Comparison of Oscillation Optimization Strategies for MMC-HVDC Based on Meta-heuristic Algorithms

Shuaijun Zhao, Yanbo Che, Yijing Chen

Abstract


This paper elucidates the oscillation issues in offshore wind farms employing modular multilevel converter -high-voltage direct current (MMC-HVDC) transmission systems. Initially, a small-signal model was constructed, integrating sub-module capacitor triple harmonic components and control link delays. Oscillation modes and their origins were elucidated through the analysis of participation factors and eigenvalues, facilitating the selection of controller parameters. Subsequently, the DBO(dung beetle optimization) algorithm was subsequently augmented with chaotic mapping, spiral search strategies, Levy flights, and t-distribution-based perturbation strategies, collectively termed the TDBO algorithm. A strategy for modulating controller parameters to reduce oscillation risks in the flexible direct current system was proposed. Finally, Comparative performance analysis of several algorithms was conducted using benchmark test functions and the small-signal model, culminating in the identification of the most efficacious algorithm for addressing this specific engineering challenge.


Keywords


MMC-HVDC system; small-signal model; controller parameter optimization; meta-heuristic algorithms

Full Text:

PDF

References


R. Hong, Z. Yuebin, and L. Weiwei, “The engineering application and development prospect of VSC-HVDC transmission technology [J]”, Automation of Electric Power Systems, vol. 47, no. 1, pp. 1-11, 2023.

M. Kurto?lu, F. Ero?lu, A. O. Arslan, and A. M. Vural, “Recent contributions and future prospects of the modular multilevel converters: A comprehensive review”, International Transactions on Electrical Energy Systems, vol. 29, no. 3, pp. e2763, 2019.

F. Martinez-Rodrigo, D. Ramirez, A. B. Rey-Boue, S. De Pablo, and L. C. Herrero-de Lucas, “Modular multilevel converters: Control and applications”, Energies, vol. 10, no. 11, pp. 1709, 2017.

C. Yin, X. Xie, H. Liu, X. Wang, Z. Wang, and Y. Chi, “Analysis and control of the oscillation phenomenon in VSC-HVDC transmission system”, Power System Technology, vol. 42, no. 4, pp. 1117-1123, 2018.

J. Sun, I. Vieto, E. V. Larsen, and C. Buchhagen, "Impedance-based characterization of digital control delay and its effects on system stability", 2019 20th Workshop on Control and Modeling for Power Electronics (COMPEL). pp. 1-8, 2019.

Y. Miao, X. Zhang, H. Peng, L. Wang, and Q. Guo, "Modeling and Oscillation Mechanism Analysis of Renewable Energy Base Connected into MMC-HVDC", 2023 3rd Power System and Green Energy Conference (PSGEC). pp. 203-209, 2023.

Q. Jiang, Y. Tao, B. Li, T. Liu, Z. Chen, F. Blaabjerg, and P. Wang, “Joint Limiting Control Strategy Based on Virtual Impedance Shaping for Suppressing DC Fault Current and Arm Current in MMC-HVDC System”, Journal of Modern Power Systems and Clean Energy, vol. 11, no. 6, pp. 2003-2014, 2023.

B. Hu, H. Nian, M. Li, Y. Liao, J. Yang, and H. Tong, “Impedance characteristic analysis and stability improvement method for DFIG system within PLL bandwidth based on different reference frames”, IEEE Transactions on Industrial Electronics, vol. 70, no. 1, pp. 532-543, 2022.

Y. Meng, F. Jia, K. Wu, Z. Duan, X. Wang, Y. Yang, and X. Wang, “Structure optimization based on phase?locked loop and controller parameters optimization of Y?connected modular multi?level converter for fractional frequency offshore wind power system under weak grid”, IET Generation, Transmission & Distribution, vol. 16, no. 18, pp. 3605-3616, 2022.

J. B. Soomro, F. Akhtar, R. Hussain, J. A. Ansari, and H. M. Munir, “A Detailed Review of MMC Circuit Topologies and Modelling Issues”, International Transactions on Electrical Energy Systems, DOI: 10.1155/2022/8734010 , vol. 2022, Mar 4, 2022.

Z. Zhang, Y. Liang, and X. Zhao, “Adaptive inter-area power oscillation damping from offshore wind farm and MMC-HVDC using deep reinforcement learning”, Renewable Energy, pp. 120164, 2024.

H. Li, H. Nian, Y. Liu, B. Hu, Y. Liao, and M. Li, “An improved quantitative analysis method of oscillation mode for DFIG-based wind power base with LCC-HVDC transmission considering frequency coupling characteristic”, IEEE Access, vol. 11, pp. 31143-31156, 2023.

M. Arshad, O. Beik, R. Pallapati, and S. Hoberg, “Overview and Impedance-Based Stability Analyses of Bison Wind Farm: A PRACTICAL EXAMPLE”, Ieee Industry Applications Magazine, DOI: 10.1109/MIAS.2023.3345835, 2024 Jan 17, 2024.

M. A. Hannan, N. N. Islam, A. Mohamed, M. S. H. Lipu, P. J. Ker, M. M. Rashid, and H. Shareef, “Artificial intelligent based damping controller optimization for the multi-machine power system: A review”, IEEE Access, vol. 6, pp. 39574-39594, 2018.

S. Bhongade, D. Patel, A. Singh, and R. Mandloi, “Optimizing Load Frequency Control of Micro-grid using Black Widow Optimization Algorithm”, International Journal of Smart Grid-ijSmartGrid, vol. 8, no. 1, pp. 53-62, 2024.

F. Tooryan, E. R. Collins, A. Ahmadi, and S. S. Rangarajan, "Distributed generators optimal sizing and placement in a microgrid using PSO", 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA). pp. 614-619, 2017.

A. Bouakkaz, S. Haddad, J. A. Martín-García, A. J. Gil-Mena, and R. Jiménez-Castañeda, “Optimal scheduling of household appliances in off-grid hybrid energy system using PSO algorithm for energy saving”, International Journal of Renewable Energy Research (IJRER), vol. 9, no. 1, pp. 427-436, 2019.

A. Ndiaye, and M. Faye, "Experimental Validation of PSO and Neuro-Fuzzy Soft-Computing Methods for Power Optimization of PV installations", 2020 8th International Conference on Smart Grid (icSmartGrid). pp. 189-197, 2020.

T. Bahi, and A. Lakhdara, “Analysis of Genetic and Cuckoo Search Algorithms for MPPT in Partial Shaded”, International Journal of Smart Grid-ijSmartGrid, vol. 8, no. 1, pp. 35-40, 2024.

A. I. Nusaif, and A. L. Mahmood, “MPPT algorithms (PSO, FA, and MFA) for PV system under partial shading condition, case study: BTS in Algazalia, Baghdad”, International Journal of Smart Grid-ijSmartGrid, vol. 4, no. 3, pp. 100-110, 2020.

A. Mamizadeh, N. Genc, and R. Rajabioun, "Optimal tuning of pi controller for boost dc-dc converters based on cuckoo optimization algorithm", 2018 7th international conference on renewable energy research and applications (ICRERA). pp. 677-680, 2018.

T. Jayabarathi, T. Raghunathan, N. Mithulananthan, S. Cherukuri, and G. L. Sai, “Enhancement of distribution system performance with reconfiguration, distributed generation and capacitor bank deployment”, Heliyon, 2024.

Y. Zhu, Y. Guo, T. Hu, C. Wu, and L. Zhang, “Wind Farm Layout Optimization Based on Dynamic Opposite Learning-Enhanced Sparrow Search Algorithm”, International Journal of Energy Research, vol. 2024, 2024.

M. Mezaache, B. Babes, and S. Chaouch, “Optimization of welding input parameters using PSO technique for minimizing HAZ width in GMAW”, Periodica Polytechnica Mechanical Engineering, vol. 66, no. 2, pp. 99-108, 2022.

N. Hamouda, B. Babes, S. Kahla, A. Boutaghane, A. Beddar, and O. Aissa, "ANFIS controller design using PSO algorithm for MPPT of solar PV system powered brushless DC motor based wire feeder unit", 2020 International Conference on Electrical Engineering (ICEE). pp. 1-6, 2020.

B. Babes, A. Boutaghane, N. Hamouda, S. Kahla, A. Kellai, T. Ellinger, and J. Petzoldt, “New Optimal Control of Permanent Magnet DC Motor for Photovoltaic Wire Feeder Systems”, Journal Européen des Systèmes Automatisés, vol. 53, no. 6, 2020.

N. Hamouda, B. Babes, S. Kahla, C. HAMOUDA, and A. Boutaghane, “Particle Swarm Optimization of Fuzzy Fractional PDµ+ I Controllerof a PMDC Motor for Reliable Operation of Wire-Feeder Units ofGMAW Welding Machine”, Przegl?d Elektrotechniczny, vol. 48, no. 12, pp. 40-46, 2020.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer”, Advances in engineering software, vol. 69, pp. 46-61, 2014.

S. Mirjalili, and A. Lewis, “The whale optimization algorithm”, Advances in engineering software, vol. 95, pp. 51-67, 2016.

J. Xue, and B. Shen, “Dung beetle optimizer: A new meta-heuristic algorithm for global optimization”, The Journal of Supercomputing, vol. 79, no. 7, pp. 7305-7336, 2023.

D. Zhang, C. Zhang, X. Han, and C. Wang, “Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve”, Measurement Science and Technology, DOI: 10.1088/1361-6501/ad3be0, vol. 35, no. 7, Jul 1, 2024.

J. He, and L.-h. Fu, “Robot path planning based on improved dung beetle optimizer algorithm”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 46, no. 4, Apr, 2024.

D. H. Wolpert, “The lack of a priori distinctions between learning algorithms”, Neural computation, vol. 8, no. 7, pp. 1341-1390, 1996.

M. Dehghani, Š. Hubálovský, and P. Trojovský, “Northern goshawk optimization: a new swarm-based algorithm for solving optimization problems”, Ieee Access, vol. 9, pp. 162059-162080, 2021.

H. A. A. Eldawy, W. El-Shafai, E. E.-D. Hemdan, G. M. El-Banby, and F. E. A. El-Samie, “A robust cancellable face and palmprint recognition system based on 3D optical chaos-DNA cryptosystem”, Optical and Quantum Electronics, vol. 55, no. 11, pp. 970, 2023.

C. Guo, J. Zhang, S. Yang, and N. Lv, “Impact of Time Delay on the Control Link in Small Signal Dynamics of LCC-HVDC System”, IEEE Transactions on Power Delivery, DOI: 10.1109/TPWRD.2023.3272673, vol. 38, no. 5, pp. 3342-3355, 2023.




DOI (PDF): https://doi.org/10.20508/ijrer.v16i3.15424.g9229

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is indexed in EI Compendex, SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics)and CrossRef.

IJRER has been indexed in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE in 2026; 

h=38,

Average citation per item=7.69

Last three Years Impact Factor=(1768+1782+2085)/(146+78+80)=5635/304=18.53

Category Quartile:Q4