Electrical Equivalent Circuit Parameter Estimation of Commercial Induction Machines Using an Enhanced Grey Wolf Optimization Algorithm
This paper addresses the critical challenge of optimizing the energy efficiency of induction motors, which are pivotal components across diverse industrial sectors due to their substantial energy consumption. Given the non-measurable internal parameters of induction motors, parameter identification...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Biomimetics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-7673/10/4/228 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850155898256752640 |
|---|---|
| author | Premkumar Manoharan Sowmya Ravichandran Jagarapu S. V. Siva Kumar Mustafa Abdullah Tan Ching Sin Tengku Juhana Tengku Hashim |
| author_facet | Premkumar Manoharan Sowmya Ravichandran Jagarapu S. V. Siva Kumar Mustafa Abdullah Tan Ching Sin Tengku Juhana Tengku Hashim |
| author_sort | Premkumar Manoharan |
| collection | DOAJ |
| description | This paper addresses the critical challenge of optimizing the energy efficiency of induction motors, which are pivotal components across diverse industrial sectors due to their substantial energy consumption. Given the non-measurable internal parameters of induction motors, parameter identification becomes a complex, multidimensional optimization problem characterized by highly nonlinear and multimodal error surfaces. Traditional optimization algorithms often weaken, yielding suboptimal results due to an inadequate balance between the exploration and exploitation phases. To overcome these limitations, this study introduces an Adaptive Weight Grey Wolf Optimizer (AWGWO) to enhance the accuracy and reliability of induction motor parameter estimation. The AWGWO incorporates an adaptive weight mechanism that dynamically adjusts the exploration and exploitation balance, effectively mitigating issues such as premature convergence to local optima. Extensive simulation validation was conducted across various induction motor models, including eight commercial motors, and demonstrated that AWGWO consistently outperforms state-of-the-art algorithms in terms of convergence speed, solution accuracy, and robustness in multimodal optimization landscapes. The AWGWO consistently exhibited faster convergence, significantly reducing premature convergence. Moreover, the adaptive weight mechanism enabled a more effective balance between exploration and exploitation, leading to higher accuracy in parameter estimation. Comparative analyses reveal that AWGWO outperforms existing algorithms not only in achieving lower error rates, but also in maintaining stability. This study significantly contributes to progress in the field by providing an effective tool for induction motor parameterization, thereby offering potential improvements in energy efficiency. |
| format | Article |
| id | doaj-art-8767451ccbc54b729df0e971daed559f |
| institution | OA Journals |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-8767451ccbc54b729df0e971daed559f2025-08-20T02:24:44ZengMDPI AGBiomimetics2313-76732025-04-0110422810.3390/biomimetics10040228Electrical Equivalent Circuit Parameter Estimation of Commercial Induction Machines Using an Enhanced Grey Wolf Optimization AlgorithmPremkumar Manoharan0Sowmya Ravichandran1Jagarapu S. V. Siva Kumar2Mustafa Abdullah3Tan Ching Sin4Tengku Juhana Tengku Hashim5Institute of Power Engineering (IPE), Department of Electrical and Electronics Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya, Kajang 43000, Selangor, MalaysiaDepartment of Electronics and Communication Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaDepartment of Electrical and Electronics Engineering, GMR Institute of Technology, Rajam 532127, Andhra Pradesh, IndiaElectric Vehicle Engineering Department, Hourani Center for Applied Scientific Research, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, JordanInstitute of Power Engineering (IPE), Department of Electrical and Electronics Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya, Kajang 43000, Selangor, MalaysiaInstitute of Power Engineering (IPE), Department of Electrical and Electronics Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya, Kajang 43000, Selangor, MalaysiaThis paper addresses the critical challenge of optimizing the energy efficiency of induction motors, which are pivotal components across diverse industrial sectors due to their substantial energy consumption. Given the non-measurable internal parameters of induction motors, parameter identification becomes a complex, multidimensional optimization problem characterized by highly nonlinear and multimodal error surfaces. Traditional optimization algorithms often weaken, yielding suboptimal results due to an inadequate balance between the exploration and exploitation phases. To overcome these limitations, this study introduces an Adaptive Weight Grey Wolf Optimizer (AWGWO) to enhance the accuracy and reliability of induction motor parameter estimation. The AWGWO incorporates an adaptive weight mechanism that dynamically adjusts the exploration and exploitation balance, effectively mitigating issues such as premature convergence to local optima. Extensive simulation validation was conducted across various induction motor models, including eight commercial motors, and demonstrated that AWGWO consistently outperforms state-of-the-art algorithms in terms of convergence speed, solution accuracy, and robustness in multimodal optimization landscapes. The AWGWO consistently exhibited faster convergence, significantly reducing premature convergence. Moreover, the adaptive weight mechanism enabled a more effective balance between exploration and exploitation, leading to higher accuracy in parameter estimation. Comparative analyses reveal that AWGWO outperforms existing algorithms not only in achieving lower error rates, but also in maintaining stability. This study significantly contributes to progress in the field by providing an effective tool for induction motor parameterization, thereby offering potential improvements in energy efficiency.https://www.mdpi.com/2313-7673/10/4/228adaptive weightenergygrey wolf optimizerinduction motormultimodal optimizationparameter estimation |
| spellingShingle | Premkumar Manoharan Sowmya Ravichandran Jagarapu S. V. Siva Kumar Mustafa Abdullah Tan Ching Sin Tengku Juhana Tengku Hashim Electrical Equivalent Circuit Parameter Estimation of Commercial Induction Machines Using an Enhanced Grey Wolf Optimization Algorithm Biomimetics adaptive weight energy grey wolf optimizer induction motor multimodal optimization parameter estimation |
| title | Electrical Equivalent Circuit Parameter Estimation of Commercial Induction Machines Using an Enhanced Grey Wolf Optimization Algorithm |
| title_full | Electrical Equivalent Circuit Parameter Estimation of Commercial Induction Machines Using an Enhanced Grey Wolf Optimization Algorithm |
| title_fullStr | Electrical Equivalent Circuit Parameter Estimation of Commercial Induction Machines Using an Enhanced Grey Wolf Optimization Algorithm |
| title_full_unstemmed | Electrical Equivalent Circuit Parameter Estimation of Commercial Induction Machines Using an Enhanced Grey Wolf Optimization Algorithm |
| title_short | Electrical Equivalent Circuit Parameter Estimation of Commercial Induction Machines Using an Enhanced Grey Wolf Optimization Algorithm |
| title_sort | electrical equivalent circuit parameter estimation of commercial induction machines using an enhanced grey wolf optimization algorithm |
| topic | adaptive weight energy grey wolf optimizer induction motor multimodal optimization parameter estimation |
| url | https://www.mdpi.com/2313-7673/10/4/228 |
| work_keys_str_mv | AT premkumarmanoharan electricalequivalentcircuitparameterestimationofcommercialinductionmachinesusinganenhancedgreywolfoptimizationalgorithm AT sowmyaravichandran electricalequivalentcircuitparameterestimationofcommercialinductionmachinesusinganenhancedgreywolfoptimizationalgorithm AT jagarapusvsivakumar electricalequivalentcircuitparameterestimationofcommercialinductionmachinesusinganenhancedgreywolfoptimizationalgorithm AT mustafaabdullah electricalequivalentcircuitparameterestimationofcommercialinductionmachinesusinganenhancedgreywolfoptimizationalgorithm AT tanchingsin electricalequivalentcircuitparameterestimationofcommercialinductionmachinesusinganenhancedgreywolfoptimizationalgorithm AT tengkujuhanatengkuhashim electricalequivalentcircuitparameterestimationofcommercialinductionmachinesusinganenhancedgreywolfoptimizationalgorithm |