Improve Performance of Induction Motor Drive using Weighting Factor approach-based Gravitational Search Algorithm

Zhu, Tiezhu; Nazir, Muhammad Shahzad; Mokhtarzadeh, Amir Ali; Abdalla, Ahmed N.; Nazir, Hafiz M. Jamheed; Wan, Cao

doi:10.1080/00207217.2021.1964615

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Weighting factor removal by reference transformation 33,34 Higher computational burden as compared with conventional PTC and difficult to incorporate multiple control objectives 35 Weighting factor tuning based on coefficient of variation 36 Optimized weights are uncertain in this method and complex calculations are required to implement on hardware Weighting factor tuning based on TOPSIS and NSGA-II methods 37 TOPSIS and NSGA-II algorithms require complex calculations leading to computational challenges 12 Weighting factor removal by Ranking method 38 Ranking based techniques become unfeasible as number of control objectives increases 39 Tuning of weighting factor based on simple additive technique 40 Although technique is simple but not suitable for multiple control objectives 11 Weighting factor tuning based on current ripples 41 Highly dependent on parameter estimation 8,42 Tuning of weighting factor based on error of control objectives 43 This method becomes challenging and complex when number of control objectives increases 44 Weighting factor tuning using Genetic Algorithm (GA) 45 , Simulated Annealing (SA) 42 or Gravitational Search Algorithm (GSA) 43 , Artificial Neural Network 46 , Ant colony based optimization 47 These algorithms are very complex and pose computational challenges 48 Weighting factor tuning based on algebraic/numerical techniques 49 Design complexity increases as slection of weighting factor increases 50 Weighting factor selection based on homogeneous cost functions [51][52][53] This technique is relatively efficient but unable to include multiple control objectives 54 Direct vector selection based techniques to remove weighting factors from cost function 55,56 Direct vector selection techniques provid lower computational burden and lower complexity , however cannot incorporate multiple control objecitve 57 Weighting factor elimination by using cascaded structure of FCS-MPC …”

Section: Ptc Methods Limitationsmentioning

confidence: 99%

“…However, online computing of weighting factor by such methods limits the search accuracy and increases the computational complexity 47 . Other meta-heuristic methods include gravitational search algorithm (GSA) 43 , non-dominated sorting genetic algorithm II (NSGA-II) and Particle Swarm Optimization (PSO) [10][11][12] . ANN methods are reported in 46,[63][64][65][66] .…”

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confidence: 99%

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Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications

Shahid,

Jin,

Abbasi

et al. 2024

Sci Rep

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In the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively.

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Section: Ptc Methods Limitationsmentioning

confidence: 99%

mentioning

confidence: 99%

Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications

Shahid,

Jin,

Abbasi

et al. 2024

Sci Rep

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show abstract

“…The inverter has weak resistance to Sustainability 2022, 14, 8510 2 of 15 current and is prone to overcurrent. The synchronous motor has a strong ability to cope with external disturbances due to its structural characteristics, while the inverter topology is mainly composed of power electronic devices, which have limited bearing capacity and poor resistance in case of system failure [6]. Therefore, the characteristics of the synchronous motor can ensure the stable operation of the power system.…”

Section: Introductionmentioning

confidence: 99%

An Energy Storage Assessment: Using Frequency Modulation Approach to Capture Optimal Coordination

et al. 2022

Self Cite

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To reduce the allocation of energy storage capacity in wind farms and improve economic benefits, this study is focused on the virtual synchronous generator (synchronverter) technology. A system accompanied by wind power, energy storage, a synchronous generator and load is presented in detail. A brief description of the virtual synchronous generator control strategy is given. The capacity allocation is based on different optimization goals and the optimal energy storage capacity configuration of the coordinated frequency modulation (FM) control strategy. The detail of the dual-loop control strategy is carried out by establishing the grid-connected transfer function model of the synchronverter energy storage and a theoretical model of life cycle cost is established. The optimal control strategy of coordinated FM for wind storage is implemented using MATLAB software. The simulation showed that the proposed strategy provided the energy storage capacity at high wind speed, which is configured to be 5.9% of the installed capacity of the wind turbine, marking a reduction of 26% compared with the 8% capacity required for independent support. In addition, the proposed method has improved the energy storage capacity configuration of the coordinated FM control strategy.

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Optimal drive train management of wind turbine using LiDAR-assisted predictive control strategy

Sudharsan

Karthikeyan

Xavier³

et al. 2023

Environ Dev Sustain

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Improve Performance of Induction Motor Drive using Weighting Factor approach-based Gravitational Search Algorithm

Cited by 5 publications

References 15 publications

Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications

Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications

An Energy Storage Assessment: Using Frequency Modulation Approach to Capture Optimal Coordination

Optimal drive train management of wind turbine using LiDAR-assisted predictive control strategy

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