Enhanced Controller With Z-Source Converter for Voltage Regulation and Power Factor Improvement in Switched Reluctance Motors

Switched reluctance motors (SRMs) are less stable, simple, and reliable, even in harsh environments. Despite its advantages, SRM remained out of date until advancements in power electronic devices made it possible to implement SRM drives. The efficiency of SRMs is limited by issues such as high torque ripple, low power factor (PF), and control complexity. Developments in power electronics have stimulated concepts for further enhancing the performance of SRMs, making them even better candidates for modern applications. Hence, issues related to acoustic noise and nonlinear characteristics remain. Addressing these constraints ensures reliable operation and greater efficiency. In this paper, an enhanced Z-Source converter-based controller is developed for voltage management and PF correction of SRMs, an innovative front-end converter that simultaneously performs voltage regulation and PF correction, tailored for SRM performance enhancement. The proposed converter, acting as a front-end device, performs power-factor correction and voltage regulation by adjusting the magnetization voltage according to the operating mode and drive structure requirements. To achieve these objectives, a central control technique (CCT) is developed that reduces the third-harmonic distortion (THD) and improves the PF. Moreover, angle control is employed to reduce torque ripple and maintain voltage regulation in the front-end converter. It uses a fractional order integral derivative (FOPID) system that is optimized using the modified coronavirus mask protection algorithm (MCMPA). This optimization was improved by MCMPA, which is an addition of the coronavirus mask protection algorithm (CMPA) combined with Levy flight distribution (LFD). Efficient operation of the converter ensures improved voltage management and PF correction. To validate the performance of the proposed controller, the SRM motor was tested under electric vehicle (EV) load conditions. To validate the proposed methodology, it was designed in MATLAB; the performance was evaluated using different measures such as SRM motor current, voltage, speed, and torque. The proposed methodology was compared with conventional approaches such as ant colony optimization (ACO), whale optimization algorithm (WOA), and enhanced fire hawk optimization (EFHO).