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Electromagnetic suspension technology has been developed in recent years due to advantages such as no contact and reduced friction. Of course, ensuring efficiency in these systems requires precise control of the position of the suspended object. Therefore, electromagnetic suspension is considered as a process by control engineers. The dynamics of electromagnetic suspension systems is nonlinear and also include model and parametric uncertainties such as the weight of the suspended object. In this paper, a finite time nonlinear hybrid method is used to stabilize the electromagnetic suspension system. Proof of finite time stability of the proposed method is performed using Lyapunov theory and a relation for calculating the convergence time depends on the controller gains is presented. To ensure the finite time convergence of the system state and output variables, the backstepping algorithm is used and in each step, the finite-time convergence theory is used. The controller designed in this paper is compared with the backsteping method and the superiority of the proposed method in various simulations is shown.

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In this paper, a new adaptive model predictive control based on Laguerre functions is proposed for the load-frequency control problem of a multi-area power system, in which the estimation of the internal model of the power system is updated online using the recursive least squares method. The use of the adaptive reduced-order internal model in the structure of model predictive control is the innovation of this research. In the studied system, the controller of each area is designed independently so that the stability of the overall closed-loop system is guaranteed. Numerical simulations for a three-area power system are carried out to validate the effectiveness of the proposed scheme and the results were compared with those of conventional model predictive control (MPC) and proportional-integral-derivative control (PID). The simulation results show that the proposed scheme performs better than PID and MPC in rejecting step load disturbance (with respect to nominal and uncertain parameters) and nevertheless, thanks to the use of the reduced-order model and Laguerre functions, reduces the computational burden significantly compared to conventional MPC.         
 

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This paper investigates the performance of differential protection of power transformers in the presence of internal faults, external faults, and cross-country faults in the presence of current transformers saturation, which is one of the main innovations of this study. Today, detection and discrimination of cross-country faults from other disturbances are one of the most important challenges facing protection engineers. Therefore, in this study, maximum overlap discrete wavelet transform has been used in order to accurately detect and classify these disturbances based on the extraction of energy coefficient indices of superior features. First, the cross-country faults, internal faults and external electrical faults, and inrush current phenomenon on the system under study in the EMTP software are simulated and differential current is sampled in different disturbances. Then, the mean indices of the sum of energy coefficient each level are calculated by MODWT by MATLAB software, and based on the values of indices, discrimination and classification of events are done. The results obtained from the simulations confirm that the proposed protection algorithm can detect and classify cross-country faults from other disturbances. Also, this method will improve the differential protection performance in different operating conditions and increase the reliability of power systems.

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In this paper, a new adaptive controller based on the barrier function is designed for high-order nonlinear systems with uncertainties in mind. Accordingly, this paper uses a sliding mode controller that can simultaneously create asymptotic convergence and deal with perturbations. The main problems controlling the slip mode can be considered asymptotic convergence, umbrella phenomenon, stimulus saturation, control gain estimation and failure to deal with time-varying uncertainties. In this paper, the terminal slip mode controller is used to deal with the phenomenon of asymptotic convergence and umbrella and the barrier function is used to overcome the uncertainties of time variable. The advantages of the proposed method include the elimination of the Chattering phenomenon, convergence in finite time, compatibility with time-varying uncertainties, no use of estimates and no need for information on the high limit of perturbations. Stability analysis shows that in the proposed controller, the tracking errors approach the convergence region in the zero range and provide faster convergence. Finally, to prove the efficiency of the controller, based on the chaos synchronization theory, we apply the proposed controller to a new 5D hyperchaotic system. The results show that the proposed controller, despite the disturbances applied to the system, provides rapid convergence and eliminates the umbrella phenomenon.

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Polymer electrolyte membrane fuel cells (PEMFC) have been considered by researchers due to their high efficiency, low pollution, and high-power density in distributed generation systems. In this paper, the connected PEMFC fuel cell power recovery system with an LCL filter is evaluated in the harmonic Grid. LCL filters, despite their greater ability to attenuate harmonics, can lead to system resonance and instability. In this research, a transformer has been used to connect the fuel cell inverter to the Grid and its leakage inductance has been used as the inductor on the network side. Besides, for optimal resonance damping, and attenuation of current ripple caused by grids voltage harmonics, capacitor voltage through feedback control has been used. Complete control of capacitor voltage feedback includes proportional, derivative, and second-order components. In the proposed control scheme, the capacitor voltage derivative component opposes the capacitor current feedback due to identical and symmetrical loop gain. Therefore, both of them can be deleted. Thus, the capacitor current sensor is saved. Instead, the LCL filter resonance is damped by a proportional component and a second-order derivative of the capacitor voltage. A low-pass filter is also added to the second-order derivative in the controllable frequency range to ensure system stability. The simulation results of the PEMFC power recovery system in different conditions confirm the proper attenuation of the grid-connected inverter, the injection of current of suitable quality into the contaminated and harmonic grid, the stability, and the appropriate dynamic response of the proposed system.
 

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Brushless direct current motors have been considered by many researchers due to their many advantages over other direct current motors. The upper band is unknown and aims to converge in a limited time. For this purpose, by rewriting the motor model in the presence of modeling uncertainties and limited external perturbations, the terminal slip mode control law is designed to stabilize the system and converge the output speed and motor flow to the desired values ​​in a finite time, using the development theorem. In this control law, the high bandwidth of total uncertainties and external disturbances is estimated online using an adaptive law. Finally, while calculating the system convergence finite time, an idea to reduce the umbrella in using the terminal slip mode control is presented. The results of the numerical simulations performed show the accuracy of the designed controllers.

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Today, electricity service providers have to consider economic index as well as social indicators and consumer perspectives. In this paper, by studying the consumer satisfaction function, a new load model based on nonlinear consumer perspective has been developed. The main variable in the proposed model is the coefficient of individual consumer behavior, which requires field and statistical activities to estimate. As an alternative solution in this paper, the relationship between demand elasticity and individual behavior coefficients has been mathematically developed to estimate these coefficients through demand elasticity information. Also, a new demand response program based on individual behavior and satisfaction function has been developed to determine the incentive rate for consumer participation in an optimal way based on their individual perspective. In the numerical results section, the data of Iran's electricity network has been selected for study. Numerical results showed that in order to study the annual consumption change, in addition to increasing prices, inflation rates and increasing consumer incomes also need to be examined. It was also shown that for the Iranian electricity grid, the demand response program implementation for the household consumers has the lowest cost. However, in order to achieve maximum welfare, it is more appropriate these programs have been applied to the public customers.
 

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Nowadays, sleep deprivation is a pervasive problem that affects human physical and mental health. In this research, the effects of sleep deprivation on brain function and its diagnosis have been studied using electroencephalogram (EEG) signals recorded from 30 subjects after complete sleep and one day of sleep deprivation with open and closed eyes. Linear features like signal power and nonlinear features consisting of Shannon, Renyi, sample, and permutation entropies were extracted from signals. We used the PCA algorithm and Wilcoxon feature ranking method to extract the superior features and employed SVM, KNN, and a Decision tree to detect sleep-deprived cases. Brain maps of extracted features were plotted using the sLORETA algorithm to investigate the effects of sleep deprivation. Based on the results, the decision tree classifier with 100 superior selected features of Wilcoxon achieved the best performance with accuracy and precision of 99.0% and 99.8%, respectively. Also, comparing the results of linear and nonlinear features reveals the impressive role of the nonlinear features in the classification problem of this work. The maps of the features revealed noticeable changes in the level of attention, concentration, decision-making, and visual and movement activities.

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Currently, renewable energy is rapidly developing across the world in response to technical, economic, and environmental developments, as well as political and social initiatives. Moreover, the excessive penetration of distributed generation (DG) systems into electrical networks may lead to various problems and operational limit violations, such as over and under voltages, excessive line losses, overloading of transformers and feeders, protection failure and high harmonic distortion levels exceeding the limits of international standards. These problems occur when the system exceeds its Hosting Capacity (HC) limit. The HC is a transactive approach that provides a way for the distribution network to be integrated with different types of energy systems.
Distributed Generation (DG) sources are one of the important componeents of novel distribution systems, among which renewable and clean energy sources have received more attention due to the important role of these sources in reducing greenhouse pollutants.
The use of renewable sources such as wind and photovoltaic sources is expanding day by day with the increasing demand for electric energy supply. However, the limitations in the amount of the penetration of DG resources are one of the main challenges in the development of the use of these resources.
This paper is looking for a method to improve the HC of the distribution network from DG sources by using reactive power compensation and the reconfiguration of distribution systems. The results of the simulation show the advantages of using the proposed method in increasing the HC and as a result the development of the use of renewable resources.
 

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In an islanded microgrid, power electronic converters are used to exchange power, and these converters have very low inertia, thus compromising the frequency stability of the microgrid. Virtual inertia control is used to improve the frequency stability of an islanded microgrid. The derivative control technique is usually used to implement virtual inertia control in the microgrid. Factors such as disturbance and uncertainty of parameters of the islanded microgrid compromise the performance of virtual inertia control and may cause system frequency instability. Therefore, the virtual inertia control structure, a complementary controller is needed that can weaken the effect of disturbance on the microgrid as much as possible and be resistant to the uncertainty of parameters of the microgrid. In this paper, a robust control method is used in a virtual inertia control structure that uses system output feedback. The proposed method is expressed based on linear matrix inequality and is proved based on the Lyapunov criterion. Among the advantages of the proposed method is the attenuation of disturbance, resistance to the uncertainty of parameters of the microgrid, and increasing the degree of freedom to control the system in this method. The results of the proposed method to improve the performance of virtual inertia control in several different scenarios by considering the uncertainty of parameters of the two-zone microgrid and disturbances on the microgrid are compared with several methods and the effectiveness of the proposed method in terms of improving frequency stability is shown.

 

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The control of unmanned aerial vehicles is a challenging problem due to their lightweight and intense coupling between longitudinal and lateral motion. Considering this issue, in this article, an automatic landing system for a fixed-wing unmanned aircraft exposed to wind disturbances and parametric uncertainties is designed using the backstepping algorithm and the disturbance observer-based sliding mode control. Two controllers are designed based on the backstepping algorithm and sliding mode control to stabilize the attitude angles. The longitudinal speed controller uses the sliding mode technique to maintain the total speed relative to the ground at a constant desired value in all landing phases. A nonlinear disturbance-observer is considered in the sliding mode controller structure to estimate wind disturbance and parametric uncertainty. The new robust automatic landing system is software implemented, and its performance is investigated by several numerical simulations; Lateral deviation relative to the runway is eliminated while the unmanned aerial vehicle maintains its desired trajectory slope angle in all phases of the landing at the desired value. Therefore, the results of numerical simulations prove that the new control structure is stable and robust against different initial conditions, different types of wind disturbances (wind shear and discrete gust), and parametric uncertainty.

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In order to reduce the cost of electric power generation and also reduce greenhouse gas emissions in ships with electric propulsion system, renewable energy resources and energy storage systems are used along with thermal units. Therefore, in this paper, a stochastic mixed integer linear model has been suggested for optimal management of electrical energy of a ship with electric propulsion system, energy storage, heat generators and renewable solar resources with the aim of minimizing the cost of electricity production and determining the optimal ship speed. In this paper, the Monte Carlo simulation method is used to model the uncertainty in predicting the solar power and the ship's electric load. The proposed model is implemented and analyzed in GAMS optimization software. The simulation results show the efficiency of the proposed model and the introduced energy management strategy.
 

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Extracting maximum power, especially with partial shading conditions, is one of the most critical issues in using a photovoltaic system. Under partial shading conditions, the power-voltage characteristic of photovoltaic arrays has several local maximum points. A maximum power point tracking method for photovoltaic systems should enable fast and accurate tracking of the global maximum during partial shading conditions to minimize power losses and steady-state fluctuations. This research presents an algorithm for tracking the maximum power point in a photovoltaic system under partial shading conditions using the gray wolf optimization technique. The gray wolf algorithm is a new optimization method that overcomes limitations such as poor tracking, steady-state fluctuations, and undesirable transients in perturb and observe and particle swarm optimization techniques. The proposed algorithm based on the gray wolf optimization algorithm is implemented on a photovoltaic system in MATLAB software to prove its efficiency. The performance of the proposed design is compared with two maximum power point tracking techniques based on cuckoo search and particle swarm optimization. The simulation results show that the performance of the proposed maximum power point tracking technique is superior to the compared designs in terms of speed and steady-state stability of the response, so that it reduces the values of maximum overshoot, settling time, and sustained fluctuations up to 40.91%, 66.67% and 59.1% respectively.
 

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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.

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Insufficient training data is one of the main challenges of utilizing deep Convolutional Neural Networks (CNNs) for Environmental Sound Classification (ESC). As a promising solution, Transfer Learning (TL) has addressed this issue by adapting a network pre-trained on a large-scale dataset to the target task. In this paper, we demonstrate that not all neurons/kernels of every layer in CNN networks are equally utilized to process the inputs of different classes, but there is a specific subgroups of neurons/kernels in every layer that play the key role in classification of every output class. Based on this observation and due to similarities that exist between feature spaces of some source and target classes, we propose to concentrate the fine-tuning process only on those neurons/kernels that do need changes and have the greatest impact on misclassifying target data. To identify these neurons/kernels, we pose a nested optimization problem for which we propose an effective evolutionary approach as solution.  Compared to the conventional fine-tuning approach, our proposed method achieves absolute improvements of about 1.9% and 2.3% in accuracy on ESC-50 and DCASE-17, respectively; remarkable improvements produced not by adding augmented data but with a more efficient utilization of knowledge stored in the pre-trained network. It is noteworthy that the computation time overhead of the proposed evolutionary method is rather small (about one third of the time required to train the model from scratch.

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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.

 

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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.

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Usually, constrained lateral acceleration would have undesirable effects on the stability and performance of a guidance system. The composite nonlinear feedback (CNF) can be effectively used to improve the transient response of the closed-loop system in the presence of the constrained input. In this way, guidance law consists of an extra nonlinear term besides the conventional linear one. As a result, such a term adjusts the qualitative characteristics of the transient response. Meanwhile, the nonlinear term is a function of the rate of line-of-sight (LOS) angle which is not activated at origin and infinity. Thus it would be effective only in a specified region. In this paper, proportional navigation is employed for the linear term of the CNF-based guidance law. Therefore, a guidance algorithm is developed for tracking problems using the CNF idea. Applying the proposed guidance method, the closed-loop stability is analytically proved via the well-known Lyapunov stability theory. The suggested approach is simulated in a numerical example. Then the results are compared with an existing technique. As expected, guaranteeing closed-loop stability, in contrast to a similar method, the addressing scheme considerably improves the performance and transient response of the guidance system in the presence of lateral acceleration limitations.