جستجوی مقالات مرتبط با کلیدواژه « کنترل پیش بین » در نشریات گروه « برق »

In this paper, an extension for Data-Driven Model Predictive Control for Linear Parameter Varying systems is presented. Model-based controllers are highly dependent on model precision. On the other hand, data-driven methods are either replaced with the model or import its dynamic data into the design. Throughout this paper, direct data-driven approaches, which have gained considerable attention in recent years, are used in designing different parts of the controller, including future predictions. In addition, the stability and recursive feasibility guarantees are presented as the first novelty of this research with respect to a prior platform for data-driven approach. Furthermore, the base platform of direct DD-MPC for LPV systems is extended. The new developed form with the goal of robustness against measurement noise is defined as the next novelty of this paper. In order to check the performance of the proposed method, simulations on DC motor are applied. The results show effectiveness of the proposed approach as compared with similar approaches reported in the literature.

One of the important factors that are always discussed in the study of power systems is the issue of power system stability. In this paper, the load angle control of a three-phase synchronous steam power plant generator is presented by overcoming the load angle fluctuations in both transient and permanent system conditions and expanding the range of stable generator performance. First, the equations of the linear state space of the steam power plant around the operating point are extracted. Then, based on Hankel single values, the model is approximated to the 6th order and based on that, a proportional, derivative, and integral controller for the load angle is presented. Then, using the arithmetic optimization algorithm, the controller coefficients are adjusted with two approaches reducing the settling time and reducing the overshoot of the step response. The stability of the proposed controller output power was also used using the stability criteria Bode and Nyquist was examined. The simulation results show that the arithmetic optimization algorithm has a better performance in controlling the load angle of the three-phase synchronous generator than the fuzzy controller and genetic and harris hawks algorithms. For example, in comparison with the genetic algorithm, the output power has good stability, and the rise time, settling time and the amount of overshoot in the step response of the proposed algorithm is reduced by 82.97, 82, and 40.18%, respectively.

In this paper, the improvement of low frequency oscillation (LFO) damping in a power system including SVC is investigated. To achieve this goal, a new control strategy has been presented in which the multi-model controller is optimized using the linear optimal controller (LOC) and the particle swarm algorithm (PSO). The control bank in the multi-model controller includes three LOC controllers that generate optimal signals through the linearization of the nonlinear equations of the system and the minimization of an energy function to be combined by the Bayes recursive algorithm simultaneously to the generator excitation system and SVC. In order to generate an optimal linear signal, Riccati's equation must be solved; Riccati's equation includes two weight matrices Rric and Qric. These matrices elements are optimized by PSO algorithm. The PSO algorithm has calculated the optimal Rric and Qric with two different objective functions of maximizing the eigenvalues and minimizing the area under the speed curve. To evaluate the MMC-LOC-PSO control strategy, the symmetrical three-phase error is applied to the worst bus and the results of these two objective functions are compared. The simulation of the single machine power system has been done by MATLAB. The proposed control strategy, while maintaining stability, also effectively damps the LFOs, in addition, the permanent rotor speed and rotor angle error have also been favorably pushed to zero.

In recent years, the rate of natural disasters has increased significantly due to global warming. One of the most frequent natural disasters is the storm that threatens the security of distribution systems and in many cases leads to major outages in the network. Preventive measures in case of any disturbance and production planning in the post-event phase are two important measures to deal with emergencies.Therefore, this paper presents a framework for increasing the flexibility of the distribution system, where, in addition to preventive measures, production scheduling is performed with the possibility of network reconfiguration. The proposed framework is modeled as a three-stage optimization problem, where photovoltaic panels (PV), electric energy storage systems (EES), and load priority are considered. In the first step, the optimal placement of the remote control switches is performed to minimize the expected energy not served (EENS). It should be noted that in addition to automatic switches, the network is equipped with manual switches (on-site control). In the second stage, the prepositioning of crew teams (to alter the status of manual ‎switches) is performed. Finally, in the third stage, repairs are done after the incident. The proposed model is implemented on a 69-bus distribution system. The results demonstrate that the dynamic topology and MEGs have reduced 10.04% and 64.05% of the forced load shedding, respectively. Also, the results confirm that implementing the direct load control (DLC) program has led to a 10.34% reduction in forced load shedding at the critical loads.

Disturbance and uncertaities exist in industrial systems and greatly affect the performance and stability of these systems. The robotic manipulator is one the most widely used devices in the industry that is highly affected by various disturbances. Hence establishing a proper control algorithm to estimate and eliminate disturbances seems crucial. Since the robotic manipulator is a highly nonlinear system, we need to design a nonlinear disturbance observer. In this thesis a nonlinear disturbance observer is proposed to estimate the constant and oscillatory disturbances in the studied system. On the other hand, since proportional-derivative controllers (PD) are widely used in industrial systems, so in this thesis, a suitable proportional derivative controller will be designed. This controller is not capable of dealing with disturbances and uncertainties, so a new supervisory controller structure has been proposed to estimate disturbances and stabilize the system. The core of proposed controller uses a new sliding model controller. Finally, some comparisions with PD and super twisting sliding mode controllers have been performed in several cases and the numerical results show the advantages of the proposed controller.

In this paper, formation control based on the virtual structure for the non-holonomic mobile robot system with two models of certain and uncertain kinematic equations is discussed. First, the formation equations of a certain model are calculated and then it is proved that it is possible to create a geometric shape and maintain that state by using the sliding model control theory for any two moving mobile robots. Then, after deriving the formation equations of the uncertain model, a sliding model controller is designed that is able to control the uncertain model provided that the uncertainty range of the kinematic equation is present. For each design, the stability of the system is guaranteed using the Lyapunov stability theorem. Finally, in order to compare the performance of the designed controllers, a pre-designed back-stepping controller is introduced and the results will be presented in the form of simulations. The simulation results show the effective performance of the designed controllers.

The issue of frequency control is very important in the power system. The presence of wind turbine in the power system makes frequency control challenging. In order to improve the frequency control of the power system in the presence of wind turbine, in this paper, a new control method is designed. In this method, the coordinated control of load-frequency control (LFC) system and superconducting magnetic energy storage (SMES) has been discussed using PD-FOPID cascade controller. The PD member in this type of controller responds to the frequency changes of the power system faster and also the FOPID member has a favorable performance against the uncertainty of the system parameters and disturbances. In this paper, the problem of owl search algorithm is solved. Considering that the owl search algorithm may get stuck in the local optimum. In this paper, solutions are presented to solve this problem of the owl search algorithm, which is called the developed owl search algorithm, and in order to improve the performance of the PD-FOPID controller, the developed owl search algorithm is used to optimally adjust its parameters. . The proposed control method with several methods including: Load frequency control (LFC) and superconducting magnetic energy storage (SMES) based on the robust controller, LFC and SMES based on the MSA-PID controller, LFC based on the MSA-PID controller with SMES and LFC based on the MSA-PID controller without SMES has been compared in four scenarios and the results show the superiority of the proposed method over the other mentioned methods. Is. The proposed method is resistant to load disturbances, disturbances caused by wind turbines, and uncertainty related to system parameters.

Brushless dc motors (BLDC) are widely used in industrial applications due to their simple structure, high efficiency and long lifetime. The drive of these motors also has a fast transient response and has high quality waveforms in steady state. In this paper, the direct power control using the model predictive method with finite control set (DP-FCS-MPC) is presented in BLDC motor drive and compared with the conventional current control method based on FCS-MPC. This comparison is made under the same operating conditions and includes the steady state operation of the BLDC motor. The simulations performed in PLECS software show the performance of both methods in BLDC motor speed control under sudden load changes. Nevertheless, it is shown that the direct power control using model predictive method with finite control set has better performance in terms of torque ripple reduction, less speed and torque fluctuations, less active and reactive power ripple, and current waveforms with less harmonic distortions.

In this paper, a model-free nonlinear architecture is presented to control a fixed-wing UAV. This architecture has inner and outer loops. The inner loops, which are designed based on convolutional neural networks, control the internal dynamics of the aircraft in a model-free procedure. The outer loops, which use conventional linear controllers, are designed to control the kinematics of the UAV. The neural networks used to control the inner loops are trained offline based on databases to avoid time-consuming online learning processes. These databases are created by simulating simple training models. Then, the input-output data of these training models are pre-processed and mapped to image frames so that they can be given as input to convolutional neural networks. After that, a suitable network structure is selected and the networks are trained based on the mapped databases. These trained networks, together with cascaded linear controllers, are applied to the nonlinear simulation of the fixed-wing UAV and its performance is investigated. The inner loop of the controller that controls the internal dynamics of the UAV has been applied in both single-stage and two-stage forms and their performance has been compared.

To deal with the challenge of the level of penetration of renewable energy sources against uncertainties and in order to develop the secondary load frequency control loop, with the aim of reducing the impact of disturbances and adjusting the resistant gain coefficient to uncertainties, using a virtual inertial controller based on the resistant control of the Mu-synthesis method. For the lack of inertia in the islanded microgrid clusters, alternating current has been done. The stability and performance of this method is proved based on the structured singular value. The experimental results of the hardware in the loop by the TMS320F2812 digital signal processor and the simulation in the MATLAB/SIMULINK software environment confirm the performance of the proposed controller for the development of traditional load frequency control in comparison with the enhanced virtual inertial control and inertialess mode, under various disturbances, that the application of the controller The virtual inertia based on Mu-synthesis improves the stability of islanded microgrid clusters and damping of power fluctuations, and also reduces frequency deviations to a significant extent.

Due to renewable generation increment in power systems and the complexity of control and protection of them, the evaluation of   transient stability is necessary. This paper aims at analyzing the impact of wind power generation on the transient stability of power systems. To this end, doubly fed induction generator (DFIG) converters were first investigated in a closed-loop control mode. More detailed controls were used for the rotor side converter. Controls of the DC link converter on the network side were assumed to be ideal. Then, through using Gershgorin's theory, a new approach to adjust converter control parameters was presented. The simulation results in different operating modes confirmed the capability of this method in adjusting the converter’s control parameters. Also, by using performed simulations, the effect of increasing wind power production penetration and the changes of reactive power compensation by DFIG on the transient stability of power system were investigated. Finally, the effect of power system strength and DFIG parameters change, in the presence of DFIG-based wind power generation, on transient stability was analyzed. The simulation results showed that an increase in wind power generation initially led to an improvement and finally to the weakening of the transient stability of the system. In fact, this paper provides a professional and valuable perspective towards the investors and operators of the power systems with wind power generation from the viewpoint of transient stability.