Dwarf Mongoose Optimization Algorithm: Minimization of Total Harmonic Distortion in Multi-Level Inverter Output Voltage

Öz

The Total Harmonic Distortion (THD) value is an important metric used to evaluate the performance of various devices and systems in electrical systems. A low THD value is expected for optimal performance. In this study, the THD value of the output voltage of a cascaded H-bridge multi-level inverter (CHB-MLI) was optimized by using a newly developed Dwarf Mongoose Optimization (DMO) algorithm. The performance of the adapted DMO algorithm has been tested alongside the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms on a three-phase, nine-level cascaded H-bridge multi-level inverter (CHB-MLI). Convergence, consistency, and speed tests have been conducted for these algorithms. After detailed validation through MATLAB simulations, the results were analyzed, and it was concluded that the DMO algorithm outperformed the mentioned algorithms in the modulation index range of 0.1 to 1.0. In conclusion, it has been demonstrated that the DMO algorithm can effectively optimize the THD value of the output voltage of CHB-MLI.

Anahtar Kelimeler

• Dwarf Mongoose Optimization Algorithm
• Total Harmonic Distortion
• Multi-level Inverter
• Fast Fourier Transform Analysis

Cüce kuyruksüren optimizasyon algoritması: Çok seviyeli inverter çıkış geriliminin toplam harmonik distorsiyon değerinin azaltılması

Öz

Toplam Harmonik Distorsiyon (THD) değeri, elektrik sistemlerinde kullanılan çeşitli cihazların ve sistemlerin performansını değerlendirmek için kullanılan önemli bir ölçüttür. THD değerinin olabildiğince düşük değerde olması beklenir. Bu çalışmada, kaskad h-köprü çok seviyeli inverter (KHK-ÇSİ) çıkış geriliminin THD değerini en iyi seviyeye getirmek için yeni geliştirilen Cüce Kuyruksüren Optimizasyon (DMO) algoritması uyarlanmıştır. Uyarlanan DMO algoritmasının performansı, üç fazlı dokuz seviyeli kaskad h-köprü çok seviyeli (KHK-ÇSİ) üzerinde Genetik Algoritma (GA) ve Parçacık Sürü Optimizasyonu (PSO) algoritmaları ile birlikte test edilmiştir. Bu algoritmaların yakınsama, tutarlılık ve hız testleri yapılmıştır. Sonuçlar, MATLAB simülasyonlarıyla ayrıntılı bir şekilde doğrulandıktan sonra analiz edilmiş ve DMO algoritmasının 0.1 ile 1.0 modülasyon indeksi aralığında belirtilen algoritmalardan daha üstün performans gösterdiği sonucuna varılmıştır. Sonuç olarak, KHK-ÇSİ çıkış geriliminin THD değerini optimize etmek için DMO algoritmasının etkili bir şekilde kullanılabileceğini gösterilmiştir.

Anahtar Kelimeler

• Cüce Kuyruksüren Optimizasyon Algoritması
• Toplam harmonik distorsiyon
• Çok seviyeli inverter
• Hızlı Fourier transform analizi

Kaynakça

1. Mittal, N., Singh, B., Singh, S. P., Dixit, R., & Kumar, D. (2012, December). Multi-level inverters: A literature survey on topologies and control strategies. In 2012 2nd International Conference on Power, Control and Embedded Systems (pp. 1-11). IEEE.
2. Panigrahi, R., Mishra, S. K., Srivastava, S. C., Srivastava, A. K., & Schulz, N. N. (2020). Grid integration of small-scale photovoltaic systems in secondary distribution network—A review. IEEE Transactions on Industry Applications, 56(3), 3178-3195.
3. Djehaf, M. A., Zidi, S. A., Kobibi, Y. D., & Hadjeri, S. (2015, March). Modeling of a multi-level converter based VSC HVDC supplying a dead load. In 2015 International Conference on Electrical and Information Technologies (ICEIT) (pp. 218-223). IEEE.
4. Zhou, B., Li, B., He, J., Li, Y., & Li, Q. (2023). A novel mitigation strategy of subsequent commutation failures in the hybrid cascaded LCC-MMC HVDC transmission system. International Journal of Electrical Power & Energy Systems, 148, 108969.
5. Shashank, B. P., Ananya, T., Venkatesh, S., & Srinivas, N. (2023, May). Application of Multi Level Inverter Using MMC and FACTS in Distributed Energy Systems. In 2023 4th International Conference for Emerging Technology (INCET) (pp. 1-5). IEEE.
6. Behbahanifard H, Abazari S, Sadoughi A (2019) New scheme of SHE-PWM technique for cascaded multi-level inverters with regulation of DC voltage sources. ISA Trans
7. Arani AAK, Ghasemi A, Karami H et al (2019) optimal switching algorithm for different topologies of 15-level inverter using genetic algorithm. In: 2019 IEEE 5th conference on knowledge based engineering and innovation (KBEI 2019), pp 352–358
8. Fei, W., Du, X., & Wu, B. (2009). A generalized half-wave symmetry SHE-PWM formulation for multi-level voltage inverters. IEEE Transactions on Industrial Electronics, 57(9), 3030-3038.

9. Hutson CM, Venayagamoorthy GK, Corzine KA (2008) Optimal SVM switching for a multi-level multi-phase machine using modified discrete PSO. In: 2008 IEEE Swarm Intelligence Symposium
10. Sumithira TR, Nirmal Kumar A (2013) Elimination of harmonics in multi-level inverters connected to solar photovoltaic systems using ANFIS: an experimental case study. J Appl Res Technol 11:124–132.
11. Karaca, H., & Bektas, E. (2016). Selective Harmonic Elimination Using Genetic Algorithm for Multi-level Inverter with Reduced Number of Power Switches. Engineering Letters, 24(2).
12. Jing, T., Maklakov, A. S., & Gasiyarova, O. A. (2019, January). Research on selective harmonic elimination technique based on particle swarm optimization. In 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) (pp. 694-700). IEEE.
13. Sayyah A, Aflaki M, Rezazadeh A (2008) Optimization of total harmonic current distortion and torque pulsation reduction in high-power induction motors using genetic algorithms. J Zhejiang Univ Sci A 9:1741–1752
14. Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer methods in applied mechanics and engineering, 391, 114570.
15. Akinola, O. A., Ezugwu, A. E., Oyelade, O. N., & Agushaka, J. O. (2022). A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets. Scientific Reports, 12(1), 14945.
16. Aldosari, F., Abualigah, L., & Almotairi, K. H. (2022). A normal distributed dwarf mongoose optimization algorithm for global optimization and data clustering applications. Symmetry, 14(5), 1021.
17. Agushaka, J. O., Ezugwu, A. E., Olaide, O. N., Akinola, O., Zitar, R. A., & Abualigah, L. (2023). Improved dwarf mongoose optimization for constrained engineering design problems. Journal of Bionic Engineering, 20(3), 1263-1295.
18. Raj, M. G., & Pani, S. K. (2023). Intrusion detection system using combination of deep residual fuzzy network and white shark-dwarf mongoose optimization. Soft Computing, 1-17.
19. Dora, B. K., Bhat, S., Halder, S., & Sahoo, M. (2023). Solution of Reactive Power Dispatch problems using Enhanced Dwarf Mongoose Optimization Algorithm. In 2023 International Conference for Advancement in Technology (ICONAT) (pp. 1-6). IEEE.
20. Çelik, E. (2023). IEGQO-AOA: information-exchanged Gaussian arithmetic optimization algorithm with quasi-opposition learning. Knowledge-Based Systems, 260, 110169.
21. Çelik, E., Öztürk, N., & Arya, Y. (2021). Advancement of the search process of salp swarm algorithm for global optimization problems. Expert Systems with Applications, 182, 115292.