<p>Electric fault is the main challenge in the process of providing continues electric supply. Fault can occur at anytime and anywhere. Due to the fault causes are mainly based on natural disaster or accident. Most fault occurrence hardly predicted nor avoided. Therefore, a quick response fault detection is necessary to ensure that the fault area is maintained to ensure a continuous power supply system. Hence, a system is required to detect and locate the position of the fault in the power system especially in the transmission line and distribution line. This paper will review the type of fault that possibly occurs in an electric power system, the type of fault detection and location technique that are available together with the protection device that can be utilized in the power system to protect the equipment from electric fault.</p><p> </p>
This paper deals with controlling a grid-connected dual-active bridge multilevel inverter for renewable energy integration. The concept of direct power control is integrated with model predictive control algorithm, which is termed as predictive direct power control, to control the real and reactive power injected into the power grid. The proposed multilevel inverter allows more options of feasible voltage vectors for switching vector selections in order to generate multilevel outputs, and thereby obtaining high power quality in the power grid. By using the predictive direct power control, simulation results show that the proposed multilevel inverter produces lower power ripple and manage to achieve currents with low total harmonic distortion which are well within the IEEE standard. The modeling and simulation of the system are implemented and validated by MATLAB Simulink software.
The field oriented control theory and space vector pulse width modulation technique make a permanent magnet synchronous motor can achieve the performance as well as a DC motor. However, due to the nonlinearity of the permanent magnet synchronous motor drive characteristics, it is difficult to control by using conventional proportional-integral-derivative controller. By this reason in this paper an online neural network controller for the permanent magnet synchronous motor is proposed. The controller is designed to tracks variations of speed references and also during load disturbance. The effectiveness of the proposed method is verified by develop simulation model in MATLAB-simulink program. The simulation results show that the proposed controller can reduce the overshoot, settling time and rise time. It can be concluded that the performance of the controller is improved.
<p>Transmission line is the most important part of the power system. Transmission lines a principal amount of power. The requirement of power and its allegiance has grown up exponentially over the modern era, and the major role of a transmission line is to transmit electric power from the source area to the distribution network. The exploded between limited production, and a tremendous claim has grown the focus on minimizing power losses. Losses like transmission loss and also conjecture factors as like as physical losses to various technical losses, Another thing is the primary factor it has a reactive power and voltage deviation are momentous in the long-range transmission power line. In essentially, fault analysis is a very focusing issue in power system engineering to clear fault in short time and re-establish power system as quickly as possible on very minimum interruption. However, the fault detection that interrupts the transmission line is itself challenging task to investigate fault as well as improving the reliability of the system. The transmission line is susceptible given all parameters that connect the whole power system. This paper presents a review of transmission line fault detection.</p>
This paper proposed a 7-level Cascaded H-Bridge Multilevel Inverter (CHBMI) with two diffenrent controller, ie, PID and Artificial Neural Network (ANN) controller to improve the output voltage performance and achieve a lower Total Harmonic Distortion (THD). A PWM generator is connected to the 7-level CHBMI to provide switching of the MOSFET. The reference signal waveform for the PWM generator is set to be sinusoidal to obtain an ideal AC output voltage waveform from the CHBMI. By tuning the PID controller as well as the self-learning abilities of the ANN controller, switching signals towards the CHBMI can be improved. Simulation results from the general CHBMI together with the proposed PID and ANN controller based 7-level CHBMI models will be compared and discussed to verifyl the proposed ANN controller based 7-level CHBMI achieved a lower output voltage THD value with a better sinusoidal output performance.
This paper deal with the problem in speed controller for Indirect Field Oriented Control of Induction Motor. The problem cause decrease performance of Induction Motor where it widely used in high-performance applications. In order decrease the fault of speed induction motor, Takagi-Sugeno type Fuzzy logic control is used as the speed controller. For this, a model of indirect field oriented control of induction motor is built and simulating using MATLAB simulink. Secondly, error of speed and derivative error as the input and change of torque command as the output for speed control is applied in simulation. Lastly, from the simulation result overshoot is zero persent, rise time is 0.4s and settling time is 0.4s. The important data is steady state error is 0.01 percent show that the speed can follow reference speed. From that simulation result illustrate the effectiveness of the proposed approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.