<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>To solve motor heating and life shortening of parallel switched reluctance generator (SRG) induced by uneven output currents due to different external characteristics, we generally adopt current sharing control (CSC) to make each parallel generator undertake large load currents on average to improve the reliability of parallel power generation system. However, the method usually causes additional loss of power because it does not consider the efficiency characteristics of each parallel generator. Therefore, with the efficiency expression for the parallel system of SRG established and analysed, the control strategy based on differential evolution (DE) algorithm is proposed as a mechanism by which to enhance generating capacity and reliability of multi-machine power generation from the perspective of efficiency optimisation. We re-adjust the reference current of each parallel generator to transform the working point of each generator and implement the efficiency optimisation of parallel system. The performance of the proposed control method is evaluated in detail by the simulation and experiment, and comparison with traditional CSC is carried out as well.</p>
</abstract>

To solve motor heating and life shortening of parallel switched reluctance generator (SRG) induced by uneven output currents due to different external characteristics, we generally adopt current sharing control (CSC) to make each parallel generator undertake large load currents on average to improve the reliability of parallel power generation system. However, the method usually causes additional loss of power because it does not consider the efficiency characteristics of each parallel generator. Therefore, with the efficiency expression for the parallel system of SRG established and analysed, the control strategy based on differential evolution (DE) algorithm is proposed as a mechanism by which to enhance generating capacity and reliability of multi-machine power generation from the perspective of efficiency optimisation. We re-adjust the reference current of each parallel generator to transform the working point of each generator and implement the efficiency optimisation of parallel system. The performance of the proposed control method is evaluated in detail by the simulation and experiment, and comparison with traditional CSC is carried out as well.

Control Strategy of Parallel Systems with Efficiency Optimisation in Switched Reluctance Generators

State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems

China University of Mining and Technology

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

To solve motor heating and life shortening of parallel switched reluctance generator (SRG) induced by uneven output currents due to different external...

Control Strategy of Parallel Systems with Efficiency Optimisation in Switched Reluctance Generators

Publicado en línea: 16 jul 2021

Páginas: 61 - 74

Recibido: 27 mar 2021

Aceptado: 04 may 2021

DOI: https://doi.org/10.2478/pead-2021-0003

Palabras clave
switched reluctance generator, parallel system, efficiency optimization, differential evolution algorithm

© 2021 Xiaoshu Zan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Control Strategy of Parallel Systems with Efficiency Optimisation in Switched Reluctance Generators

Publicado en línea: 16 jul 2021

Páginas: 61 - 74

Recibido: 27 mar 2021

Aceptado: 04 may 2021

DOI: https://doi.org/10.2478/pead-2021-0003

Palabras claveswitched reluctance generator, parallel system, efficiency optimization, differential evolution algorithm

© 2021 Xiaoshu Zan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
switched reluctance generator, parallel system, efficiency optimization, differential evolution algorithm