Electrical machines parameter identification using genetic algorithms
In Indirect Field Orientation (IFO) of induction motors, the interest for parameters identification has increased rapidly due to the great demand for high performance drives and more sophisticated control systems that have been made possible by the development of very powerful processors, such as fl...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2010
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| Online Access: | https://eprints.nottingham.ac.uk/14005/ |
| _version_ | 1848791858989236224 |
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| author | Kampisios, Konstantinos T. |
| author_facet | Kampisios, Konstantinos T. |
| author_sort | Kampisios, Konstantinos T. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | In Indirect Field Orientation (IFO) of induction motors, the interest for parameters identification has increased rapidly due to the great demand for high performance drives and more sophisticated control systems that have been made possible by the development of very powerful processors, such as floating point DSPs. Accurate knowledge of the machine electrical parameters is also required to ensure correct alignment of the stator current vector relative to the rotor flux vector, to decouple the flux - and torque - producing currents and to tune the current control loops. The accuracy and general robustness of the machine is dependant on this model.
Artificial intelligent technologies have been tested in the field of electro mechanics like neural networks, fuzzy logic, simulated annealing and genetic algorithms. These methods are increasingly being utilised in solving electric machine problems.
This thesis addresses a novel non - intrusive approach for identifying induction motor equivalent circuit parameters based on experimental transient measurements from a vector controlled Induction Motor (I.M.) drive and using an off line Genetic Algorithm (GA) routine with a linear machine model. The evaluation of the electrical motor parameters at rated flux operation is achieved by minimising the error between experimental responses (speed or current) measured on a motor drive and the respective ones obtained by a simulation model based on the same control structure as the experimental rig. An accurate and fast estimation of the electrical motor parameters is so achieved. Results are verified through a comparison of speed, torque and line current responses between the experimental IM drive and a Matlab - Simulink model.
The second part of the research work introduces a new approach based on heuristic optimisation for identifying induction motor electrical parameters under different operating conditions such as different load and flux levels. Results show via interpolation test the effect of the most important electrical parameters, the magnetising inductance Lm and rotor resistance Rr, at each different operating condition. |
| first_indexed | 2025-11-14T18:35:12Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-14005 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:35:12Z |
| publishDate | 2010 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-140052025-02-28T11:28:21Z https://eprints.nottingham.ac.uk/14005/ Electrical machines parameter identification using genetic algorithms Kampisios, Konstantinos T. In Indirect Field Orientation (IFO) of induction motors, the interest for parameters identification has increased rapidly due to the great demand for high performance drives and more sophisticated control systems that have been made possible by the development of very powerful processors, such as floating point DSPs. Accurate knowledge of the machine electrical parameters is also required to ensure correct alignment of the stator current vector relative to the rotor flux vector, to decouple the flux - and torque - producing currents and to tune the current control loops. The accuracy and general robustness of the machine is dependant on this model. Artificial intelligent technologies have been tested in the field of electro mechanics like neural networks, fuzzy logic, simulated annealing and genetic algorithms. These methods are increasingly being utilised in solving electric machine problems. This thesis addresses a novel non - intrusive approach for identifying induction motor equivalent circuit parameters based on experimental transient measurements from a vector controlled Induction Motor (I.M.) drive and using an off line Genetic Algorithm (GA) routine with a linear machine model. The evaluation of the electrical motor parameters at rated flux operation is achieved by minimising the error between experimental responses (speed or current) measured on a motor drive and the respective ones obtained by a simulation model based on the same control structure as the experimental rig. An accurate and fast estimation of the electrical motor parameters is so achieved. Results are verified through a comparison of speed, torque and line current responses between the experimental IM drive and a Matlab - Simulink model. The second part of the research work introduces a new approach based on heuristic optimisation for identifying induction motor electrical parameters under different operating conditions such as different load and flux levels. Results show via interpolation test the effect of the most important electrical parameters, the magnetising inductance Lm and rotor resistance Rr, at each different operating condition. 2010 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/14005/1/523688.pdf Kampisios, Konstantinos T. (2010) Electrical machines parameter identification using genetic algorithms. PhD thesis, University of Nottingham. Electric motors induction genetic algorithms |
| spellingShingle | Electric motors induction genetic algorithms Kampisios, Konstantinos T. Electrical machines parameter identification using genetic algorithms |
| title | Electrical machines parameter identification using genetic algorithms |
| title_full | Electrical machines parameter identification using genetic algorithms |
| title_fullStr | Electrical machines parameter identification using genetic algorithms |
| title_full_unstemmed | Electrical machines parameter identification using genetic algorithms |
| title_short | Electrical machines parameter identification using genetic algorithms |
| title_sort | electrical machines parameter identification using genetic algorithms |
| topic | Electric motors induction genetic algorithms |
| url | https://eprints.nottingham.ac.uk/14005/ |