Parameter Estimation and Sensorless Control of Variable Phase-Pole Induction Machines in the Context of Magnetic Pole Transition
| dc.contributor.advisor | Mengesha Mamo (PhD) | |
| dc.contributor.advisor | Luca Peretti (PhD) | |
| dc.contributor.author | Mesfin Tilahune | |
| dc.date.accessioned | 2025-07-21T13:41:16Z | |
| dc.date.available | 2025-07-21T13:41:16Z | |
| dc.date.issued | 2025-05 | |
| dc.description.abstract | The importance of transitioning from fossil fuel cars to electric vehicles is a critical step in the global initiative to mitigate the impacts of climate change. As electric vehicles become a central element of sustainable transportation systems, countries worldwide, including Ethiopia, are working to achieve their carbon reduction goals. One of the primary reasons for this shift is the absence of tailpipe emissions from EVs, unlike traditional internal combustion engine vehicles, which release substantial amounts of carbon dioxide 𝐶𝑂2 and other harmful pollutants into the environment. Hence, replacing conventional cars with electric ones significantly reduces greenhouse gas emissions, which are a major contributor to global warming. The first part of this dissertation focuses on the necessity of accurately estimating electrical parameters for multiphase machines to be used in advanced control approaches. This necessity is especially pertinent for the special subclass of multiphase machines identified as Variable Phase-Pole Induction Machines (VPPIMs). This part of the research explores, calculates, and experimentally confirms various parameter estimation techniques for awound stator coil induction machine with independent slot current control capable of dynamically adjusting the phase-pole configuration in real time. The parameter estimation techniques covered are a traditional finite element analysis-based stator impedance calculation and an application of the finite element-based harmonic plane decomposition (HPD) method. Experimental validation demonstrates the proposed estimation methods across different machine configurations. When comparing the two methods, parameter estimation based on HPD theory aligns closely with practical outcomes. The classical method results in a 22% relative error for the magnetizing inductance (𝐿𝑀) and a 62% overestimation for the leakage inductance (𝐿𝜎 ) in the 3-phase/2-pole VPPIM compared to measurements. The HPD approach reduces these errors to 15% for magnetizing inductance and 36% for leakage inductance. The conventional approach yields a 17% rate for the magnetizing inductance 𝐿𝑀, which decreases to 6% with the HPD method. Similarly, for the leakage inductance (𝐿𝜎 ), the rate of 54% is reduced to 23% in the 9-phase/2-pole VPPIM configuration. Additionally, the HPD model cuts computing time to one-eighth of the conventional methods, significantly enhancing computational efficiency. The latter portion of this research concentrates on variable phase-pole induction machines, which show potential for electromobility applications because of their ability to electronically modify the number of magnetic poles during operation, consequently enhancing the machine’s torque-speed performance. This research introduces a model reference adaptive system approach for the sensorless speed operation of a nine-phase variable phase-pole induction machine. The same adaptive full-order observer estimates mechanical speed in two distinct ways, each corresponding to a different pole-pair configuration of interest. Sensorless drive operation is sustained during pole-pair configuration transitions through an appropriate switch between the two speed-estimation strategies, ensuring accurate rotor flux linkage information for high-performance control. The proposed method’s simulation and experimental outcomes strongly correlate, affirming the sensorless methodology’s effectiveness under variable load scenarios and during positive/negative speed transitions. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/5661 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Electric drives | |
| dc.subject | Electrical machine | |
| dc.subject | Multiphase drive | |
| dc.subject | Pole-phase changing | |
| dc.subject | State-space model | |
| dc.subject | Speed control | |
| dc.subject | Sensorless control. | |
| dc.title | Parameter Estimation and Sensorless Control of Variable Phase-Pole Induction Machines in the Context of Magnetic Pole Transition | |
| dc.type | Dissertation |