This paper applied an artificial intelligence technique to control Variable Speed in a wind generator system. One of these techniques is an offline Artificial Neural Network (ANN-based system identification methodology, and applied conventional proportional-integral-derivative (PID) controller). ANN-based model predictive (MPC) and remarks linearization (NARMA-L2) controllers are designed, and employed to manipulate Variable Speed in the wind technological knowledge system. All parameters of controllers are set up by the necessities of the controller's design. The effects show a neural local (NARMA-L2) can attribute even higher than PID. The settling time, upward jab time, and most overshoot of the response of NARMA-L2 is a notable deal an awful lot less than the corresponding factors for the accepted PID controller. The conclusion from this paper can be to utilize synthetic neural networks of industrial elements and sturdy manageable to be viewed as a dependable desire to normal modeling, simulation, and manipulation methodologies. The model developed in this paper can be used offline to structure and manufacturing points of conditions monitoring, faults detection, and troubles shooting for wind generation systems.
The power quality problems can be defined as the difference between the quality of power supplied and the quality of power required. Recently a large interest has been focused on a power quality domain due to: disturbances caused by non-linear loads and Increase in number of electronic devices. Power quality measures the fitness of the electric power transmitted from generation to industrial, domestic and commercial consumers. At least 50% of power quality problems are of voltage quality type. Voltage sag is the serious power quality issues for the electric power industry and leads to the damage of sensitive equipments like, computers, programmable logic controller (PLC), adjustable speed drives (ADS). The prime goal of this paper is to investigate the performance of the Fuzzy Logic controller based DVR in reduction the power disturbances to restore the load voltage to the nominal value and reduce the THD to a permissible value which is 5% for the system less than 69Kv. The modeling and simulation of a power distribution system have been achieved using MATLABL/Simulink. Different faults conditions and power disturbances with linear and non-linear loads are created with the proposed system, which are initiated at a duration of 0.8sec and kept till 0.95sec.
In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.
Five-phase machine employment in electric drive system is expanding rapidly in many applications due to several advantages that they present when compared with their three-phase complements. Synchronous reluctance machines(SynRM) are considered as a proposed alternative to permanent magnet machine in the automotive industry because the volatilities in the permanent magnet price, and a proposed alternative for induction motor because they have no field excitation windings in the rotor, SyRM rely on high reluctance torque thus no needing for magnetic material in the structure of rotor. This paper presents dynamic simulation of five phase synchronous reluctance motor fed by five phase voltage source inverter based on mathematical modeling. Sinusoidal pulse width modulation (SPWM) technique is used to generate the pulses for inverter. The theory of reference frame has been used to transform five-phase SynRM voltage equations for simplicity and in order to eliminate the angular dependency of the inductances. The torque in terms of phase currents is then attained using the known magnetic co-energy method, then the results obtained are typical.
The increasing demand for electricity due to population expansion has led to frequent interruptions in electrical power, so there are backup power lines everywhere, especially in the sectors of education, health, banking, transportation and communications. DC sources are beginning to become widely spread in terms of low maintenance requirements, no need for refueling, and no pollutant emission in these institutions. The problems of DC systems are; losses in DC system components, and change in output voltage as loads change. This research presents a power system that generates 1760W AC power from batteries bank, the system consists of a twin inverter to reduce losses in switches and filters, and thus improving the efficiency and the power factor of the system, and fuzzy logic controllers to regulate the output voltage of the converter and inverter. Modeling and simulation in MATLAB / Simulink showed obtaining a constant load voltage with acceptable values of total harmonics distortion (THD) under different conditions of loads and batteries.