In this novel control scheme, the different charging stations take the constant power from the grid throughout their operation and acts as a constant load. And most importantly increases the load fluctuations onto the main distribution grid. This causes an extra burden on the main supporting grid. An increase in the number of charging stations increases the power demand to a high rate. The increasing use of electrical vehicles (EVs) and hybrid EVs raises the need for more charging stations. This paper presents a new energy management scheme for charging stations that reduces the load demand as well as load fluctuations on main distribution grid. An Energy Management Scheme for Grid Connected EVs Charging Stations
The proposed adaptive PID controller model has done by MATLAB/SIMULINK.Ģ3.
The test results are exhibited to demonstrate that Adaptive PID controllers dependent on the extra mistake of the reverse control signal are fit for the following movement, just as diminishing the impact of parameter changes. This paper applies an Adaptive PID controller dependent on the extra blunder of a turnaround control sign to comprehend non-linearity, parameter varieties, and burden travel issues that happen in the BLDC motor drive framework. The list items test that any adaptive PID controller has favoured both the PI controller and the customary Tahitian PID controller. An adaptive PID controls can meet the highlights and are anything but difficult to enrol.
It is very hard to tune the parameters and accomplish full force highlights utilizing a basic custom PID controller. Adaptive PID Controller Using for Speed Control of the BLDC Motorīrushless DC Motor (BLDCM) is broadly utilized for some mechanical applications, given their high efficiency, high torque, and low volume. In summary, this paper demonstrated the efficiency of both controllers designed for the DC motor in terms of speed control in both simulation and hardware implementation.Ģ4. DSpace controller board is used for real-time interface of Simulink model and hardware development. IGBT is used to control the variable DC voltage and hence, achieve the target speed of the DC motor. In the hardware part, a DC chopper is used to convert fixed DC voltage from the power supply to variable DC voltage.
The software part is the development of the DC motor model with the controller to achieve the desired speed by using MATLAB/Simulink. This paper consists of two parts which are software development and hardware implementation. The switching of the DC chopper is controlled by using the pulse width modulation (PWM) technique. DC chopper is used to control the speed of the DC motor by controlling their armature voltage of the motor. The type of motor used in this project is a separately excited DC motor. Intelligent controllers have gained wide popularity in the application of control systems including DC motor speed control. This paper presents a comparative study of speed control DC motor by using two different artificial intelligent controllers which are a fuzzy logic controller (FLC) and artificial neural network (ANN). A Comparative Study of Fuzzy Logic Controller and Artificial Neural Network in Speed Control of Separately Excited DC Motor
If you have any doubts related to electrical, electronics, and computer science, then ask question. In this article, I will share MATLAB Simulink projects for electrical, electronics, and computer science students, MATLAB projects for engineers, Simulink projects, etc.