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Optimal wind power generation system by honey badger algorithm with differential evolution strategies

Published: 01 February 2024 Publication History

Abstract

The purpose of this study is to build an optimal hybrid wind power system consisting of a permanent magnet direct-drive wind power generation unit, a hybrid energy storage system (HESS), a power electronic converter, and loads. Moreover, a reasonable control method is designed for each part, and a honey badger algorithm (HBA) with differential evolution strategies is employed to realize the coordinated control of each unit and improve the system stability. The real anti-interference capability of wind power system can be improved by the proposed HBA with differential evolution strategies (IHBA). Firstly, the wind hybrid system model is constructed, in which the wind power generation unit adopts the variable step climbing method to achieve the maximum power point tracking control; the HESS designs the power distribution method based on the bus voltage; the converter supplying energy to the AC load introduces the virtual synchronous generator (VSG) control strategy. Secondly, due to the existence of energy exchange in each unit of the system, VSG has more parameters and its control performance is influenced by the system itself, this study proposes HBA with differential evolution strategies for system parameter optimization and constructs an IHBA-VSG model with the objective of minimizing the bus voltage fluctuation value. The IHBA improves original honey badger algorithm by three mechanisms which are time control function, Gaussian variation factor and differential evolution strategy. Finally, the parameters obtained from the HBA with differential evolution strategies optimization search are substituted into the wind hybrid system, and the simulation system is built by MATLAB/Simulink. The simulation results show that when the environment of the system changes or the load on the AC side changes abruptly, the proposed control method can reduce the bus voltage fluctuation by 2.9%− 18.22% compared with the honey badger algorithm Optimized VSG (HBA-VSG), and can reduce the bus voltage fluctuation by 5.09%− 17.98% compared with the traditional droop control without considering the system interaction. This study can effectively improve the stability of wind power generation system and promote the development of new energy industry.

Highlights

Bus voltage-based distribution strategy is used for HESS.
The inverter is controlled by VSG technology.
HBA with differential evolution strategies algorithm is developed.
The proposed algorithm is used to search the optimal value of hybrid system.

References

[1]
J.M. Aberilla, A. Gallego-Schmid, L. Stamford, A. Azapagic, Design and environmental sustainability assessment of small-scale off-grid energy systems for remote rural communities, Appl. Energy 258 (2020).
[2]
A. Agarwal, A. Chandra, S. Shalivahan, R.K. Singh, Grey wolf optimizer: a new strategy to invert geophysical data sets, Geophys. Prospect. 66 (6) (2018) 1215–1226.
[3]
F. Al Thobiani, S. Khatir, B. Benaissa, E. Ghandourah, S. Mirjalili, M.A. Wahab, A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification, Theor. Appl. Fract. Mech. 118 (2022).
[4]
E. Belge, A. Altan, R. Hacolu, Metaheuristic optimization-based path planning and tracking of quadcopter for payload hold-release mission, Electronics 11 (8) (2022).
[5]
J. Brest, S. Greiner, B. Boskovic, M. Mernik, V. Zumer, Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems, IEEE Trans. Evolut. Comput. 10 (6) (2006) 646–657.
[6]
J.R. Chen, T. O'Donnell, Parameter constraints for virtual synchronous generator considering stability, IEEE Trans. Power Syst. 34 (3) (2019) 2479–2481.
[7]
M. Chen, J. Zhang, Research on control strategy of battery-supercapacitor hybrid energy storage system based on droop control, Int. J. Low. -Carbon Technol. 16 (4) (2021) 1377–1383.
[8]
S. Choudhury, N. Khandelwal, A novel weighted superposition attraction algorithm-based optimization approach for state of charge and power management of an islanded system with battery and supercapacitor-based hybrid energy storage system, Iete J. Res. (2020).
[9]
W. Deng, J. Xu, An enhanced msiqde algorithm with novel multiple strategies for global optimization problems, IEEE Trans. Syst. Man Cybern. - Syst. 52 (3) (2022) 1578–1587.
[10]
L.A.G. Gomez, A.P. Grilo, M.B.C. Salles, A.J. Sguarezi, Combined control of DFIG-based wind turbine and battery energy storage system for frequency response in microgrids, Energies 13 (4) (2020).
[11]
F.A. Hashim, K. Hussain, E.H. Houssein, M.S. Mabrouk, W. Al-Atabany, Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems, Appl. Intell. 51 (3) (2021) 1531–1551.
[12]
F.A. Hashim, E.H. Houssein, K. Hussain, M.S. Mabrouk, W. Al-Atabany, Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems, Math. Comput. Simul. 192 (2022) 84–110.
[13]
W. Jiang, L. Zhang, H. Zhao, H.C. Huang, R.J. Hu, Research on power sharing strategy of hybrid energy storage system in photovoltaic power station based on multi-objective optimisation, IET Renew. Power Gener. 10 (5) (2016) 575–583.
[14]
S. Koohi-Fayegh, M.A. Rosen, A review of energy storage types, applications and recent developments, J. Energy Storage 27 (2020).
[15]
J. Li, B.Y. Wen, H.Y. Wang, Adaptive virtual inertia control strategy of VSG for micro-grid based on improved bang-bang control strategy, IEEE Access 7 (2019) 39509–39514.
[16]
L.L. Li, H.Y. Li, M.L. Tseng, H. Feng, A.S.F. Chiu, Renewable energy system on frequency stability control strategy using virtual synchronous generator, Symmetry-Basel 12 (10) (2020).
[17]
L.L. Li, Z.F. Liu, M.L. Tseng, S.J. Zheng, K.L. Ming, Improved tunicate swarm algorithm: solving the dynamic economic emission dispatch problems, Appl. Soft Comput. 108 (5) (2021).
[18]
S.Q. Li, M.Y. Cao, J. Li, J.F. Cao, Z.W. Lin, Sensorless-based active disturbance rejection control for a wind energy conversion system with permanent magnet synchronous generator, IEEE Access 7 (2019) 122663–122674.
[19]
Y. Lin, A semi-consensus strategy toward multi-functional hybrid energy storage system in dc microgrids, Trends Ecol. Evol. 35 (1) (2020) 336–346.
[20]
J. Liu, Y. Miura, T. Ise, Fixed-parameter damping methods of virtual synchronous generator control using state feedback, IEEE Access 7 (2019) 99177–99190.
[21]
X.Y. Lu, Y.J. Chen, M.F. Fu, H.Y. Wang, Multi-objective optimization-based real-time control strategy for battery/ultracapacitor hybrid energy management systems, IEEE Access 7 (2019) 11640–11650.
[22]
T. Ma, M.S. Javed, Integrated sizing of hybrid PV-wind-battery system for remote island considering the saturation of each renewable energy resource, Energy Convers. Manag. 182 (2019) 178–190.
[23]
Y.J. Ma, X. Yang, X.S. Zhou, L.Y. Yang, Y.L. Zhou, Dual closed-loop linear active disturbance rejection control of grid-side converter of permanent magnet direct-drive wind turbine, Energies 13 (5) (2020).
[24]
U. Markovic, Z.D. Chu, P. Aristidou, G. Hug, LQR-based adaptive virtual synchronous machine for power systems with high inverter penetration, IEEE Trans. Sustain. Energy 10 (3) (2019) 1501–1512.
[25]
H. Mehrjerdi, M. Bornapour, R. Hemmati, S.M.S. Ghiasi, Unified energy management and load control in building equipped with wind-solar-battery incorporating electric and hydrogen vehicles under both connected to the grid and islanding modes, Energy 168 (2019) 919–930.
[26]
X. Meng, J.J. Liu, Z. Liu, A generalized droop control for grid-supporting inverter based on comparison between traditional droop control and virtual synchronous generator control, IEEE Trans. Power Electron. 34 (6) (2019) 5416–5438.
[27]
A. Mhq, B. Hmh, A. Sa, Enhanced whale optimization algorithm for maximum power point tracking of variable-speed wind generators, Appl. Soft Comput. (2020) 86.
[28]
S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Softw. 95 (2016) 51–67.
[29]
U.R. Nair, R. Costa-Castello, A model predictive control-based energy management scheme for hybrid storage system in islanded microgrids, IEEE Access 8 (2020) 97809–97822.
[30]
M.K.K. Prince, M.T. Arif, A. Gargoom, A.M.T. Oo, M.E. Haque, Modeling, parameter measurement, and control of PMSG-based grid-connected wind energy conversion system, J. Mod. Power Syst. Energy 9 (5) (2021) 1054–1065.
[31]
H.U. Rehman, X.W. Yan, M.A. Abdelbaky, M.U. Jan, S. Iqbal, An advanced virtual synchronous generator control technique for frequency regulation of grid-connected PV system, Int. J. Electr. Power Energy Syst. (2021) 125.
[32]
S. Saadatmand, P. Shamsi, M. Ferdowsi, Power and frequency regulation of synchronverters using a model free neural network-based predictive controller, IEEE Trans. Ind. Electron. 68 (5) (2021) 3662–3671.
[33]
T. Santhoshkumar, V. Senthilkumar, Transient and small signal stability improvement in microgrid using AWOALO with virtual synchronous generator control scheme, ISA Trans. 104 (2020) 233–244.
[34]
Z.K. Shuai, C. Shen, X. Liu, Z.Y. Li, Z.J. Shen, Transient angle stability of virtual synchronous generators using Lyapunov's direct method, IEEE Trans. Smart Grid 10 (4) (2019) 4648–4661.
[35]
S.K. Singh, R. Singh, H. Ashfaq, R. Kumar, Virtual inertia emulation of inverter interfaced distributed generation (IIDG) for dynamic frequency stability & damping enhancement through BFOA tuned optimal controller, Arab. J. Sci. Eng. 47 (3) (2022) 3293–3310.
[36]
S. Sinha, P. Bajpai, Power management of hybrid energy storage system in a standalone DC microgrid, J. Energy Storage 30 (2020).
[37]
R. Sitharthan, M. Karthikeyan, D.S. Sundar, S. Rajasekaran, Adaptive hybrid intelligent mppt controller to approximate effectual wind speed and optimal rotor speed of variable speed wind turbine, ISA Trans. 96 (2020) 479–489.
[38]
S. Thenpennaisivem, V. Senthilkumar, Improvement of transient and small signal stability in micro grid by hybrid technique with virtual synchronous generator control scheme, Trans. Inst. Meas. Control 43 (12) (2021) 2835–2859.
[39]
K. Wang, W.L. Wang, L.C. Wang, L.W. Li, An improved SOC control strategy for electric vehicle hybrid energy storage systems, Energies 13 (20) (2020).
[40]
L. Wang, Y.J. Wang, C. Liu, D. Yang, Z.H. Chen, A power distribution strategy for hybrid energy storage system using adaptive model predictive control, IEEE Trans. Power Electron. 35 (6) (2020) 5897–5906.
[41]
Y. Wang, C. Wang, L. Xu, J.H. Meng, Y. Hei, Adjustable inertial response from the converter with adaptive droop control in dc grids, IEEE Trans. Smart Grid 10 (3) (2019) 3198–3209.
[42]
Y.J. Wang, L. Wang, M.C. Li, Z.H. Chen, A review of key issues for control and management in battery and ultra-capacitor hybrid energy storage systems, Etransportation 4 (2020).
[43]
Wu, T.Z., Ye, F.C., Su, Y.H., Wang, Y.B., & Riffat, S. (2020). Coordinated control strategy of DC microgrid with hybrid energy storage system to smooth power output fluctuation.
[44]
T.Z. Wu, X. Shi, L. Liao, C.J. Zhou, H. Zhou, Y.H. Su, A capacity configuration control strategy to alleviate power fluctuation of hybrid energy storage system based on improved particle swarm optimization, Energies 12 (4) (2019).
[45]
L. Xiong, P. Li, M. Ma, Z. Wang, J. Wang, Output power quality enhancement of pmsg with fractional order sliding mode control, Int. J. Electr. Power Energy Syst. 115 (2020) 105402.1–105402.15.
[46]
H.Z. Xu, C.Z. Yu, C. Liu, Q.L. Wang, X. Zhang, An improved virtual inertia algorithm of virtual synchronous generator, J. Mod. Power Syst. Clean Energy 8 (2) (2020) 377–386.
[47]
F.J. Yao, J.B. Zhao, X.J. Li, L. Mao, K.Q. Qu, RBF neural network based virtual synchronous generator control with improved frequency stability, IEEE Trans. Ind. Inform. 17 (6) (2021) 4014–4024.
[48]
X. Yao, Y. Liu, G.M. Lin, Evolutionary programming made faster, IEEE Trans. Evolut. Comput. 3 (2) (1999) 82–102.
[49]
L. Zhang, H. Zheng, T. Wan, D.H. Shi, L. Lyu, G.W. Cai, An integrated control algorithm of power distribution for islanded microgrid based on improved virtual synchronous generator, IET Renew. Power Gener. 15 (12) (2021) 2674–2685.
[50]
L. Zhang, X.S. Wang, Z. Zhang, Y. Cui, L. Ling, G.W. Cai, An adaptative control strategy for interfacing converter of hybrid microgrid based on improved virtual synchronous generator, IET Renew. Power Gener. 16 (2) (2022) 261–273.
[51]
L. Zhang, H. Zheng, G.W. Cai, Z. Zhang, X.S. Wang, L.H. Koh, Power-frequency oscillation suppression algorithm for AC microgrid with multiple virtual synchronous generators based on fuzzy inference system, IET Renew. Power Gener. 16 (8) (2022) 1589–1601.
[52]
Y. Zhang, Z. Yan, C.C. Zhou, T.Z. Wu, Y.Y. Wang, Capacity allocation of hess in micro-grid based on ABC algorithm, Int. J. Low. -Carbon Technol. 15 (4) (2020) 496–505.
[53]
R. Zhu, A.L. Zhao, G.C. Wang, X. Xia, Y.P. Yang, An energy storage performance improvement model for grid-connected wind-solar hybrid energy storage system, Comput. Intell. Neurosci. (2020) 2020.
[54]
X.M. Zou, X. Du, H.M. Tai, Stability analysis for direct-drive permanent magnet synchronous generator based wind farm integration system considering wind speed, IET Renew. Power Gener. 14 (11) (2020) 1894–1903.

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Published In

cover image Applied Soft Computing
Applied Soft Computing  Volume 149, Issue PA
Dec 2023
1074 pages

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 February 2024

Author Tags

  1. New energy
  2. Permanent magnet direct-drive wind power generation
  3. Hybrid energy storage system
  4. Virtual synchronous generator
  5. Honey badger algorithm

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