Experimental study on self-excited induction generator for small-scale isolated rural electricity applications

Induction generators have been gaining popularity since the last few decades for the small -scale off-grid power generation renewable energy applications due to many inherent advantages. The IG is not a self-started generator in off-grid mode of operation to generate the required voltage because of isolation from the grid to supply the required amount of reactive power to the generator. In this context, the self-excitation process in the induction generator mainly depends on the amount of reactive power, then speed of the rotor, and load on the system. In this paper, the effect of these three parameters on the performance of the self-excited induction generator is experimentally studied. The focus of the paper is to identify the best configuration of generator operation to carry out the maximum loading capacity and economical operation. Further, the proposed study is extended for an extra initial excitation approach and verified the performance under the same source and load condition and derived some key aspects for utilization of the generator at its maximum loading capacity. A micro-hydro source driven turbine emulations used as an input source to the SEIG in this experimental work

Design, control and performance comparison of PI and ANFIS controllers for BLDC motor driven electric vehicles

The research and usage of electric vehicles (EVs), including two and four-wheeler vehicles, are rapidly increasing worldwide as alternatives to oil/gas-based vehicles. Brushless direct current (BLDC) motors are popular for industrial and traction applications due to their inherent advantages. In EVs, achieving low error in steady-state and transient responses is crucial for smooth acceleration at the wheel. This paper presents the design and control of a BLDC motor for speed control during acceleration and deceleration, considering error as a key factor in the MATLAB/Simulink environment. Proportional-integral (PI) and fuzzy controllers are commonly used for motor control to improve steady-state and transient performance, thereby reducing error. In this study, the PI and adaptive neuro-fuzzy inference system (ANFIS) controllers are designed and compared for a 5-kW, 48-V, and 100-Amp BLDC motor in EV applications. The results demonstrate that the ANFIS controller enhances the dynamic performance of the BLDC motor and improves other operating characteristics such as rise time, settling time, peak overshoot percentage and the vehicle response in terms of speed and distance