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Journals / Türk Doğa ve Fen Dergisi / 2026 / Cilt: 15 Sayı: 3

COMPARATIVE MATLAB-BASED ANALYSIS OF PI, PID, AI-PI AND AI-PID CONTROLLERS FOR INVERTER DYNAMIC PERFORMANCE UNDER EV PENETRATION VARIATIONS

EV Penetrasyon Değişimleri Altında İnverter Dinamik Performansı İçin PI, PID, AI-PI ve AI-PID Kontrolörlerinin Karşılaştırmalı MATLAB Tabanlı Analizi

Ozan Gül(BİNGÖL ÜNİVERSİTESİ)0000-0002-1724-2992
Pages
231–242
Publication Date
2026-09-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Simulation results show that increasing EV penetration significantly degrades inverter dynamic stability.

📌 3 Key Takeaways

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Objective & Hypothesis:The increasing penetration of electric vehicles (EVs) introduces important dynamic stability challenges to inverter-based smart grid systems.
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Key Findings & Data:Rapid EV charging demand variations reduce system damping and increase oscillatory behavior, negatively affecting transient performance.
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Conclusion & Impact:Simulation results show that increasing EV penetration significantly degrades inverter dynamic stability.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Elektrikli araçlar · AI-PID kontrolü · inverter kararlılığı

Abstract

The increasing penetration of electric vehicles (EVs) introduces important dynamic stability challenges to inverter-based smart grid systems. Rapid EV charging demand variations reduce system damping and increase oscillatory behavior, negatively affecting transient performance. This study presents a comparative MATLAB-based analysis of PI, PID, AI-PI, and AI-PID controllers under different EV penetration levels. The controllers are evaluated using overshoot, settling time, rise time, disturbance rejection, Integral Squared Error (ISE), Integral Time Absolute Error (ITAE), and control effort metrics. Simulation results show that increasing EV penetration significantly degrades inverter dynamic stability. Although the AI-PI controller improves transient response speed, it produces excessive overshoot under high EV loading conditions. The proposed AI-PID controller achieves the best overall performance by reducing overshoot, improving disturbance rejection capability, and minimizing error indices. The results demonstrate that AI-assisted adaptive PID control can significantly improve inverter robustness and transient stability in EV-integrated smart grids.

Özet

Elektrikli araç (EV) penetrasyonunun artması, inverter tabanlı akıllı şebeke sistemlerinde önemli dinamik kararlılık problemleri oluşturmaktadır. Hızlı EV şarj talebi değişimleri sistem sönümünü azaltmakta ve osilasyon davranışını artırarak geçici rejim performansını olumsuz etkilemektedir. Bu çalışmada farklı EV penetrasyon seviyeleri altında PI, PID, AI-PI ve AI-PID kontrolörlerinin karşılaştırmalı MATLAB tabanlı analizi sunulmuştur. Kontrolörler aşım, yerleşme zamanı, yükselme zamanı, bozucu etki bastırma yeteneği, Integral Squared Error (ISE), Integral Time Absolute Error (ITAE) ve kontrol eforu kriterleri kullanılarak değerlendirilmiştir. Benzetim sonuçları, EV penetrasyonu arttıkça inverter dinamik kararlılığının önemli ölçüde bozulduğunu göstermektedir. AI-PI kontrolörü geçici rejim hızını artırmasına rağmen yüksek EV yük koşullarında aşırı aşım üretmektedir. Önerilen AI-PID kontrolörü ise aşımı azaltarak, bozucu etki bastırma performansını geliştirerek ve hata indislerini minimize ederek en iyi genel performansı sağlamıştır. Sonuçlar, yapay zekâ destekli adaptif PID kontrolünün EV entegreli akıllı şebekelerde inverter dayanıklılığını ve geçici rejim kararlılığını önemli ölçüde artırabileceğini göstermektedir.

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Cite This Article

Gül, O. (2026). COMPARATIVE MATLAB-BASED ANALYSIS OF PI, PID, AI-PI AND AI-PID CONTROLLERS FOR INVERTER DYNAMIC PERFORMANCE UNDER EV PENETRATION VARIATIONS. Türk Doğa ve Fen Dergisi, Cilt: 15 Sayı: 3, 231–242. https://doi.org/10.46810/tdfd.1964713

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References (21)

  1. Blaabjerg F, Teodorescu R, Liserre M, Timbus AV. Overview of control and grid synchronization for distributed power generation systems. IEEE Trans Ind Electron. 2006;53(5):1398-409.
  2. Guerrero JM, Vasquez JC, Matas J, de Vicuña LG, Castilla M. Hierarchical control of droop-controlled AC and DC microgrids—A general approach toward standardization. IEEE Trans Ind Electron. 2011;58(1):158-72.
  3. Yazdanian M, Mehrizi-Sani A. Distributed control techniques in microgrids. IEEE Trans Smart Grid. 2014;5(6):2901-9.
  4. Golestan S, Guerrero JM, Vasquez JC. Three-phase PLLs: A review of recent advances. IEEE Trans Power Electron. 2017;32(3):1894-907.
  5. Deilami S, Masoum AS, Moses PS, Masoum MAS. Real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile. IEEE Trans Smart Grid. 2011;2(3):456-67.
  6. Clement-Nyns K, Haesen E, Driesen J. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Trans Power Syst. 2010;25(1):371-80.
  7. Richardson P, Flynn D, Keane A. Impact assessment of varying penetrations of electric vehicles on low voltage distribution systems. In: 2010 IEEE Power and Energy Society General Meeting; 2010 Jul 25-29; Minneapolis, MN, USA. IEEE; 2010.
  8. Sortomme E, Hindi MM, MacPherson SDJ, Venkata SS. Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses. IEEE Trans Smart Grid. 2011;2(1):198-205.
  9. Dubey A, Santoso S. Electric vehicle charging on residential distribution systems: Impacts and mitigations. IEEE Access. 2015; 3:1871-93.
  10. Yilmaz M, Krein PT. Review of battery charger topologies, charging power levels, and infrastructure for plug-in electric and hybrid vehicles. IEEE Trans Power Electron. 2013;28(5):2151-69.
  11. Kempton W, Tomic J. Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy. J Power Sources. 2005;144(1):280-94.
  12. Åström KJ, Hägglund T. PID controllers: Theory, design, and tuning. 2nd ed. Research Triangle Park (NC): ISA; 1995.
  13. Ang KH, Chong G, Li Y. PID control system analysis, design, and technology. IEEE Trans Control Syst Technol. 2005;13(4):559-76.
  14. Li Y, Ang KH, Chong GCY. PID control system analysis and design. IEEE Control Syst Mag. 2006;26(1):32-41.
  15. Bose BK. Neural network applications in power electronics and motor drives—An introduction and perspective. IEEE Trans Ind Electron. 2007;54(1):14-33.
  16. Zhao S, Blaabjerg F, Wang H. An overview of artificial intelligence applications for power electronics. IEEE Trans Power Electron. 2021;36(4):4633-58.
  17. Babu NP. Adaptive grid-connected inverter control schemes for power quality enrichment in microgrid systems: Past, present, and future perspectives. Electr Power Syst Res. 2024; 230:110288.
  18. Wang Q, Li G, Chen Z, Jing Z, Zhang Z. Adaptive control strategy for microgrid inverters based on Narendra model. Sci Rep. 2024; 14:21389.
  19. Nur A, Güre B, Rüstemli S, Bezek Güre Ö. Wind speed prediction in microgrid energy management using the random forest and artificial neural networks methods. Middle East J Sci. 2025;11(2):276-89.
  20. Polat O, Coskun M. Physics-guided deep residual learning for high-accuracy reconstruction of PV plant AC power from DC string electrical measurements. Savunma Bilimleri Dergisi. 2026; Advanced Online Publication.
  21. Yılmaz G. Comparative analysis of FOPID-based electric vehicle speed control using heuristic optimization algorithms. Türk Doğa ve Fen Dergisi. 2025;14(4):209-21.