Hasan A. Yousef

A new method for voltage and frequency control of stand-alone self-excited induction generator using PWM converter with variable DC link voltage

Three-phase self-excited induction generators (SEIG) play an important rule in renewable energy sources such as wind and hydraulic energy. Their main disadvantage is poor voltage and frequency regulation under varying load and speed. This paper introduces a new and simple method for voltage and frequency control of three-phase unregulated speed induction generators in the islanding mode. The method uses a constant voltage constant frequency (CVCF) PWM converter without regulating the DC capacitor voltage. The capacitor voltage is left changing with the loading conditions and the AC side voltage is regulated by controlling the modulation index. This eliminates the need of an auxiliary switch in the DC side which reduces the cost and also reduces the high frequency current components flowing in the DC capacitor and that increases its life time. The proposed technique is tested under step changes in load and prime mover speed. The proposed technique gives the same response as the old technique but without the use of DC side switch.

Adaptive fuzzy mimo control of induction motors

This paper presents a new adaptive fuzzy control technique applied to induction motors (IM). The control task of such motors is considered complicated by the fact that these motors have uncertain time-varying parameters and are subjected to unknown load disturbance. A nonlinear multi-input multi-output (MIMO) state feedback linearizing control is designed for the IM modeled in a stationary reference frame. An adaptive fuzzy controller is proposed to estimate the unknown nonlinear functions that appear in the state feedback input-output linearizing control. Simulation results are presented to validate the effectiveness of the proposed controller even in the presence of motor parameter variations and unknown load disturbance.

Adaptive fuzzy APSO based inverse tracking-controller with an application to DC motors

This paper introduces the use of the adaptive particle swarm optimization (APSO) for adapting the weights of fuzzy neural networks (FNN) on line. The fuzzy neural network is used for identification of the dynamics of a DC motor with nonlinear load torque. Then the motor speed is controlled using an inverse controller to follow a required speed trajectory. The parameters of the DC motor are assumed unknown as well as the nonlinear load torque characteristics. In the first stage a nonlinear fuzzy neural network (FNN) is used to approximate the motor control voltage as a function of the motor speed samples. In the second stage, the above mentioned approximator is used to calculate the control signal (the motor voltage) as a function of the speed samples and the required reference trajectory. Unlike the conventional back-propagation technique, the adaptation of the weights of the FNN approximator is done on-line using adaptive particle swarm optimization (APSO). The APSO is based on the least squares error minimization with random initial condition and without any off-line pre-training. Simulation results are presented to prove the effectiveness of the proposed control technique in achieving the tracking performance.

Wavelet network-based motion control of DC motors

In this paper, a wavelet network is presented to design different controllers for DC motors based on the multi-resolution analysis and the wavelet transform. One of the basic advantages of wavelet network is that training is done using the recursive least square method which is suitable for online training usually required for adaptive control. The wavelet network is used to design adaptive speed controllers for a DC motor to achieve high performance speed control even if the motor model is unknown, the load characteristics are also unknown function of speed and the load torque changes online. Simulation and experimental results are presented to validate the effectiveness of the proposed controllers.

Adaptive Fuzzy Decentralized Control for a Class of Large-Scale Nonlinear Systems with MIMO Subsystems

This paper presents a fuzzy basis function approach for adaptive decentralized control of a class of large-scale nonlinear systems with MIMO subsystems. Hybrid adaptive-robust tracking control schemes which are based on a combination of the HT tracking theory, and fuzzy control design are developed such that all the states and signals are bounded and the HT tracking control performance is guaranteed. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds. The resultant decentralized control with multi-controller architecture guarantee stability and convergence of the output errors to zero asymptotically, by local output-feedback. Simulation results on the control of a model of a nonlinear electrical machine are presented to illustrate the effectiveness of the proposed controller

Indirect Adaptive Fuzzy Coordinated Excitation and SVC Control for Multi-Machine Power System

Coordinated control scheme for generator excitation and static VAR compensator (SVC) using an indirect adaptive fuzzy logic control (IAFLC) of multi-machine power systems based on multi-input-multi-output feedback linearization technique is developed in this paper. The power system considered in this paper consists of two generators and infinite bus connected through a network of transformers and transmission lines. The fuzzy controller is constructed from fuzzy feedback linearization controller whose parameters are adjusted indirectly from the estimates of plant parameters. The adaptation law adjusts the controller parameters on-line so that the plant outputs tracks the reference model outputs. Simulation results show that the proposed IAFLC controller, compared with a controller based on traditional linearization technique enhances the transient stability of the power system under sever abnormalities

A New Method for Voltage and Frequency Control of Stand-alone Self-excited Induction Generator Using Pulse Width Modulation Converter with Variable DC-link Voltage

Abstract Three-phase self-excited induction generators play an important role in renewable energy sources such as wind and hydraulic energy. Their main disadvantage is poor voltage and frequency regulation under varying load and speed. This article introduces a new and simple method for voltage and frequency control of three-phase unregulated speed induction generators in the islanding mode. The method uses a constant voltage constant frequency pulse width modulation converter without regulating the DC capacitor voltage. The capacitor voltage is left changing with the loading conditions, and the AC side voltage is regulated by controlling the modulation index or the normalized d-axis reference voltage for space vector modulation. This eliminates the need for an auxiliary switch in the DC side, reducing the cost and high-frequency current components flowing in the DC capacitor and increasing its lifetime. Simulation results are given for comparing between the conventional and the proposed techniques when applied to a 6-kW generator. The proposed technique is tested under step changes in load and prime mover speed, and it gives the same response as the old technique but without the use of DC side switch. Experimental implementation of the proposed technique is tested on a 1-hp generator, and both simulation and experimental results are given to validate the proposed technique.

Adaptive Mamdani fuzzy control for a class of nonlinear time-delays systems

In this paper, an adaptive fuzzy tracking control is presented for a class of SISO nonlinear strict-feedback systems with unknown time delays. The developed control algorithm uses the Mamdani-type fuzzy systems to approximate on-line the unknown nonlinear function. The Krasovskii-functional is constructed to compensate for the unknown delayed state. The proposed controller guarantees uniform ultimate boundedness of all signals in the closed-loop system. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter, which is required to be updated on-line. Simulation results are presented to verify the effectiveness of the proposed approach.

High performance motion control of DC motors using wavelet networks

Based on the multi-resolution analysis and the wavelet transform, a wavelet network is presented for the control of DC motors. One of the basic advantages of wavelet network is that training is done using the recursive least square method which is suitable for online training usually required for adaptive control. The wavelet network is used to design adaptive speed controllers for a DC motor to achieve high performance speed control even if the motor model is unknown, the load characteristics are also unknown function of speed and the load torque changes online. Simulation and experimental results are presented to validate the proposed controllers.

Adaptive fuzzy semi-decentralized control for a class of large-scale nonlinear systems based on input-output linearization concept

Stable direct and indirect adaptive fuzzy controllers are presented for a class of interconnected nonlinear systems with unknown nonlinear subsystems and unknown nonlinear interconnections, which are represented not only in the canonical forms [1] but also in the general forms. Hybrid adaptive robust tracking control schemes which are based upon a combination of the Hinfin tracking theory, and fuzzy control design are developed such that all the states and signals are bounded and an Hinfin tracking control performance is guaranteed without imposing any constraints or assumptions about the interconnections. Extensive simulation example of two-link rigid robotics manipulator is provided to verify the effectiveness of the proposed algorithm.