This paper presents a novel converter configuration with fewer for switched reluctance motor (SRM) drive. The proposed novel converter insists for less number of switches compared to conventional asymmetrical type of converter configuration for switched reluctance motor. Switch count reduction in converter reduces the losses, volume of heat sink, and number of gate drive circuits and thereby the performance of the system. Closed loop speed control of switched reluctance motor fed from proposed novel converter topology was presented in this paper. Performance of closed loop operation is compared to open loop system. Further the proposed converter for SRMT is evaluated with loaded condition and comparative analysis of no-load and loaded SRM is presented. The model presented is developed and the results are analyzed using MATLAB/SIMULINK software. Closed loop performance of proposed novel converter fed switched reluctance motor drive is verified at fixed speed and variable speed conditions.

A locally installed photovoltaic (PV)-powered motor pump is a viable solution for a water pumping system (WPS) in rural locations. In this study, a single-stage, PV-fed, SRM-powered WPS was investigated and realized using a speed sensorless sliding mode controller (SMC)-based direct torque control (DTC). As a result, no additional DC-DC converter was required for maximum power absorption from the PV source. By utilizing a novel high-side switch asymmetric converter with a hybrid Perturb and Observe–Grey Wolf optimization (POGWO) method integrated with a DC-link voltage controller, an efficient single-stage conversion was achieved. The robustness of the proposed integrated control is presented by comparing it with a Genetic Algorithm and Particle Swarm Optimization (PSO). Extensive results using MATLAB SIMULINK are shown to validate the proposed system in both steady-state and transient conditions for various partial shading conditions.

<p>The best alternative machine for synchronous and induction machine is switched reluctance machine for various applications. An artificial neural network (ANN) based vector controller is implemented for novel converter to drive switched reluctance motor (SRM) in this paper. To reduce the cost and simplified the controller an effective configuration of converter is proposed with only 4 pulse-withmodulation (PWM) based switches. The 6 pole stator and 4 pole rotor machine is considered in this paper to present results based on MATLAB. The ripples in torque are reduced by proposing vector controller by using novel configuration of converter. Generally SRM machines are having high ripples in torque, hence less number of switches will be feasible solution to drive the machine in order to reduce ripples. The proposed controller can also help to operate system with less ripples in torque since the controller having both torque and flux hysteresis controllers. The extensive results are presented on Simulink platform to validate the proposed method under both steady state as well as transient conditions.</p>

The switched reluctance motor (SRM) is recently gaining huge popularity in electric vehicle (EV) applications due to its control flexibility, simple structure, lower cost and high efficiency than the synchronous and induction motors. Among all the controllers, the direct torque control (DTC) is the most preferred due to its higher efficiency, lower losses and superior control characteristics. In this paper, a 6/4 pole SRM with fuzzy logic based DTC has been proposed for the EV application along with a converter with reduced switch counts to reduce the torque ripples and enhance the performance of the system under steady and transient state conditions. The proposed system is tested and validated under various scenarios that include load torque and speed variations and compared with the vector control method. From, the investigation it has been found that the proposed technique reduces ripples from the system during all the scenarios with a resultant flux of less than 0.5pu.