Wireless Power Transfer Machine Learning Assisted Characteristics Prediction for Effective Wireless Power Transfer Systems
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Journal Title
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Perustieteiden korkeakoulu |
Master's thesis
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Authors
Date
2020-10-20
Department
Major/Subject
Autonomous Systems
Mcode
ELEC3055
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
63
Series
Abstract
One of the main challenges in wireless power transfer (WPT) devices is performance degradation when the receiver's position and characteristics vary. The variations in the system parameters such as load impedance and coupling strength in WPT devices affect performance characteristics such as output voltage and power. When the system parameters are different from the optimal operating conditions, the performances are degraded. Therefore, the load impedance and coupling strength must be monitored to do the necessary optimization and control. However, such control approaches require additional sensing circuits and a data communication link between transmitter- and receiver-sides. This study proposes a new machine learning (ML) assisted the WPT system that predicts the power delivered to the receiver by only using measurements at the transmitter-side. In addition, a method is also proposed to estimate load impedance and coupling coefficient using a machine learning approach. We study what parameters measurable at the transmitter-side can be used to predict the output power delivered to receivers at variable load impedance and coupling strengths. In the proposed method, the output power of an inductor-capacitor-capacitor (LCC)-Series tuned WPT system is successfully predicted only using the measured root-mean-square (RMS) of the input current. The random forest algorithm has shown the best accuracy to estimate the output power based on transmitter-side parameters only. The proposed approach is experimentally validated using a laboratory prototype. Harmonic components of the input current are used to assess the load impedance and coupling coefficient successfully. Multi-output regression has the highest accuracy for estimating the load impedance and coupling coefficient. The proposed ML algorithm is also used to classify the turn-on and -off regimes to ensure high-efficient operation.Description
Supervisor
Nee, Hans-PeterThesis advisor
Liakos, EvangelosJayathurathnage, Prasad
Keywords
wireless power transfer, machine learning, coupling strength estimation, load impedance estimation, multi-transmitter wireless power transfer systems