International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
MODIFIED GWO UPDATING PARAMETER FOR TUNING OF PITCH CONTROL OF FIXED SPEED WIND TURBINE Aliyu Hamza Sule Department of Electrical Engineering, Hassan Usman Katsina Polytechnic, P.M.B. 2052, Katsina, Katsina State, Nigeria ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The Proportional and Integral (PI) controller
researchers to conduct many studies to improve its performance.
found in the pitch control of the Wind Turbine has to be tuned to improve the pitch control performance. In this study, the updating parameter of the Grey Wolf Optimizer (GWO) was modified and the modified GWO was applied to tune the gains of the controller for improving the pitch control tuning results. The controller tuning is formulated as the minimization of the Integral Time multiplied Square Error (ITSE) objective function under controller gains constraints. The modified GWO tuning results were validated through a comparison of its tuning results with that of GWO. The modified GWO exhibited faster convergence speed and provided better-tuned PI gains of the controller than the GWO. The faster settling time in pitch control of the Wind Turbine provided by the modified GWO can reduce the stress in the pitch control of the Wind Turbine compared to GWO.
The accuracy and convergence speed of GWO was improved by [3] through the hybridization of GWO with the Modified Differential Evolution (MDE) algorithm to form MDE-GWO. This improved the GWO search ability and local optimum avoidance. The influence of dominant wolves in improving the searching ability of the GWO was studied in [4], where the dominant wolves were varied at the commencement of every iteration. Furthermore, the dominant wolves were guided by learning data in establishing the three dominant wolves in the subsequent generations. These modifications of the GWO improved its performance in solving optimization problems more than the standard GWO. The a and A parameters of a GWO were tuned in [5] to form the modified GWO. This led to the proper balance between its exploration and exploitation stages, consequently, the modified GWO to converged faster than the GWO. The authors [6] improved the capability of the fittest wolves in the pact to occupy better positions during iterations. This is achieved by balancing the exploitation and exploration stages of the GWO. The simulation result shows improved performance of AGC tuned with the Modified GWO compare with the untuned AGC. The Lévy flight and greedy selection processes were embedded in the GWO [7] to modify its hunting stages This solves the problem of insuffient diversity of wolves in the GWO.
Key Words: Modified Grey Wolf Optimizer, updating parameter, tuning of pitch control, fixed speed wind turbine.
1. INTRODUCTION The Grey Wolf Optimizer (GWO) is one of the populationbased algorithms which was developed by Mir Jalili [1]. It mimics the leadership hierarchy and hunting strategy of grey wolves as one of the top predators in the food chain. The GWO apply its exploration and exploitation capabilities to find the optimal solution to a tuning problem. At the top of the hierarchy of the wolves is the α wolf which is the fittest wolf in the pack normally consisting of 5 or 12 wolves. And next to the α wolf in the leadership hierarchy is the β wolves which assist the α wolf. The δ wolves are the third in the leadership hierarchy, and they shoulder more responsibility for searching for prey and encouraging the rest of the wolves to follow the α wolf. The lowest in the leadership hierarchy are the ⍵ wolves. Their movements depend on how they are instructed by the wolves above them in the leadership [2]. One of the advantages of GWO is its simplicity. It only requires adjusting two parameters to overcome its nearoptimal convergence problem and sharing knowledge of the search space between its search agents. Also, adapting the values of vector A and operator a, ensures the efficient transition of its exploration and exploitation behaviour [1]. However, the GWO has some limitations such as low accuracy and slow convergence speed [3] these led
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The limitation of assigning alpha, beta and delta wolves with the same leadership superiority in the GWO updating position mechanism which contradicts the social leadership of the wolves was solved in [3] through hybridization of the GWO with Modified Differential Evolution to form MDEGWO. It is observed in this study the updating mechanism of the GWO, where the best positions of Alpha (λ), Beta (β) and Delta (δ) wolves used to update the positions of the ω wolves, the three best wolves have equal influences in the updating mechanism. The equal influence has violated the social hierarchy of the wolves and the violation has the possibility of not providing an optimal updating mechanism. The objectives of this study are: 1) To modify the updating parameter of the GWO 2) To Formulate the transfer function of the closed-loop pitch control system of fixed-speed Wind Turbine.
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