Araştırma Makalesi
BibTex RIS Kaynak Göster

Çok Seviyeli Eviricilerde Seçici Harmonik Eliminasyon İçin PSO Varyantlarının Performans Değerlendirmesi

Yıl 2025, Cilt: 12 Sayı: 25, 93 - 112, 30.04.2025

Hüseyin Doğan

https://doi.org/10.54365/adyumbd.1662482

Öz

Bu çalışmada, farklı Parçacık Sürü Optimizasyonu (PSO) varyantlarının Kaskat Bağlı H-Köprü Çok Seviyeli Eviricinin (CHB-MLI) Seçici Harmonik Eliminasyon (SHE) denklemlerini çözmedeki performansları karşılaştırılmıştır SHE yöntemi, özellikle yüksek seviyeli sistemlerde analitik olarak çözülmesi oldukça zor olan doğrusal olmayan transandantal denklemlerden oluşmaktadır. Çalışmanın amacı, PSO varyantlarının genel olarak sağladığı iyileştirmeleri incelemek ve özellikle bu tür karmaşık mühendislik problemlerindeki etkinliklerini değerlendirmektir. Standart PSO'nun yanı sıra, literatürdeki geliştirilmiş bazı PSO versiyonları ele alınmıştır. Her varyant için SHE denklemlerini çözen optimal anahtarlama açıları belirlenerek MATLAB/Simulink ortamında bir CHB-MLI modeline uygulanmıştır. Algoritmaların yakınsama davranışları, çıkış gerilimlerinin toplam harmonik distorsiyonları ve temel bileşen genlikleri istatistiksel olarak analiz edilmiştir. Bu analizler sonucunda, PSO varyantlarının optimizasyon süreçlerindeki güçlü ve zayıf yönleri ortaya konmuştur. Ayrıca elde edilen bulgular doğrultusunda, başarılı varyantların seçilen güçlü özelliklerinin entegre edildiği hibrit bir model de önerilmiştir. Özellikle önerilen hibrit modelin, en kötü senaryolarda dahi istikrarlı ve rekabetçi bir performans sergileyerek diğer varyantlardan ayrıştığı gözlemlenmiştir. Bu bulgular, etkin şekilde geliştirilen PSO varyantlarının gerçek dünya optimizasyon problemlerinin çözümünde güçlü bir alternatif olabileceğini göstermektedir.

Anahtar Kelimeler

PSO SHE CHB-MLI Optimizasyon PSO Varyantı Çok Seviyeli Evirici

Kaynakça

  • Eberhart R, Kennedy J. A new optimizer using particle swarm theory. MHS95 Proc Sixth Int Symp Micro Mach Hum Sci [Internet]. 1995 [cited 2025 Mar 17]. p. 39–43. Available from: https://ieeexplore.ieee.org/document/494215.
  • Gad AG. Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. Arch Comput Methods Eng. 2022;29(5):2531–2561.
  • Xu H, Deng Q, Zhang Z, et al. A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies. Sci Rep. 2025;15(1):4518.
  • Shaqarin T, Noack BR. A Fast-Converging Particle Swarm Optimization through Targeted, Position-Mutated, Elitism (PSO-TPME). Int J Comput Intell Syst. 2023;16(1):6.
  • Jain M, Saihjpal V, Singh N, et al. An Overview of Variants and Advancements of PSO Algorithm. Appl Sci. 2022;12(17):8392.
  • Zhan Z-H, Zhang J, Li Y, et al. Adaptive Particle Swarm Optimization. IEEE Trans Syst Man Cybern Part B Cybern. 2009;39(6):1362–1381.
  • Chauhan P, Deep K, Pant M. Novel inertia weight strategies for particle swarm optimization. Memetic Comput. 2013;5(3):229–251.
  • Fan S-KS, Chiu Y-Y. A decreasing inertia weight particle swarm optimizer. Eng Optim. 2007;39(2):203–228.
  • Feng Y, Yao Y-M, Wang A-X. Comparing with Chaotic Inertia Weights in Particle Swarm Optimization. 2007 Int Conf Mach Learn Cybern [Internet]. 2007 [cited 2025 Feb 23]. p. 329–333. Available from: https://ieeexplore.ieee.org/abstract/document/4370164.
  • Yang K, Zhang Q, Zhang J, et al. Unified Selective Harmonic Elimination for Multilevel Converters. IEEE Trans Power Electron. 2017;32(2):1579–1590.
  • Dahidah MSA, Konstantinou G, Agelidis VG. A Review of Multilevel Selective Harmonic Elimination PWM: Formulations, Solving Algorithms, Implementation and Applications. IEEE Trans Power Electron. 2015;30(8):4091–4106.
  • Yang K, Yuan Z, Yuan R, et al. A Groebner Bases Theory-Based Method for Selective Harmonic Elimination. IEEE Trans Power Electron. 2015;30(12):6581–6592.
  • Bertin T, Despesse G, Thomas R. Comparison between a Cascaded H-Bridge and a Conventional H-Bridge for a 5-kW Grid-Tied Solar Inverter. Electronics. 2023;12(8):1929.
  • Mao W, Zhang X, Hu Y, et al. A Research on Cascaded H-Bridge Module Level Photovoltaic Inverter Based on a Switching Modulation Strategy. Energies. 2019;12(10):1851.
  • Lingom PM, Song-Manguelle J, Nyobe-Yome JM, et al. A Comprehensive Review of Compensation Control Techniques Suitable for Cascaded H-Bridge Multilevel Inverter Operation with Unequal DC Sources or Faulty Cells. Energies. 2024;17(3):722.
  • Sharma B, Manna S, Saxena V, et al. A comprehensive review of multi-level inverters, modulation, and control for grid-interfaced solar PV systems. Sci Rep. 2025;15(1):661.
  • Memon MA, Mekhilef S, Mubin M, et al. Selective harmonic elimination in inverters using bio-inspired intelligent algorithms for renewable energy conversion applications: A review. Renew Sustain Energy Rev. 2018;82:2235–2253.
  • Zhang H, Li D. Applications of computer vision techniques to cotton foreign matter inspection: A review. Comput Electron Agric. 2014;109:59–70.
  • Bektaş Y, Karaca H, Taha TA, et al. Red deer algorithm-based selective harmonic elimination technique for multilevel inverters. Bull Electr Eng Inform. 2023;12(5):2643–2650.
  • Shi Y, Eberhart R. A modified particle swarm optimizer. 1998 IEEE Int Conf Evol Comput Proc IEEE World Congr Comput Intell Cat No98TH8360 [Internet]. 1998 [cited 2025 Feb 23]. p. 69–73. Available from: https://ieeexplore.ieee.org/document/699146.
  • Shi Y, Eberhart RC. Empirical study of particle swarm optimization. Proc 1999 Congr Evol Comput-CEC99 Cat No 99TH8406 [Internet]. 1999 [cited 2025 Feb 23]. p. 1945-1950 Vol. 3. Available from: https://ieeexplore.ieee.org/document/785511.
  • Eberhart RC, Shi Y. Tracking and optimizing dynamic systems with particle swarms. Proc 2001 Congr Evol Comput IEEE Cat No01TH8546 [Internet]. 2001 [cited 2025 Feb 23]. p. 94–100 vol. 1. Available from: https://ieeexplore.ieee.org/document/934376.
  • Feng Y, Teng G-F, Wang A-X, et al. Chaotic Inertia Weight in Particle Swarm Optimization. Second Int Conf Innov Comput Informatio Control ICICIC 2007 [Internet]. 2007 [cited 2025 Feb 23]. p. 475–475. Available from: https://ieeexplore.ieee.org/document/4428117.
  • Qin Z, Yu F, Shi Z, et al. Adaptive Inertia Weight Particle Swarm Optimization. In: Rutkowski L, Tadeusiewicz R, Zadeh LA, et al., editors. Artif Intell Soft Comput – ICAISC 2006. Berlin, Heidelberg: Springer; 2006. p. 450–459.
  • Arumugam MS, Rao MVC. On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems. Discrete Dyn Nat Soc. 2006;2006(1):079295.
  • Yang X, Yuan J, Yuan J, et al. A modified particle swarm optimizer with dynamic adaptation. Appl Math Comput. 2007;189(2):1205–1213.
  • Jiao B, Lian Z, Gu X. A dynamic inertia weight particle swarm optimization algorithm. Chaos Solitons Fractals. 2008;37(3):698–705.
  • Ratnaweera A, Halgamuge SK, Watson HC. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput. 2004;8(3):240–255.
  • Panigrahi BK, Ravikumar Pandi V, Das S. Adaptive particle swarm optimization approach for static and dynamic economic load dispatch. Energy Convers Manag. 2008;49(6):1407–1415.
  • Suresh K, Ghosh S, Kundu D, et al. Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search. 2008 Eighth Int Conf Intell Syst Des Appl [Internet]. 2008 [cited 2025 Mar 17]. p. 253–258. Available from: https://ieeexplore.ieee.org/document/4696340.

Performance Evaluation Of PSO Variants For Selective Harmonic Elimination In Multi-Level Inverters

Yıl 2025, Cilt: 12 Sayı: 25, 93 - 112, 30.04.2025

Hüseyin Doğan

https://doi.org/10.54365/adyumbd.1662482

Öz

In this study, the performance of different Particle Swarm Optimization (PSO) variants in solving the Selective Harmonic Elimination (SHE) equations of a Cascaded H-Bridge Multilevel Inverter (CHB-MLI) was compared. The SHE method consists of nonlinear transcendental equations, which are particularly difficult to solve analytically in high-level systems. The aim of the study is to examine the overall improvements provided by the PSO variants and to specifically evaluate their effectiveness in solving such complex engineering problems. In addition to the standard PSO, several improved versions from the literature have been considered. For each variant, the optimal switching angles that solve the SHE equations were determined and applied to a CHB-MLI model in the MATLAB/Simulink environment. The convergence behaviors of the algorithms, the total harmonic distortions (THD) and the amplitudes of the fundamental components of the output voltages were analyzed statistically. As a result of these analyses, the strengths and weaknesses of the PSO variants in the optimization processes were revealed. Based on the findings, a hybrid model was also proposed, which integrates the strong features of the successful variants. It was observed that the proposed hybrid model stands out from the other variants by exhibiting a stable and competitive performance even in the worst-case scenarios. These findings indicate that effectively developed PSO variants can be a powerful alternative for solving real-world optimization problems.

Anahtar Kelimeler

PSO SHE CHB-MLI Optimization PSO Variant Multilevel Inverter

Kaynakça

  • Eberhart R, Kennedy J. A new optimizer using particle swarm theory. MHS95 Proc Sixth Int Symp Micro Mach Hum Sci [Internet]. 1995 [cited 2025 Mar 17]. p. 39–43. Available from: https://ieeexplore.ieee.org/document/494215.
  • Gad AG. Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. Arch Comput Methods Eng. 2022;29(5):2531–2561.
  • Xu H, Deng Q, Zhang Z, et al. A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies. Sci Rep. 2025;15(1):4518.
  • Shaqarin T, Noack BR. A Fast-Converging Particle Swarm Optimization through Targeted, Position-Mutated, Elitism (PSO-TPME). Int J Comput Intell Syst. 2023;16(1):6.
  • Jain M, Saihjpal V, Singh N, et al. An Overview of Variants and Advancements of PSO Algorithm. Appl Sci. 2022;12(17):8392.
  • Zhan Z-H, Zhang J, Li Y, et al. Adaptive Particle Swarm Optimization. IEEE Trans Syst Man Cybern Part B Cybern. 2009;39(6):1362–1381.
  • Chauhan P, Deep K, Pant M. Novel inertia weight strategies for particle swarm optimization. Memetic Comput. 2013;5(3):229–251.
  • Fan S-KS, Chiu Y-Y. A decreasing inertia weight particle swarm optimizer. Eng Optim. 2007;39(2):203–228.
  • Feng Y, Yao Y-M, Wang A-X. Comparing with Chaotic Inertia Weights in Particle Swarm Optimization. 2007 Int Conf Mach Learn Cybern [Internet]. 2007 [cited 2025 Feb 23]. p. 329–333. Available from: https://ieeexplore.ieee.org/abstract/document/4370164.
  • Yang K, Zhang Q, Zhang J, et al. Unified Selective Harmonic Elimination for Multilevel Converters. IEEE Trans Power Electron. 2017;32(2):1579–1590.
  • Dahidah MSA, Konstantinou G, Agelidis VG. A Review of Multilevel Selective Harmonic Elimination PWM: Formulations, Solving Algorithms, Implementation and Applications. IEEE Trans Power Electron. 2015;30(8):4091–4106.
  • Yang K, Yuan Z, Yuan R, et al. A Groebner Bases Theory-Based Method for Selective Harmonic Elimination. IEEE Trans Power Electron. 2015;30(12):6581–6592.
  • Bertin T, Despesse G, Thomas R. Comparison between a Cascaded H-Bridge and a Conventional H-Bridge for a 5-kW Grid-Tied Solar Inverter. Electronics. 2023;12(8):1929.
  • Mao W, Zhang X, Hu Y, et al. A Research on Cascaded H-Bridge Module Level Photovoltaic Inverter Based on a Switching Modulation Strategy. Energies. 2019;12(10):1851.
  • Lingom PM, Song-Manguelle J, Nyobe-Yome JM, et al. A Comprehensive Review of Compensation Control Techniques Suitable for Cascaded H-Bridge Multilevel Inverter Operation with Unequal DC Sources or Faulty Cells. Energies. 2024;17(3):722.
  • Sharma B, Manna S, Saxena V, et al. A comprehensive review of multi-level inverters, modulation, and control for grid-interfaced solar PV systems. Sci Rep. 2025;15(1):661.
  • Memon MA, Mekhilef S, Mubin M, et al. Selective harmonic elimination in inverters using bio-inspired intelligent algorithms for renewable energy conversion applications: A review. Renew Sustain Energy Rev. 2018;82:2235–2253.
  • Zhang H, Li D. Applications of computer vision techniques to cotton foreign matter inspection: A review. Comput Electron Agric. 2014;109:59–70.
  • Bektaş Y, Karaca H, Taha TA, et al. Red deer algorithm-based selective harmonic elimination technique for multilevel inverters. Bull Electr Eng Inform. 2023;12(5):2643–2650.
  • Shi Y, Eberhart R. A modified particle swarm optimizer. 1998 IEEE Int Conf Evol Comput Proc IEEE World Congr Comput Intell Cat No98TH8360 [Internet]. 1998 [cited 2025 Feb 23]. p. 69–73. Available from: https://ieeexplore.ieee.org/document/699146.
  • Shi Y, Eberhart RC. Empirical study of particle swarm optimization. Proc 1999 Congr Evol Comput-CEC99 Cat No 99TH8406 [Internet]. 1999 [cited 2025 Feb 23]. p. 1945-1950 Vol. 3. Available from: https://ieeexplore.ieee.org/document/785511.
  • Eberhart RC, Shi Y. Tracking and optimizing dynamic systems with particle swarms. Proc 2001 Congr Evol Comput IEEE Cat No01TH8546 [Internet]. 2001 [cited 2025 Feb 23]. p. 94–100 vol. 1. Available from: https://ieeexplore.ieee.org/document/934376.
  • Feng Y, Teng G-F, Wang A-X, et al. Chaotic Inertia Weight in Particle Swarm Optimization. Second Int Conf Innov Comput Informatio Control ICICIC 2007 [Internet]. 2007 [cited 2025 Feb 23]. p. 475–475. Available from: https://ieeexplore.ieee.org/document/4428117.
  • Qin Z, Yu F, Shi Z, et al. Adaptive Inertia Weight Particle Swarm Optimization. In: Rutkowski L, Tadeusiewicz R, Zadeh LA, et al., editors. Artif Intell Soft Comput – ICAISC 2006. Berlin, Heidelberg: Springer; 2006. p. 450–459.
  • Arumugam MS, Rao MVC. On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems. Discrete Dyn Nat Soc. 2006;2006(1):079295.
  • Yang X, Yuan J, Yuan J, et al. A modified particle swarm optimizer with dynamic adaptation. Appl Math Comput. 2007;189(2):1205–1213.
  • Jiao B, Lian Z, Gu X. A dynamic inertia weight particle swarm optimization algorithm. Chaos Solitons Fractals. 2008;37(3):698–705.
  • Ratnaweera A, Halgamuge SK, Watson HC. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput. 2004;8(3):240–255.
  • Panigrahi BK, Ravikumar Pandi V, Das S. Adaptive particle swarm optimization approach for static and dynamic economic load dispatch. Energy Convers Manag. 2008;49(6):1407–1415.
  • Suresh K, Ghosh S, Kundu D, et al. Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search. 2008 Eighth Int Conf Intell Syst Des Appl [Internet]. 2008 [cited 2025 Mar 17]. p. 253–258. Available from: https://ieeexplore.ieee.org/document/4696340.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Hüseyin Doğan 0000-0002-9609-7825

Erken Görünüm Tarihi 26 Nisan 2025
Yayımlanma Tarihi 30 Nisan 2025
Gönderilme Tarihi 21 Mart 2025
Kabul Tarihi 11 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 12 Sayı: 25

Kaynak Göster

APA Doğan, H. (2025). Performance Evaluation Of PSO Variants For Selective Harmonic Elimination In Multi-Level Inverters. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 12(25), 93-112. https://doi.org/10.54365/adyumbd.1662482
AMA Doğan H. Performance Evaluation Of PSO Variants For Selective Harmonic Elimination In Multi-Level Inverters. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. Nisan 2025;12(25):93-112. doi:10.54365/adyumbd.1662482
Chicago Doğan, Hüseyin. “Performance Evaluation Of PSO Variants For Selective Harmonic Elimination In Multi-Level Inverters”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 12, sy. 25 (Nisan 2025): 93-112. https://doi.org/10.54365/adyumbd.1662482.
EndNote Doğan H (01 Nisan 2025) Performance Evaluation Of PSO Variants For Selective Harmonic Elimination In Multi-Level Inverters. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 12 25 93–112.
IEEE H. Doğan, “Performance Evaluation Of PSO Variants For Selective Harmonic Elimination In Multi-Level Inverters”, Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, c. 12, sy. 25, ss. 93–112, 2025, doi: 10.54365/adyumbd.1662482.
ISNAD Doğan, Hüseyin. “Performance Evaluation Of PSO Variants For Selective Harmonic Elimination In Multi-Level Inverters”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 12/25 (Nisan 2025), 93-112. https://doi.org/10.54365/adyumbd.1662482.
JAMA Doğan H. Performance Evaluation Of PSO Variants For Selective Harmonic Elimination In Multi-Level Inverters. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2025;12:93–112.
MLA Doğan, Hüseyin. “Performance Evaluation Of PSO Variants For Selective Harmonic Elimination In Multi-Level Inverters”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, c. 12, sy. 25, 2025, ss. 93-112, doi:10.54365/adyumbd.1662482.
Vancouver Doğan H. Performance Evaluation Of PSO Variants For Selective Harmonic Elimination In Multi-Level Inverters. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2025;12(25):93-112.
 Kapak Resmi İndir