A Flexible AC Distribution System Device for a Microgrid

Abstract—

This paper presents a flexible ac distribution system device for microgrid applications. The device aims to improve the power quality and reliability of the overall power distribution system that the microgrid is connected to. The control design employs a new model predictive control algorithm which allows faster computational time for large power systems by optimizing the steady-state and the transient control problems separately. Extended Kalman filters are also employed for frequency tracking and to extract the harmonic spectra of the grid voltage and the load currents in the microgrid. The design concept is verified through different test case scenarios to demonstrate the capability of the proposed device and the results obtained are discussed.

Improved Active Power Filter Performance for Renewable Power Generation Systems

Abstract—
An active power filter implemented with a four-leg voltage-source inverter using a predictive control scheme is presented. The use of a four-leg voltage-source inverter allows the compensation of current harmonic components, as well as unbalanced current generated by single-phase nonlinear loads. A detailed yet simple mathematical model of the active power filter, including the effect of the equivalent power system impedance, is derived and used to design the predictive control algorithm. The compensation performance of the proposed active power filter and the associated control scheme under steady state and transient operating conditions is demonstrated through simulations and experimental results.

Neuro-Fuzzy Based Speed Controller For Permanent Magnet Synchronous Motor

Abstract—

The conventional Proportional-Integral (PI) speed control has been widely used in industrial motor controls due to its capabilities in controlling linear plants. However, motor behaves as non-linear plant where the PI speed control may not be able to provide precise speed responses. With the fast growing of artificial intelligent in motor controls, the fuzzy logic and Adaptive Network Fuzzy Inference system (ANFIS) is available in more precise motor controls. Nevertheless, there are still many disputes on the superiority of PI and fuzzy logic controls. The fuzzy logic controller with rules-based is limited to a particular load torque due to its output membership functions, on the other hand, PI controller has better adaptability over load torque variation and has a smaller steady-state error even though it incurs the overshoot and has longer settling time. In this paper, a comparative analysis of PI, fuzzy logic, and ANFIS has done in the MATLAB SIMULINK environment

Design, Development & Simulation of Fuzzy Logic Controller to Control the Speed of Permanent Magnet Synchronous Motor Drive System

ABSTRACT
The paper presents  the detailed modelling by fuzzy logic controller (FLC) for  permanent magnet synchronous motor derive system in Simulink, the simulation includes all realistic components of the system, which enables the calculation of current and voltages in different parts of the invertors and motor under transients and steady state condition. The fuzzy logic controller is used for speed control of this type of motor. The dynamic response of (PMSM) with the proposed controller is studied under different load disturbances. The effectiveness of the proposed fuzzy logic controller is compared with that of the conventional PI & PID controllers. The proposed controller is used in order to overcome the nonlinearity problem of PMSM and also to achieve faster settling response.