doi:10.1109/TSG.2020.2995313
24. 24. Alqushaibi A, Hasan MH, Jadid Abdulkadir S, Danyaro KU, Al-Selwi SM, Ragab MG, Hamid Sumiea E, Al-Zawari A, Mahamad S. Mosquito optimizer: a nature-inspired meta-heuristic algorithm. In: Proc 8th Int Conf Comput Commun Control Autom (ICCUBEA); 2024; Pune, India. p. 1–7. doi:10.1109/ICCUBEA61740.2024.10774876
25. 25. Sha’ban ZR, Alkallak IN. Adaptation of a mosquito swarm algorithm to solve a binary knapsack problem. AL-Rafidain J Comput Sci Math. 2018;12(2):13–24. doi:10.33899/csmj.2018.163574
26. 26. Samarathunga W, Kurimoto Y, Ransen R. Developing a cylinder pump with self-cleaning anti-clog pulse flow, finding optimum purity for anti-clog pulse based on mosquito swarm optimization. Int J Multidiscip Res Growth Eval. 2025;6(2):915–918. doi:10.54660/IJMRGE.2025.6.2.915-918
27. 27. Alauddin M. Mosquito flying optimization (MFO). In: Proc Int Conf Electr Electron Optim Tech (ICEEOT); 2016; Chennai, India. p. 79–84. doi:10.1109/ICEEOT.2016.7754783
28. 28. Alauddin M. V-MFO: variable flight mosquito flying optimization. In: Ali R, Beg M, editors. Applications of Soft Computing for the Web. Singapore: Springer; 2017. doi:10.1007/978-981-10-7098-3_15
29. 29. Schrepp M. On the usage of Cronbach’s alpha to measure reliability of UX scales. J User Exp. 2020;15(4):247–258.