Direct average torque control of switched reluctance generator

Abstract

Abstract There are few pieces of research on switched reluctance generator (SRG) that accomplish optimal performance torque control, most prefer to use voltage, speed, or power control strategies. This paper proposes a direct average torque control (DATC) for SRG applied to wind energy conversion systems (WECS). The SRG is driven by an asymmetric half‐bridge converter (AHB), and an external chopper regulates the DC‐bus voltage. In the DATC strategy, the switching logic is modified, so that the machine operates in the generator mode. The dynamic behavior of an artificial neural network (ANN)‐based and the conventional co‐energy‐based torque estimation methods are discussed. Thereafter, the proposed DATC is compared to the direct power control (DPC) strategy. For both strategies, lookup tables are created to store the optimal switching angles. These lookup tables are optimized to achieve a trade‐off between torque ripple minimization and efficiency maximization. Experimental results show that the DATC strategy presents a lower torque ripple than the DPC method over the entire speed range. In contrast, DPC delivers superior efficiency for low and medium speeds.

Keywords

• neural nets
• power electronics
• power generation control
• reluctance generators
• reluctance motors
• renewable energy sources