نشریه مهندسی مخابرات جنوب
پیاپی 41 (پاییز 1400)

Brushless motors are widely used in industrial, domestic and electronic equipment today due to their high reliability, high efficiency, low maintenance and many other advantages. But they also have disadvantages, including electronic commutation, and this requires a speed controller (speed) for this type of engine. In recent decades, a large number of speed controllers have been designed to control the speed of brushless motors. Typically, a derivative-integral-proportional controller is the optimal choice for controlling the speed of brushless motors. By designing the parameters of the derivative-integral-proportional control system, the speed of the brushless motors can be controlled. There are many ways to obtain the optimal derivative-integral-proportional control parameters. One of the methods that has been widely used to obtain and design derivative-integral-proportional control parameters are optimization algorithms. Water cycle optimization algorithm is an optimization algorithm that is used to optimize derivative-integral-proportional control parameters. In this paper, the derivative coefficient equal to 0, the integral coefficient equal to 0.2259937 and the proportional coefficient equal to 0.00188894 are obtained which gives the stability index equal to 0.01038433, the rise time equal to0.00962 , settling time equal to 0.01492 , peak time equal to 0.01817 , settling min equal to 0.9059. These results are compared with the results obtained from particle swarm algorithms and genetics and show that the water cycle method is better.

Analog to digital converters are divided into two categories, Nyquist rate converter and oversampling converter, in terms of sampling frequency. At oversampling converter, input signal are sampled at several times the Nyquist rate. Increasing the over sampling rate leads to an increase in effective resolution, but although the use of high sampling rate for wide band signals is impractical due to the need for high sampling frequency and power consumption. Increasing the number of bits also increases the dynamic range as opposed to the multi-bit DAC required in the linear feedback path. In this paper, we designed sigma delta structure with 12 bit resolution, 1-v power supply for low power applications. on the other hand, increasing levels of quantization(number of bits) causes the reduction in-band noise power of the system, as well as the modulator stability improves without need increasing oversampling ratio. The maximum value of a signal-to-noise-and-distortion ratio(SNDR) will be achieved by means of choosing appropriate feedback coefficients. Simulation results of a 12-bit,2.4-MS/s , and 1-v proposed structure in a 0.18-µm CMOS technology show a signal-to-noise-and-distortion ratio (SNDR) of 71.3 dB, a power consumption of 451µW, and figure of merit 3.76(pJ/Conver.step.)

Recently the use of AC transmission system (FACTS) devices and distributed generation resources as technology in power and distribution systems is increasing. This equipment affects various parameters such as voltage profile, line losses, short circuit current, stability, and reliability of the system, and therefore determining the optimal installation location, their number and size are one of the important issues that are considered because the installation of these devices and Resources in non-optimal locations increase system losses and negatively affect voltage profiles and other system parameters. In this paper, the simultaneous optimization of the number, location, and size of distributed generation resources and static synchronous compensation is used and in order to solve the optimization problem, a genetic algorithm (GA) is used. For this purpose, a multi-objective function including operating costs and generation of distributed generation resources and static synchronous compensation and system load capacity are presented and the simulation results were analyzed for two 33 and 69 IEEE standard networks. The results show that with increasing system load, the cost increases because the number of equipment related to distributed generation sources and static synchronous compensator increases. Also, the simultaneous optimization and placement of this equipment reduces costs and increases the load capacity of the distribution system.

Transmission network development is one of the most important parts of the power system that determines the optimal configuration for the network based on load demand. Expansion of transmission lines is not always cost effective; For this reason, with the growth of demand and shortage of energy resources, Planning of Demand Response has received special attention. In this paper, the role of microgrids as an alternative solution for transmission network development is investigated and a proposed method for modeling microgrids in the capacity market is presented as a solution to the transmission network development planning problem. The results show that the addition of microgrids causes reduction in transmission network development costs. In addition, the addition of microgrids causes a slight increase in the profitability of selling capacity to consumers. These results prove that by continuing to grow the number and capacity of microgrids in each area, it is possible to ultimately eliminate the need for transmission network development and reduce the market cost of capacity to zero.

Discrete control of the robot manipulators with uncertain model is the purpose of this paper. The proposed control design is model-free by employing an adaptive fuzzy estimator in the controller for the estimation of uncertainty as unknown function. An adaptive mechanism is proposed in order to overcome uncertainties. Parameters of the fuzzy estimator are adapted to minimize the estimation error using a novel gradient descent algorithm. The proposed model-free discrete control is robust against all uncertainties associated with the robot manipulator and actuators including model’s uncertainty and external disturbances. The most gradient descent algorithms have used a known cost function based on the tracking error for adaptation whereas the proposed algorithm has proposed a cost function based on the uncertainty estimation error. Then, the uncertainty estimation error is calculated from the joint position error and its derivative using the closed-loop system. Most control algorithms require all state variable feedback to ensuring stability for the robot manipulators. Practical implementation of this control method is easy because it has a decentralized structure and measures only from the joint position. The simulation results confirm the correct operation of this method and we will prove the stability of the control system.

The purpose of the economic load distribution problem is to find a suitable design for the output of power plants in order to supply the consumption load in a certain time horizon, so that it ensures that the proposed design will meet the load demand at an acceptable level of reliability. With the advent of energy management and power consumption methods, the load growth trend has decreased, but there is still a need to optimize the issues of optimal operation in the production, transmission and distribution networks to supply and transfer power from producers to consumers. Therefore, solving the problem of economic load distribution in an optimal way and at an acceptable level of reliability is essential. The main purpose of this issue is to reduce operating costs. Operating costs include the cost of operating power plants and maintaining the network. In addition to cost, there are other goals such as network reliability that have not been carefully and seriously examined, so this article examines these goals. In this paper, a safe multi-objective optimization framework for dispatching power plants is presented. Cost minimization, redistributed energy were considered as optimization objective functions.