Enhanced salp swarm algorithm: Application to variable speed wind generators

Abstract This article presented a novel modification and application of the salp swarm algorithm (SSA) that is inspired by the chain behavior of salp fishes that live in deep oceans. Firstly, the enhanced salp swarm algorithm (ESSA) is proposed to improve the inadequate results of the SSA compared to the other algorithms, especially for the high dimensional functions. The ESSA algorithm is verified using twenty-three benchmark test functions and compared with the original SSA algorithm and other algorithms. The statistical analysis of the obtained results revealed that the ESSA algorithm is significantly improved and the convergence curves showed the fast convergence to the best solution. Secondly, The SSA and ESSA algorithms are applied to enhance the maximum power point tracking and the fault-ride through ability of a grid-tied permanent magnet synchronous generator driven by a variable speed wind turbine (PMSG-VSWT). The multi-objective function (integral squared error) is minimized to find the high dimensional parameters of Takagi–Sugeno–Kang fuzzy logic controllers (TSK-FLC) used in the cascaded control of grid-tied PMSG-VSWT. The simulation results using PSCAD/EMTDC proved that the produced power when using ESSA is higher than when using SSA which mean higher efficiency and lower cost.

[1]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[2]  Tomasz Pajchrowski,et al.  Neural Speed Controller Trained Online by Means of Modified RPROP Algorithm , 2015, IEEE Transactions on Industrial Informatics.

[3]  Ali Karsaz,et al.  A hybrid optimal PID-Fuzzy control design for seismic exited structural system against earthquake: A salp swarm algorithm , 2018, 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS).

[4]  S. M. Muyeen,et al.  Design Optimization of Controller Parameters Used in Variable Speed Wind Energy Conversion System by Genetic Algorithms , 2012, IEEE Transactions on Sustainable Energy.

[5]  Bin Wu,et al.  Power Conversion and Control of Wind Energy Systems , 2011 .

[6]  Stavros A. Papathanassiou,et al.  A review of grid code technical requirements for wind farms , 2009 .

[7]  Aboul Ella Hassanien,et al.  Swarming behaviour of salps algorithm for predicting chemical compound activities , 2017, 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS).

[8]  Hany M. Hasanien,et al.  Affine projection algorithm based adaptive control scheme for operation of variable-speed wind generator , 2015 .

[9]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[10]  Mohamed H. Haggag,et al.  A novel chaotic salp swarm algorithm for global optimization and feature selection , 2018, Applied Intelligence.

[11]  Songfeng Lu,et al.  Improved salp swarm algorithm based on particle swarm optimization for feature selection , 2018, Journal of Ambient Intelligence and Humanized Computing.

[12]  Hany M. Hasanien,et al.  Hybrid ANFIS-GA-based control scheme for performance enhancement of a grid-connected wind generator , 2018 .

[13]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[14]  Hany M. Hasanien,et al.  Output power smoothing of grid-connected permanent-magnet synchronous generator driven directly by variable speed wind turbine: a review , 2017 .

[15]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[16]  Hany M. Hasanien,et al.  A Taguchi Approach for Optimum Design of Proportional-Integral Controllers in Cascaded Control Scheme , 2013, IEEE Transactions on Power Systems.

[17]  Hossam Faris,et al.  An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems , 2018, Knowl. Based Syst..

[18]  A. Kaveh,et al.  A new meta-heuristic method: Ray Optimization , 2012 .

[19]  Bijaya K. Panigrahi,et al.  A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning , 2016, Swarm Evol. Comput..

[20]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[21]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[22]  Harish Sharma,et al.  Hybrid Artificial Bee Colony algorithm with Differential Evolution , 2017, Appl. Soft Comput..

[23]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[24]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[25]  Vimal J. Savsani,et al.  Effect of hybridizing Biogeography-Based Optimization (BBO) technique with Artificial Immune Algorithm (AIA) and Ant Colony Optimization (ACO) , 2014, Appl. Soft Comput..

[26]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[27]  Hany M. Hasanien,et al.  Augmented grey wolf optimizer for grid-connected PMSG-based wind energy conversion systems , 2018, Appl. Soft Comput..

[28]  Ali Kaveh,et al.  Colliding bodies optimization: A novel meta-heuristic method , 2014 .

[29]  Hany M. Hasanien,et al.  Transient stability enhancement of a grid-connected wind farm using an adaptive neuro-fuzzy controlled-flywheel energy storage system , 2015 .

[30]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[31]  Tomonobu Senjyu,et al.  A comprehensive review of low voltage ride through capability strategies for the wind energy conversion systems , 2016 .

[32]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[33]  Hany M. Hasanien,et al.  Low voltage ride-through capability enhancement of grid-connected permanent magnet synchronous generator driven directly by variable speed wind turbine: a review , 2009 .

[34]  Hany M. Hasanien,et al.  A Grey Wolf Optimizer for Optimum Parameters of Multiple PI Controllers of a Grid-Connected PMSG Driven by Variable Speed Wind Turbine , 2018, IEEE Access.

[35]  Kit Po Wong,et al.  Recent advancement on technical requirements for grid integration of wind power , 2013 .

[36]  Konstantinos G. Margaritis,et al.  On benchmarking functions for genetic algorithms , 2001, Int. J. Comput. Math..

[37]  Om Prakash Mahela,et al.  Comprehensive overview of grid interfaced wind energy generation systems , 2016 .

[38]  Attia A. El-Fergany,et al.  Extracting optimal parameters of PEM fuel cells using Salp Swarm Optimizer , 2018 .

[39]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[40]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[41]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[42]  Xiong Luo,et al.  Parameter Estimation for Soil Water Retention Curve Using the Salp Swarm Algorithm , 2018, Water.

[43]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[44]  Mujahed Al-Dhaifallah,et al.  A Novel Robust Methodology Based Salp Swarm Algorithm for Allocation and Capacity of Renewable Distributed Generators on Distribution Grids , 2018, Energies.