This research has tried to achieve a new robust controller with back stepping control and sliding mode control method. Also as we know, in all analytical controllers there are constant coefficients like the back stepping and sliding mode controllers, redesigning the Lyapunov and the feedback linearization,-∞ and so forth. There are two major problems in their set: firstly, the adjustment is cumbersome and time-consuming. Secondly, assuming that these parameters can be adjusted to workability, a designer can never tell exactly what are the parameters chosen to be optimal. To resolve this problem, the numerical algorithm which is a genetic algorithm is used here and we have the optimal parameters of the proposed controller. That genetic algorithm (GA) has been used to solve difficult engineering problems that are complex and difficult to solve by conventional optimization methods, and at the end of this section, we apply a new robust controller on ball and beam system. Simulation results are expressed.
Nowadays, there is a need for charging electric vehicles (EVs) wirelessly, since it provides a more convenient, reliable, and safer charging option for the EV customers. A wireless charging system using a double-sided LCC compensation topology is proven to be highly efficient. In this paper, considering the importance of wireless charging of electric vehicles, using a circuit equivalent to the wireless power Transfer (WPT) system, the behavior of electric vehicles in the power grid only in technical analysis is investigated. Accordingly, two different scenarios have been used in modeling and simulation. In the first scenario, the charge of electric vehicles was controlled using an [Formula: see text] controller to stabilize the charging frequency. The model predictive control (MPC) controller was used to identify the optimum power factor for electric vehicles and determine the optimal power values and efficiency. In the second scenario, a two-way AC-DC converter was designed using a Fuzzy type-II controller under uncertainty to provide precise control over the vehicle charge exchange in terms of voltage, current, and stage of control (SOC). So, type-II fuzzy controller is used to control the process of charging and discharging vehicles. The results show that the final efficiency is 0.98, and the power will be 1480 W.
Recently, the interest in using electric and environmentally friendly vehicles has risen increasingly. The most important challenge is charging these electric vehicles (EVs) at the right time based on the amount of charging demand, which if not paid attention to, can affect the network power profile. In this article, the effect of charging EVs in fast charging stations on the voltage profile and power factor in the IEEE Bus‐33 standard network is investigated. Therefore, a mathematical model is used to model the problem that the parameters of EV arrival time to charging stations, EV charging time, number of charging times, EV status of charging (SOC), distance traveled by the EV are considered as model variables. In this article, four charging stations are considered for each fast‐charging station. The power rate that each station in the fast‐charging station receives from the network is considered 200 kW. So, if all stations are charged simultaneously, the charging station will receive 800 kW of electricity from the grid. There are also 300 EVs in the network, each charging time in fast charging stations is less than 10 minutes. To control the charge and power of cars, the FOPID controller has been used to determine the optimal coefficients through the application of a genetic algorithm (GA). The simulation results showed that in the uncontrolled reactive power mode, the voltage range in the busbars to which the charging stations are connected is less than the standard value (0.95 p.u) in the interval, but using FOPID‐GA controller, we will be able to increase the voltage range and improve the power factor of the distribution system about 10.26% by controlling the reactive power of the vehicles.
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