0000000000436086
AUTHOR
Dhirendran Kumar
Adaptive Feed-Forward Neural Network for Wind Power Delivery
This paper describes a grid connected wind energy conversion system. The interconnecting filter is a simple inductor with a series resistor to minimize three-phase current Total Harmonic Distortion (THD). Using the Recursive Least Squares (RLS) Estimator, an online grid impedance technique is proposed in the stationary reference frame using the Recursive Least Squares (RLS) Estimator. An Adaptive Feedforward Neural (AFN) Controller has also been developed using the inverse of the system to improve the performance of the current Proportional-Integral controller under dynamical conditions and provide better DC link voltage stability. The neural network weights are computed in real-time using …
Online Estimation of the Mechanical Parameters of an Induction Machine Using Speed Loop characteristics and Recursive Least Square Technique
This paper presents a novel approach for estimation of mechanical parameters, inertia and friction coefficient of an Induction Machine (IM) using speed loop characteristics and Recursive Least Square (RLS) estimator. Using the 5th order dynamic equation for Induction Machine and the forgetting factor based RLS algorithm the technique herein proposed employs the speed of the machine and the torque as the inputs for the estimator. Results obtained compares the estimated parameters with the actual parameters under multiple step varying and exponentially varying scenarios. Upon analyzing the results, the validity and the effectiveness of the proposed identification technique is confirmed
Enhanced Current Loop PI Controllers with Adaptive Feed-Forward Neural Network via Estimation of Grid Impedance: Application to Three-Phase Grid-Tied PV Inverters
This paper describes a single-stage grid-connected three-phase photovoltaic inverter feeding power to the grid. Using the Recursive Least Squares (RLS) Estimator, an online grid impedance technique is proposed in the stationary reference frame. The method iteratively estimates the grid resistance and inductance values and is effective in detecting inverter islanding according to IEEE standard 929-2000. An Adaptive Feedforward Neural (AFN) Controller has also been developed using the inverse of the system to improve the performance of the inner-loop Proportional-Integral controllers under dynamical conditions and provide better DC link voltage stability. The neural network weights are comput…
Enhancing Speed Loop PI Controllers with Adaptive Feed-forward Neural Networks: Application to Induction Motor Drives
This paper proposes the idea to improve the performance of the speed loop PI controller by using feed-forward and adaptive control actions. Indeed, when the system to be controlled is required to track a rapidly changing reference frame, higher bandwidth is usually required, making the system more sensitive to noise and consequently less robust. In such cases, to achieve a better performance in reference tracking while keeping noise rejection capacity, one idea is to use a feed-forward controller, employed to enhance the required tracking, leaving the feedback action to stabilize the system and suppress higher frequency disturbance. As such, this paper analysis the classical PI based field …