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-Abstract
High phase machines (HPM) is the race horse of the near future decades. It offers the distribution of large power over more phases. The capability of the HPM drives to survive even if it is subjected to gate drive circuit fault is of great interest. Such a merit improves the system reliability and dependability. The HPM at post fault condition is able to deliver considerable percentage of its rated torque. The amount of derating depends on the number of the entire HPM phases and the number phases lost. As the number of HPM phases increases, the derating due to a single phase loss decreases. Several researchers exert large efforts solving HPM fault overcome issues. The latest fault overcome technique is the HDPOT. Some other efforts are exerted to get rid of the annoying mechanically weak encoders. Encoder-less operation is an additional factor that enhances the overall system reliability. Attention is paid as well to operation without encoder of the HPM subject to fault. Kalman filter is one of the observer based encoder-less methods. Kalman filter is a well-established encoder-less method that has been applied to three phase machines and HPM as well. Still the HDPOT method is not investigated together with an encoder- less method. This paper hopes to close this gap with proved simulation work. This paper will investigate the encoder-less operation of an asymmetrical six phase machine subjected to single gate drive fault using kalman filter and HDPOT method.
DOI
10.21608/ijisd.2021.145558
Keywords
Encoder- less, High phase machine, Kalman filter, Fault overcome
Authors
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AIN SHAMS UNIVERSITY
Email
elhussien.abbas@gmail.com
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Electrical Power and Machine Department, Faculty Of Engineering, Ain Shams University
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https://ijisd.journals.ekb.eg/article_145558.html
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https://ijisd.journals.ekb.eg/service?article_code=145558
Publication Title
International Journal of Industry and Sustainable Development
Publication Link
https://ijisd.journals.ekb.eg/
MainTitle
Encoder Less Hex- Phase Motor Drive Operated Under Fault Using Kalman Filter