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Go to Editorial ManagerThe continuously ever-growing demand for the electrical power causing the continuous expansion and complexity of power systems, environmental and economic factors forcing the system to work near the critical limits of stability, so research's stability have become research areas worthy of attention in the resent day. The present work includes two phases: The first one is to determine the Voltage Stability Index for the more insensitive load bus to the voltage collapse in an interconnected power system using fast analyzed method based on separate voltage and current for PQ buses from these of PV buses, while the second phase is to suggested a simulated optimization technique for optimal voltage stability profile all around the power system. The optimization technique is used to adjust the control variables elements: Generator voltage magnitude, active power of PV buses, VAR of shunt capacitor banks and the position of transformers tap with satisfied the limit of the state variables (load voltages, generator reactive power and the active power of the slack bus). These control variables are main effect on the voltage stability profile to reach the peak prospect voltage stable loading with acceptable voltage profile. An optimized voltage collapse based on Particle Swarm Optimization has been tested on both of the IEEE 6 bus system and the Iraqi Extra High Voltage 400 kV Grid 28 bus . To ensure the effectiveness of the optimization technique a comparison between the stability indexes for load buses before and after technical application are presented. Simulation results have been executed using Matlab software). Keyword: Voltage Stability Indicator; voltage collapse; Stability of Extra High Voltage Grid; PSO optimization technique.
The distribution network suffers from low voltage problems, low frequency, and rising power losses greater than transmission systems. Load shedding is one solution to these challenges and is widely regarded as the last choice for avoiding voltage collapse and outages caused by significant disturbances. The conventional approach to load shedding reduces loads without regard for their significance until the voltage of the network is enhanced. Shedding loads without taking priority into account will cause power interruptions in critical facilities. In this paper, PSO-ANN algorithm-based load shedding to improve the voltage and frequency of distribution networks. Furthermore, a multi-objective function is developed that takes into account the linear static voltage stability margin (VSM) and the amount of load reduction. The aim of the work is to obtain the optimal level of voltage stability and remaining load when implementing load shedding while maintaining the load priority of each bus in the distribution network. Using MATLAB software requirements, the proposed technique has been implemented for two scenarios (overload, line disconnection) of the IEEE 33 bus system. The results showed that the proposed technique is the most distinctive compared to the results of the voltage sensitivity method and the conventional approach.
Voltage instability problems in power system is an important issue that should be taken into consideration during the planning and operation stages of modern power system networks. The system operators always need to know when and where the voltage stability problem can occur in order to apply suitable action to avoid unexpected results. In this paper, a study has been conducted to identify the weakest bus in the power system based on multi-variable control, modal analysis, and Singular Value Decomposition (SVD) techniques for both static and dynamic voltage stability analysis. A typical IEEE 3- machine, 9-bus test power system is used to validate these techniques, for which the test results are presented and discussed.
In this paper, a combined RBF neural network sliding mode control and PD adaptive tracking controller is proposed for controlling the directional heading course of a ship. Due to the high nonlinearity and uncertainty of the ship dynamics as well as the effect of wave disturbances a performance evaluation and ship controller design is stay difficult task. The Neural network used for adaptively learn the uncertain dynamics bounds of the ship and their output used as part of the control law moreover the PD term is used to reduce the effect of the approximation error inherited in the RBF networks. The stability of the system with the combined control law guaranteed through Lyapunov analysis. Numeric simulation results confirm the proposed controller provide good system stability and convergence.
With the aim of enhancing the small signal stability of electric power systems, the present paper evaluated and compared some power system stabilizers (PSSs). The dilemma of small signal instability is avoided by equipping the generator’s automatic voltage regulator (AVR) with a backup controller known as a PSS. Conventional PSS operates with acceptable efficiency when designed to suit specific operating conditions, but there are limitations and drawbacks that arise when disturbances lead to fluctuation in system parameters. Strengthening the design methodology for PSS in the face of these limitations is achieved by adopting artificial intelligence. This research presents a fuzzy, neural system-based approach to the development of PSS. The Adaptive Network Based Fuzzy Inference System (ANFIS) is used to design the Fuzzy Neural Power Systems stabilizer (FNPSS) . ANFIS eliminates the disadvantages of using fuzzy logic and neural networks independently in PSS design. The single machine infinite bus (SMIB) power system was used as a case study to evaluate the effectiveness of the proposed methodology. Additionally, the study includes root locus scheme for loop of voltage regulation by utilizing proportional Integral controller, P-I controller, a widely used traditional linear design technique, for comparison. The simulation results confirm the effectiveness of the method, demonstrating the superiority of the ANFIS design method over other PSS designs. MATLAB, along with Control System Toolbox and SIMULINK, is used for simulation and design.
In this work, the phase lock loop PLL-based controller has been adopted for tracking the resonant frequency to achieve maximum power transfer between the power source and the resonant load. The soft switching approach has been obtained to reduce switching losses and improve the overall efficiency of the induction heating system. The jury’s stability test has been used to evaluate the system’s stability. In this article, a multilevel inverter has been used with a series resonant load for an induction heating system to clarify the effectiveness of using it over the conventional full-bridge inverter used for induction heating purposes. Reduced switches five-level inverter has been implemented to minimize switching losses, the number of drive circuits, and the control circuit’s complexity. A comparison has been made between the conventional induction heating system with full bridge inverter and the induction heating system with five level inverter in terms of overall efficiency and total harmonic distortion THD. MATLAB/ SIMULINK has been used for modeling and analysis. The mathematical analysis associated with simulation results shows that the proposed topology and control system performs well.
In electrical power plants, the excitation control system is an important part of controlling the output voltage of the synchronous generators. The purpose of this paper is to utilize various methods of excitation control, such as Proportional-Integral-Derivative (PID), Simulated Annealing (SA), and Neural Network (NN) controllers. Each method is examined in terms of its effectiveness in enhancing system stability, reliability, and adaptability to varying operational conditions. The study simulates and optimizes a 2 MVA/400 V synchronous generator driven by a three-phase diesel engine with mechanical coupling and an exciter system. MATLAB 2021 is used to implement the Simulink model. The dynamic responses of field voltage and field current to load changes were analyzed for each control technique. Additionally, the performance of three-phase voltage and current for synchronous generator were examined over a 10-second timeframe. Our findings indicate that the PID controller offers straightforward implementation and reliable performance under varying conditions. The NN controller implementation is more similar to the PID response, and the SA controller demonstrates superior adaptability. The research underscores the potential of integrating these advanced control techniques in synchronous generators, paving the way for enhanced stability and reliability in modern electric power systems, with further implications for renewable energy integration.
This paper presents a proposed configuration of paralleling scheme PWM DC/DC buck converter. The topological structure and operation principles are presented. A Bode plot diagram technique is used to study the stability of the scheme for different values of controller parameters and with a number of parallel modules. It is found that the results are confidence, and the proposed scheme can be used in high power applications by increasing the number of parallel modules.
In this paper, a model of PI-speed control current-driven induction motor based on indirect field oriented control (IFOC) is addressed. To assess the complex dynamics of a system, different dynamical properties, such as stability of equilibrium points, bifurcation diagrams, Lyapunov exponents spectrum, and phase portraits are characterized. It is found that the induction motor model exhibits chaotic behaviors when its parameters fall into a certain region. Small variations of PI parameters and load torque affect the dynamics and stability of this electric machine. A chaotic attractor has been observed and the speed of the motor oscillates chaotically. Numerical simulation results are validating the theoretical analysis.
The aim of this paper is to suggest a methodical smooth control method for improving the stability of two wheeled self-balancing robot under effect disturbance. To promote the stability of the robot, the design of linear quadratic regulator using particle swarm optimization (PSO) method and adaptive particle swarm optimization (APSO). The computation of optimal multivariable feedback control is traditionally by LQR approach by Riccati equation. Regrettably, the method as yet has a trial and error approach when selecting parameters, particularly tuning the Q and R elements of the weight matrices. Therefore, an intelligent numerical method to solve this problem is suggested by depending PSO and APSO algorithm. To appraise the effectiveness of the suggested method, The Simulation result displays that the numerical method makes the system stable and minimizes processing time.
An essential component of every RF system’s reception chain is the Low-Noise Amplifier(LNA). The sensitivity and performance of subsequent stages in the receiver chain are significantly influenced by the LNA, which is the initial step. Creating an LNA requires carefully balancing trade-offs in order to have the best possible performance in terms of gain and noise characteristics. Achieving optimal functioning and efficiency in the radio frequency system requires finding the correct balance. This article presents the design of an LNA circuit at the lowest cost without adding components such as inductors, active components, or several stages, which increase the complexity of the circuit, consume power, and add additional noise, by controlling the lengths of the microstrip line, LNA circuit was created by ADS software, and add a matching circuit. At the operating frequency of 2.4 GHz, the suggested design achieved good results with a gain of 17.48dB, NF of 0.7dB, stability factor of 1.5dB, and S11-S22 (-41dB, -25dB) in that order.
This work presents a Fuzzy based adaptive Sliding Mode Control scheme to deal with control problem of full vehicle active suspension system and take into consideration the nonlinearities of the spring and damper, unmodeled dynamics as well as the external disturbances. The control law of fuzzy based adaptive Sliding Mode Control scheme will update the parameters of fuzzy sliding mode control by using the stability analysis of Lyapunov criteria such that the convergence in finite time and the stability of the closed loop are ensured. The proposed control scheme consists of four similar subsystems used for the four sides of the vehicle. The sub control scheme contains two loops, the outer loop is built using sliding mode controller with fuzzy estimator to approximate and estimate the unknown parameters in the system. In the inner loop, a controller of type Fractional Order PID (FOPID) is utilized to create the required actuator force. All parameters in the four sub control schemes are optimized utilizing Artificial Bee Colony (ABC) algorithm in order to improve the performance. The results indicate the effectiveness and good achievement of the proposed controller in providing the best ability to limit the vibration with good robustness properties in comparison with passive suspension system and using sliding mode control method. The controlled suspension system shows excellent results when it was tested with and without typical breaking and bending torques.
This paper describes the capability of artificial neural network for predicting the critical clearing time of power system. It combines the advantages of time domain integration schemes with artificial neural network for real time transient stability assessment. The training of ANN is done using selected features as input and critical fault clearing time (CCT) as desire target. A single contingency was applied and the target CCT was found using time domain simulation. Multi layer feed forward neural network trained with Levenberg Marquardt (LM) back propagation algorithm is used to provide the estimated CCT. The effectiveness of ANN, the method is demonstrated on single machine infinite bus system (SMIB). The simulation shows that ANN can provide fast and accurate mapping which makes it applicable to real time scenario.
As a key type of mobile robot, the two-wheel mobile robot has been developed rapidly for varied domestic, health, and industrial applications due to human-like movement and balancing characteristics based on the inverted pendulum theory. This paper presents a developed Two-Wheel Self-Balanced Robot (TWSBR) model under road disturbance effects and simulated using MATLAB Simscape Multibody. The considered physical-mechanical structure of the proposed TWSBS is connected with a Simulink controller scheme by employing physical signal converters to describe the system dynamics efficiently. Through the Simscape environment, the TWSBR motion is visualized and effectively analyzed without the need for complicated analysis of the associated mathematical model. Besides, 3D visualization of real-time behavior for the implemented TWSBR plant model is displayed by Simulink Mechanics Explorer. Robot balancing and stability are achieved by utilizing Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR) controllers' approaches considering specific control targets. A comparative study and evaluation of both controllers are conducted to verify the robustness and road disturbance rejection. The realized performance and robustness of developed controllers are observed by varying object-carrying loaded up on mechanical structure layers during robot motion. In particular, the objective weight is loaded on the robot layers (top, middle, and bottom) during disturbance situations. The achieved findings may have the potential to extend the deployment of using TWSBRs in the varied important application.
In this paper, the power system stabilizer (PSS) and Thyristor controlled phase shifter(TCPS) interaction is investigated . The objective of this work is to study and design a controller capable of doing the task of damping in less economical control effort, and to globally link all controllers of national network in an optimal manner , toward smarter grids . This can be well done if a specific coordination between PSS and FACTS devices , is accomplished . Firstly, A genetic algorithm-based controller is used. Genetic Algorithm (GA) is utilized to search for optimum controller parameter settings that optimize a given eigenvalue based objective function. Secondly, an optimal pole shifting, based on modern control theory for multi-input multi-output systems, is used. It requires solving first order or second order linear matrix Lyapunov equation for shifting dominant poles to much better location that guaranteed less overshoot and less settling time of system transient response following a disturbance.
Wind Energy Conversion Systems (WECSs) have experienced significant growth in recent years.Among various types of generators employed in WECSs,Permanent Magnet Synchronous Generators (PMSGs) are an attractive choice among the wide variety of wind generators due to several advantages.The growing penetration of PMSG-based WEGSs into the worldwide electrical grid raises the concern that the failure of wind turbine generators may potentially result in the collapse of the system.This prompted several countries to adopt the Low-Voltage Ride-Through (LVRT) for wind farms.LVRT is the capability to maintain the connection between the wind farm and the grid during certain periods of voltage sag.This paper presents an efficient LVRT control strategy for a 12.0MW (6*2MW) grid-connected PMSG-based Wind Farm (PMSG-WF).The proposed strategy aims to enhance the power quality and amount of injected power to achieve the grid code requirements by integrating a Braking Chopper (BC) and a Dynamic Voltage Restorer (DVR) with the conventional structure of PMSG-WF. The detailed mathematical models for a wind turbine, PMSG, power converters, DVR system, and grid model are utilized to analyze the dynamic behavior and operation of PMSG-WF.For DVR, a PI controller is used for voltage sag mitigation to inject reactive power during grid faults, while a hysteresis controller-based BC system is utilized to keep DC-link voltage within its permissible limits.The proposed system is exposed to three scenarios of symmetrical and asymmetrical grid fault conditions (single-phase, two-phase, and three-phase faults) at the point of common coupling to evaluate its dynamic response.MATLAB/SIMULINK environment is used to validate the effectiveness of the proposed strategy during the studied scenarios.The results show the superiority of DVR in improving the voltage stability of PMSG-WF and maintaining the uninterrupted operation of the grid during different grid faults.
The gyroscope and accelerometer are the basic sensors used by most Unmanned Aerial Vehicle (UAV) like quadcopter to control itself. In this paper, the fault detection of measured angular and linear states by gyroscope and accelerometer sensors are present. Uncertainties in measurement and physical sensors itself are the main reasons that lead to generate noise and cause the fault in measured states. Most previous solutions are process angular or linear states to improving the performance of quadcopter. Also, in most of the previous solutions, KF and EKF filters are used, which are inefficient in dealing with high nonlinearity systems such as quadcopter. The proposed algorithm is developed by the robust nonlinear filter, Unscented Kalman Filter (UKF), as an angular and linear estimation filter. Simulation results show that the proposed algorithm is efficient to decrease the effect of sensors noise and estimate accurate angular and linear states. Also, improving the stability and performance properties of the quadcopter. In addition, the new algorithm leads to increasing the range of nonlinearity movements that quadcopter can perform it.
The control problem for a class of a nonlinear systems that contain the coupling of unmeasured states and unknown parameters is addressed. The system actuation is assumed to suffer from unknown dead zone nonlinearity. The parameters bounds of the unknown dead zone to be considered are unknown. Adaptive sliding mode controller, unmeasured states observer, and unknown parameters estimators are suggested such that global stability is achieved. Simulation for a single link mechanical system with unknown dead zone and friction torque is implemented for proving the efficacy of the suggested scheme.
This article presents a power-efficient low noise amplifier (LNA) with high gain and low noise figure (NF) dedicated to satellite communications at a frequency of 435 MHz. LNAs’ gain and NF play a significant role in the designs for satellite ground terminals seeking high amplification and maintaining a high signal-to-noise ratio (SNR). The proposed design utilized the transistor (BFP840ESD) to achieve a low NF of 0.459 dB and a high-power gain of 26.149 dB. The study carries out the LNA design procedure, from biasing the transistor, testing its stability at the operation frequency, and finally terminating the appropriate matching networks. In addition to the achieved high gain and low NF, the proposed LNA consumes as low power as only 2 mW.
The thermal dependence of the spectral response (i.e. transmission, reflection and time delay ( τ r ) responses) of uniform polymer optical fiber (POF) Bragg gratings has been investigated. In addition to the temperature dependence, the effects of grating strength (kL g ) and fiber index modulation ( ∆ n) have been investigated. Besides high capability of tunable wavelength due to the unique large and negative thermo-optic coefficient of POF, the spectral response for POF Bragg gratings show high stability and larger spectrum bandwidth with temperature variation compare with the silica optical fiber (SOF) Bragg gratings, especially with the increase of the kL g value. It was found that by increasing kL g , the peak reflectance value increases and the bandwidth of the Bragg reflector become narrower. Also it’s shown by increasing the kL g value, τ r deceasing significantly and reach its minimum value at the designed wavelength ( λ B ). Furthermore, the τ r for POF Bragg gratings is less than that for SOF Bragg gratings at the same value of kL g . Also it’s found that the peak reflectivity value increases to around 60% when the ∆ n value increases from 1 ˣ 10 -4 to 5 ˣ 10 -4 .
PID controller is the most popular controller in many applications because of many advantages such as its high efficiency, low cost, and simple structure. But the main challenge is how the user can find the optimal values for its parameters. There are many intelligent methods are proposed to find the optimal values for the PID parameters, like neural networks, genetic algorithm, Ant colony and so on. In this work, the PID controllers are used in three different layers for generating suitable control signals for controlling the position of the UAV (x,y and z), the orientation of UAV (θ, Ø and ψ) and for the motors of the quadrotor to make it more stable and efficient for doing its mission. The particle swarm optimization (PSO) algorithm is proposed in this work. The PSO algorithm is applied to tune the parameters of proposed PID controllers for the three layers to optimize the performances of the controlled system with and without existences of disturbance to show how the designed controller will be robust. The proposed controllers are used to control UAV, and the MATLAB 2018b is used to simulate the controlled system. The simulation results show that, the proposed controllers structure for the quadrotor improve the performance of the UAV and enhance its stability.
This paper focuses on the vibration suppression of a half-car model by using a modified PID controller. Mostly, car vibrations could result from some road disturbances, such as bumps or potholes transmitted to a car body. The proposed controller consists of three main components as in the case of the conventional PID controller which are (Proportional, Integral, and Derivative) but the difference is in the positions of these components in the control loop system. Initially, a linear half-car suspension system is modeled in two forms passive and active, the activation process occurred using a controlled hydraulic actuator. Thereafter, the two systems have been simulated using MATLAB/Simulink software in order to demonstrate the dynamic response. A comparison between conventional and modified PID controllers has been carried out. The resulting dynamic response of the half-car model obtained from the simulation process was improved when using a modified PID controller compared with the conventional PID controller. Moreover, the efficiency and performance of the half-car model suspension have been significantly enhanced by using the proposed controller. Thus, achieving high vehicle stability and ride comfort.
In this paper describes the operation of power system networks to be nearest to stability rated values limits. State estimation for monitoring and protection power system is very important because it provides a real-time (RT) Phase angle of different nodes of accuracy and then analysis and decided to choose control way (methods). In order to detect the exact situation (instant state) for power system networks parameters. In this paper proposes a new monitoring and analysis system state estimation method integrating with MATLAB environment ability, by using phasor measurement units (PMU's) technology, by this system the estimation problem, iterations numbers, and processing time will reduce. The measurements of phasors value of voltage signal and current estimated and analyzed. Mat lab/PSAT package use as a tool to design and simulate four electrical power systems networks such as INSG 24 buses, IEEE14 bus, Diyala city 10buses (IRAQ), and IEEE6 bus and then installed and applied PMU’s devices to each system. Simulation results show that the PMU's performances effectiveness appear clearly. All results show the validation of PMU’s devices as an estimator to power system networks states and a significant improvement in the accuracy of the calculation of network status. All results achieved and discussed through this paper setting up mathematical models with Graph Theoretic Procedure algorithm.
The traditional economic dispatch (ED) inattention to the fossil fuels emission of thermal power plants no longer satisfies the environmental needs. As a result of the non-convex, non-smooth fuel cost functions in addition to the nonlinearity of the emission modelling. These make the combined economic and emission dispatch (CEED) a highly nonlinear optimization problem. Furthermore, different operation process constraints should be taken into account, such as loss in electrical networks and power balance of unit operation. These constraints increase the difficulty of obtaining the global optimal solution based on traditional methods. Recently, meta-heuristic population-based algorithms have successfully become a beneficial technique for solving nonlinear optimization problems. The major contribution in this work is presenting a recent meta-heuristic approach known as Mayfly algorithm (MA) for solving nonlinear and complex CEED problem. The numerical results are compared with results obtained from modern meta-heuristic algorithms like Jellyfish Search (JS) optimizer, Dwarf mongoose optimization (DMO), Tunicate swarm algorithm (TSA), Red deer algorithm (RDA), Tuna Swarm Optimization (TSO), Golden Eagle Optimizer (GEO) and Bald eagle search Optimization algorithm (BES). The standard IEEE 30-bus test system is used in this article. The simulation results are done using MATLAB environment. The results approve the reliability, stability, and consistency of the proposed approach. The proposed technique gives reliable, robust, and high-quality solution with faster computational time. Moreover, MA is more suitable for solving nonlinear CCED problem because it has a considerable convergence feature.
Reducing the dependency of the control system on communication in the microgrid increases the reliability and flexibility of an islanded microgrid. This paper presents a local secondary control approach to provide a fast response to power change and accurate frequency restoration. It is based on a control scheme that uses a secondary controller involving a time-controllable parameter for a Low pass filter. The high value of the time-varying parameter is placed to satisfy excellent performance regarding fast active power sharing, and the time-controllable parameter decreases after achieving power-sharing based on a time protocol to ensure accurate steady-state frequency restoration. This paper also describes the criteria for control parameter selection and stability analysis based on a precise modeling approach. The MATLAB environment is used to simulate and test the proposed control scheme, and the results have been obtained that show the validity and high performance of the proposed controller in terms of dynamic response to active power change and steady-state restoration under different operation conditions.
Large disturbances in an induction generator-based wind system necessitate rapid compensation for the reactive power. This article addresses the application of Static Synchronous Compensator (STATCOM) in optimizing the performance of grid connected wind power system. The functionality of the static synchronous compensator in maintaining system stability and reliability during/post diverse severe disturbances is thoroughly investigated. A design procedure for STATCOM, particularly the capacitor in the DC side was advised.
Clustering is one of the most energy-efficient techniques for extending the lifetime of wireless sensor networks (WSNs). In a clustered WSN, each sensor node transmits the data acquired from the sensing field to the leader node (cluster head). The cluster head (CH) is in charge of aggregating and routing the collected data to the Base station (BS) of the deployed network. Thereby, the selection of the optimum CH is still a crucial issue to reduce the consumed energy in each node and extend the network lifetime. To determine the optimal number of CHs, this paper proposes an Enhanced Fuzzy-based LEACH (E-FLEACH) protocol based on the Fuzzy Logic Controller (FLC). The FLC system relies on three inputs: the residual energy of each node, the distance of each node from the base station (sink node), as well as the node's centrality. The proposed protocol is implemented using the Castalia simulator in conjunction with OMNET++, and simulation results indicate that the proposed protocol outperforms the traditional LEACH protocol in terms of network lifetime, energy consumption, and stability.
In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.
A torsional rotating system is considered for the investigation of passive vibration control using dual loop controllers Proportional-Integral-Derivative (PID) with derivative (D) gain and Proportional – Derivative (PD) with Integral (I) controllers. The controllers are used as low pass filters. Simulation of the models using Matlab-Simulink have been built in this work for torsional vibration control. A comparison between the two controllers with uncontrolled system have been carried out. Results show that the PD – I control is the best method which gives better stability response than the PID – D control.
Inter-symbol interference (ISI) exhibits major distortion effect often appears in digital storage and wireless communica- tion channels. The traditional decision feedback equalizer (DFE) is an efficient approach of mitigating the ISI effect using appropriate digital filter to subtract the ISI. However, the error propagation in DFE is a challenging problem that degrades the equalization due to the aliasing distorted symbols in the feedback section of the traditional DFE. The aim of the proposed approach is to minimize the error propagation and improve the modeling stability by incorporating adequate components to control the training and feedback mode of DFE. The proposed enhanced DFE architecture consists of a decision and controller components which are integrated on both the transmitter and receiver sides of communication system to auto alternate the DFE operational modes between training and feedback state based on the quality of the received signal in terms of signal-to-noise ratio SNR. The modeling architecture and performance validation of the proposed DFE are implemented in MATLAB using a raised-cosine pulse filter on the transmitter side and linear time-invariant channel model with additive gaussian noise. The equalizer capability in compensating ISI is evaluated during different operational stages including the training and DFE based on different channel distortion characteristics in terms of SNR using both 0.75 and 1.5 symbol duration in unit delay fraction of FIR filter. The simulation results of eye-diagram pattern showed significant improvement in the DFE equalizer when using a lower unit delay fraction in FIR filter for better suppressing the overlay trails of ISI. Finally, the capability of the proposed approach to mitigate the ISI is improved almost double the number of symbol errors compared to the traditional DFE.
The presented research introduces a control strategy for a three-phase grid-tied LCL-filtered quasi-Z-source inverter (qZSI) using a Lyapunov-function-based method and cascaded proportional-resonant (PR) controllers. The suggested control strategy ensures the overall stability of the closed-loop system and eliminates any steady-state inaccuracy in the grid current. The inverter current and capacitor voltage reference values of qZSI are created by the utilization of cascaded coupled proportional-resonant (PR) controllers. By utilizing synchronous reference frame and Lyapunov function- based control, the requirement to perform derivative operations and anticipate inductance and capacitance are avoided, resulting in achieving the goal of zero steady-state error in the grid current. The qZSI can accomplish shoot-through control by utilizing a simple boost control method. Computer simulations demonstrate that the suggested control strategy effectively achieves the desired control objectives, both in terms of steady-state and dynamic performance.
A composite PD and sliding mode neural network (NN)-based adaptive controller, for robotic manipulator trajectory tracking, is presented in this paper. The designed neural networks are exploited to approximate the robotics dynamics nonlinearities, and compensate its effect and this will enhance the performance of the filtered error based PD and sliding mode controller. Lyapunov theorem has been used to prove the stability of the system and the tracking error boundedness. The augmented Lyapunov function is used to derive the NN weights learning law. To reduce the effect of breaching the NN learning law excitation condition due to external disturbances and measurement noise; a modified learning law is suggested based on e-modification algorithm. The controller effectiveness is demonstrated through computer simulation of cylindrical robot manipulator.
Utilizing Heating PID control systems is common across numerous industries to attain the desired output. Nevertheless, the development in the status of Fractional Order Proportional Integral Derivative Controllers (FOPID) has led to improved control performance and increased degrees of freedom in industrial applications. The paper proposed real-time microwave heating systems which exhibit several challenging characteristics and are complex enough to effectively demonstrate the robustness advantage of fractional (FOPID) over traditional PID controllers. An Adaptive Neuro-Fuzzy Inference System (ANFIS) was modeled using real-time data to assess the effectiveness of conventional PID and FOPID controllers. The results of the study demonstrated that FOPID controllers outperform conventional PID controllers in terms of performance, robustness, stability, flexibility, and faster response. Additionally, the study utilized MATLAB and LabVIEW software to model the Fractional PID controller, the traditional PID controller, and the ANFIS model. The outcomes illustrate that the FOPID controller demonstrates faster rise times (3.8 seconds vs. 6.0 seconds for PID), lower overshoot (1.0oC vs. 2.5oC, and shorter settling times (10 seconds vs. 17 seconds). During setpoint drops, FOPID exhibits reduced undershoot (1.40C compared to 3.2oC) and quicker recovery (5.5 seconds vs. 8.5 seconds). In the final tracking phase, FOPID maintains a lower residual error ( 0.20C vs. 0.7oC) and achieves a steady-state error of 0.1oC, compared to 0.5oC for PID.
Efficient energy collection from photovoltaic (PV) systems in environments that change is still a challenge, especially when partial shading conditions (PSC) come into play. This research shows a new method called Maximum Power Point Tracking (MPPT) that uses fuzzy logic and neural networks to make PV systems more flexible and accurate when they are exposed to PSC. Our method uses a fuzzy logic controller (FLC) that is specifically made to deal with uncertainty and imprecision. This is different from other MPPT methods that have trouble with the nonlinearity and transient dynamics of PSC. At the same time, an artificial neural network (ANN) is taught to guess where the Global Maximum Power Point (GMPP) is most likely to be by looking at patterns of changes in irradiance and temperature from the past. The fuzzy controller fine-tunes the ANN’s prediction, ensuring robust and precise MPPT operation. We used MATLAB/Simulink to run a lot of simulations to make sure our proposed method would work. The results showed that combining fuzzy logic with neural networks is much better than using traditional MPPT algorithms in terms of speed, stability, and response to changing shading patterns. This innovative technique proposes a dual-layered control mechanism where the robustness of fuzzy logic and the predictive power of neural networks converge to form a resilient and efficient MPPT system, marking a significant advancement in PV technology.
Since recent societies become more hooked into electricity, a higher level of power supply continuity is required from power systems. The expansion of those systems makes them liable to electrical faults and several failures are raised due to totally different causes, like the lightning strike, power system element failure caused by mechanical aging as well as human mistakes. These conditions impact the stability of the power as well as lead to costly maintenance and loss of output. This article examines the latest technologies and strategies to determine the location of faults in medium voltage distribution systems. The aim is to classify and assess different strategies in order to determine the best recommended models in practice or for further improvement. Several ways to locate failures in distribution networks have therefore been established. Because faults are unpredictable, quick fault location as well as isolating are necessary to reduce the impact of faults in distribution networks as well as removing the emergency condition from the entire system. This study also includes a comprehensive evaluation of several defect location methods depending on the algorithm employed, the input, the test system, the characteristics retrieved, and the degree of complexity. In order to gain further insight into the strengths and limitations of each method and also comparative analysis is carried out. Then the main problems of the fault location methods in distribution network are briefly expounded.
This article emphasizes on a strategy to design a Super Twisting Sliding Mode Control (STSMC) method. The proposed controller depends on the device of Field Programmable Gate Array (FPGA) for controlling the trajectory of robot manipulator. The gains of the suggested controller are optimized using Chaotic Particle Swarm Optimization (PSO) in MATLAB toolbox software and Simulink environment. Since the control systems speed has an influence on their stability requirements and performance, (FPGA) device is taken in consideration. The proposed control method based on FPGA is implemented using Xilinx block sets in the Simulink. Integrated Software Environment (ISE 14.7) and System Generator are employed to create the file of Bitstream which can be downloaded in the device of FPGA. The results show that the designed controller based of on the FPGA by using System Generator is completely verified the effectiveness of controlling the path tracking of the manipulator and high speed. Simulation results explain that the percentage improvement in the Means Square Error (MSEs) of using the STSMC based FPGA and tuned via Chaotic PSO when compared with the same proposed controller tuned with classical PSO are 17.32 % and 13.98 % for two different cases of trajectories respectively.