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In a power grid with multiple two-state loads, the total power can vary over a significant range. This results in the inability to supply this system from a low-power source. The solution is an algorithm that shapes energy demand depending on its availability. For this purpose, a new load distribution method is proposed based on introducing a buffer between the temperature controller output and the heater and filtering the load using a master Proportional–Integral (PI) controller. The aim of the work was to evaluate the quality of the developed algorithm for limiting power peaks in the power grid. The research was conducted on a model of the Creep Test Laboratory with 389 heaters simulated in MATLAB Simulink R2023b. The algorithm was tested with various settings of the master controller parameters. By experimentally adjusting these parameters, a ten-fold reduction in peak power was achieved. The standard deviation for the L1 phase was reduced from 7.6 kW to 0.6 kW. Similar results were obtained for phases L2 and L3. The tested control system tracked changes in the average power value by changing the number of loads switched on and by frequency-modulating the signal when the change was less than the power of a single load. It was demonstrated that the controlled delayed switching of electrical loads can modify the shape of the total electric power without affecting their operation. The proposed solution features a low computational complexity, which allows its implementation in various systems. [ABSTRACT FROM AUTHOR]

In the realm of electrical distribution, managing power quality is critical due to its significant impact on infrastructure and customer satisfaction. Addressing issues such as voltage sags and swells, along with current and voltage harmonics, is imperative. The innovative approach proposed in this paper centers on a dual-phase control strategy using a Universal Power Quality Conditioner that integrates series and parallel compensations to rectify these disturbances simultaneously. Our methodology introduces a hybrid control scheme that employs adaptive dynamic neural networks (ADNN), a sinusoidal tracking filter (STF), and a proportional-integral (PI) controller optimized via an improved krill herd (IKH) algorithm. The first phase utilizes the ADNN-based adaptive integrated estimator for quick and accurate disturbance detection and estimation. Subsequently, the second phase employs the STF, omitting the Low Pass Filter and employing a Phase Locking Loop to generate precise reference voltages and currents for the series and parallel active filters based on dynamic load and source conditions. This advanced control mechanism not only enhances system efficacy but also reduces the need for extensive computational resources. Furthermore, the performance of both series and parallel inverters is finely tuned through a PI controller optimized with the IKH algorithm, improving the DC link voltage regulation. Our extensive testing under various conditions, including voltage imbalances and harmonic disturbances, demonstrates the robustness of the proposed solution in both transient and steady-state scenarios. [ABSTRACT FROM AUTHOR]

This paper examines the problem of synthesizing a two-loop control system for a three-phase voltage source inverter designed for an autonomous power supply system. This type of DC/AC converters is an essential part of power supply systems with renewable energy sources as primary sources, and, accordingly, must corresponds to the requirements for the electric power quality. These requirements are met through circuit design solutions, as well as through the construction of high-precision automatic control systems. The system integrates: a hydrogen electrolyzer, a hydrogen storage system, and a hydrogen fuel cell. The paper proposes a method for calculating the parameters of PI controllers with resonant components, based on the time-scale separation method and allowing independent adjustment of the controller components. The introduction of a resonant component ensures high tracking accuracy for a desired main voltage harmonic and selective suppression of external harmonic influences. The proposed approach allows to synthesize an automatic control system that significantly improves the quality of the voltage inverter output parameters. [ABSTRACT FROM AUTHOR]

A power system is a complex, nonlinear, and dynamic system, with operating parameters that change over time. Because of the complexity of big load lines and faults, high-voltage transmission power systems are usually vulnerable to transient instability concerns, resulting in power losses and increased voltage variation. This issue has the potential to cause catastrophic events such as cascade failure or widespread blackouts. This problem affects Ethiopia's high-voltage transmission power grid in the northwest area. Because of the increased demand for electrical energy, transmission lines' maximum carrying capacity should be enhanced to maintain a secure and uninterrupted power supply to consumers. To address the problem, ANFIS-based UPFC is used for high-voltage transmission lines. This device was chosen as the ideal alternative due to its capacity to rapidly correct reactive power on high-voltage transmission networks. The PSO algorithm was used to determine the best location for the UPFC. The ANFIS controller receives voltage error and rate of change of voltage error as inputs. Seventy percentage of the retrieved data are used for ANFIS training and 30% for ANFIS testing. To show the performance of the proposed controller, three-phase ground faults are used from a severity perspective. When a three-phase ground fault occurs at the midpoint of the Beles to Bahir Dar transmission line, for instance, the settling time of rotor angle deviation, rotor speed, rotor speed deviation, and output active power of synchronous generators using ANFIS-based UPFC is reduced by 70.58%, 37.75%, 37.75%, and 43.75%, respectively, compared to a system without UPFC. At peak load, PI-based UPFC and ANFIS-based UPFC reduce active power loss by 51.25% and 71.50%, respectively, compared to a system without UPFC on the Beles to Bahir Dar transmission line. In terms of percentage overshoot and settling time, ANFIS-based UPFC outperforms PI-based UPFC for transient stability enhancement. [ABSTRACT FROM AUTHOR]

This paper presents issues related to the damping of torsional vibrations in a system with elastic coupling. A novel PI-based control structure with additional feedback from torsional torque and its derivative is proposed. For the estimation of the required variables, the integral observer is proposed. Analytical expressions are presented to enable the selection of parameters of the control system. The relationship between the considered system and popular structures with a PI controller and one additional feedback from torsional torque and the derivative of torsional torque is pointed out. The proposed control structure is tested under simulation and experimental studies. [ABSTRACT FROM AUTHOR]

This paper employs artificial neural networks (ANNs) in estimating and controlling the speed of a separately excited DC motor, which is also one of the most essential modern techniques used in control applications and to enhance the effectiveness of separately excited DC motor speed control. For speed control of a separately excited DC motor, the paper compares the performance of the conventional proportional integral (PI) and artificial neural networks (ANNs). A neural network (NNs) based controller is an extremely appreciated technology for accomplishing high-recital speed control. The simulation results are expose to demonstrate the efficacy and advantage of the control system of a separately excited DC motor (SEDCM) using ANNs over the traditional control arrangement. The integration of (ANNs) has shown significant improvements in performance in speed control of DC motors separately excited, chiefly in dynamic response and robustness in contradiction of load variations. This technique leverages the adaptive competences of ANNs to improve traditional control methods. In this manuscript, two controller for speed control of SEDCM are designed and simulated using matlab Simulink program. The first one is the PI controller and the second is the ANN controller, the results of the ANN controller based speed control of the SEDCM give quick response, low overshot, small settling time, zero steady state error when sudden change happened in reference speed or load torque. In addition, as compared with the results of PI controller which give slow response, high overshot, large settling time under the same conditions. [ABSTRACT FROM AUTHOR]

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Presented in this paper is a direct model predictive control (MPC) for directly controlling the rotor speed and flux of an inverter-fed induction motor, which is also applicable to induction generators. Discrete dynamic model of the induction machine is applied in stationary reference frame with pre-set present and past reference values of the speed and rotor flux for generating the stator current references The uniqueness of the MPC proposed herein is that it does not require outer cascade proportional plus integral (PI) controllers used for regulating rotor speed and flux. Elimination of the outer loop cascade is made possible in this article by the introduction of a unique four-term cost function comprising of weighted magnitudes of the errors between four measured state variables and their reference commands. The four state variables in the unique cost function are the rotor speed, the rotor flux linkage, quadrature axis and direct axis stator currents. The MPC minimizes the cost function at every switching instance by selecting the switching states that gives the least cost function. Consequently, the selected optimal switching states are used to switch optimal inverter output voltages across the machine's stator terminals. As a precursor for obtaining an optimal rotor flux command in the MPC for every torque output, another unique efficiency improvement scheme is developed, which uniquely determines the stator angular velocity and the rotor flux that minimizes the core and copper losses while maintaining a constant slip operation. Therefore, efficacy of the proposed direct model predictive control scheme is verified by comparing its results to results obtained from equivalent vector control on equivalent machine. Results presented show lower loss regime with MPC on optimal stator angular velocity and rotor flux than vector control. [ABSTRACT FROM AUTHOR]

The power quality (PQ) variations due to the introduction of electric vehicles (EVs) and Electric Vehicle Charging Stations (EVCS) into the power system network is the foremost limitation which is holding back the push for a complete overhaul of fossil fuel-based internal combustion (IC) engine vehicles by EVs. With the emergence of EVs into the mainstream in the automobile sector, this research article proposes a system wherein distributed energy resources (DERs) like Fuel-Cells (FCs) are integrated to grid in order to supply for the additional power demand created by the EVs such that the current injected from FC compensates the grid current harmonics maintain the grid current THD ≤ 5% as per IEEE519 standards. A Particle Swarm Optimized (PSO) based PI current controller and a Fuzzy-logic-based controller are proposed for a grid connected fuel cell so as to mitigate the grid current deterioration caused due to the presence of existing nonlinear loads as well as EVCS. In addition, the parameters like grid voltage distortion and frequency variance are considered simultaneously to ascertain the capability of proposed controllers. This work is carried out in the MATLAB/SIMULINK environment. [ABSTRACT FROM AUTHOR]

Abstract A power system is a complex, nonlinear, and dynamic system, with operating parameters that change over time. Because of the complexity of big load lines and faults, high-voltage transmission power systems are usually vulnerable to transient instability concerns, resulting in power losses and increased voltage variation. This issue has the potential to cause catastrophic events such as cascade failure or widespread blackouts. This problem affects Ethiopia's high-voltage transmission power grid in the northwest area. Because of the increased demand for electrical energy, transmission lines' maximum carrying capacity should be enhanced to maintain a secure and uninterrupted power supply to consumers. To address the problem, ANFIS-based UPFC is used for high-voltage transmission lines. This device was chosen as the ideal alternative due to its capacity to rapidly correct reactive power on high-voltage transmission networks. The PSO algorithm was used to determine the best location for the UPFC. The ANFIS controller receives voltage error and rate of change of voltage error as inputs. Seventy percentage of the retrieved data are used for ANFIS training and 30% for ANFIS testing. To show the performance of the proposed controller, three-phase ground faults are used from a severity perspective. When a three-phase ground fault occurs at the midpoint of the Beles to Bahir Dar transmission line, for instance, the settling time of rotor angle deviation, rotor speed, rotor speed deviation, and output active power of synchronous generators using ANFIS-based UPFC is reduced by 70.58%, 37.75%, 37.75%, and 43.75%, respectively, compared to a system without UPFC. At peak load, PI-based UPFC and ANFIS-based UPFC reduce active power loss by 51.25% and 71.50%, respectively, compared to a system without UPFC on the Beles to Bahir Dar transmission line. In terms of percentage overshoot and settling time, ANFIS-based UPFC outperforms PI-based UPFC for transient stability enhancement.

Multiphase machines drives have gained significant attention in the control and machines communities due to their fault-tolerant capabilities, lower power and current per phase, and high torque density. However, the difficulties in extending the current traditional three-phase regulation and control structure to multi-phase systems still limit its applicability. The performance of the Vector Control (VC) scheme using conventional PI controllers gives high total harmonic distortion (THD), reduces the system's robustness, and also large torque ripple. In this regard, this work proposes the application of fuzzy logic controller (FLC) devoted to enhancing the performance of the VC scheme of a 5-phase Induction Motor (5-phase IM) fed by a Multi-Level Inverter (MLI). This work aims to develop a control strategy that integrates the advantages of multi-phase IM and MLI using artificial intelligence algorithms, enhancing system robustness and dynamic performance. This study uses Matlab/Simulink for modeling and verifying the models' performance. The advantages and robustness of the suggested control system (VCFLC- MLI) compared to the conventional technique are demonstrated according to different tracking tests, sensitivity to changes in load torque, and robustness to changes in the 5-phase IM's parameters. The simulation results have confirmed the effectiveness of the proposed VC-FLC-MLI strategy by improving dynamic efficiency and robustness against variations in 5-phase IM parameters. As compared to the conventional strategy, the proposed VC-FLC-MLI method improves the stator current %THD and torque ripples by about 54.25% and 62%, respectively. The results show that the goals of this research were effectively achieved. [ABSTRACT FROM AUTHOR]

This study focuses on the crucial need for effective control strategies design for linear electromechanical actuators (EMA), which are essential components in industrial automation. While the PI controller is commonly used due to its simplicity and versatility in various control scenarios, its effectiveness is limited by its complex tuning process, which requires significant time and effort to achieve the optimal performance. To address this issue, the paper focuses on employing metaheuristic approaches that are Spiral Dynamic Algorithm (SDA) and Artificial Bee Colony (ABC) to fine tune the PI parameters for controlling the position of EMA. The simulation results, implemented in MATLAB Simulink, show that PI-SDA and PI-ABC produce better performances with minimal steady-state error, reduced overshoot, faster settling time and rise time. Hardware-in-the-loop (HIL) testing proves the effectiveness of the controller's performance in real-world scenarios. PI-SDA achieved a steady-state error of 0.0623, zero overshoot, faster rise time of 1.6956 s, and faster settling time of 7.2166 s. As for validation, PI-ABC showed similar results in HIL environment with a steady-state error of -0.0314, an overshoot of 5.0007%, a rise time of 1.3805 s, and a settling time of 9.1002 s. These results highlight the effectiveness of metaheuristic methods in real-world implementation and outperform the traditional heuristic approaches.

Electric vehicles (EVs) cut greenhouse gas emissions and our use of non-renewable resources, making them more attractive. EVs have lower fuel and maintenance expenses than internal combustion engine automobiles. This study proposes a multi-converter/Multi‒Machine system with two induction motors (IM) that drive a pure EV’s rear wheels. EV two-stage controllers using a simple Adaline neural network (NN) regulate Field-Oriented regulate of a three-phase IM. To control IM speed, the first controller level is a hybrid proportional–integral (PI) with a robust integral sign of error (RISE) controller. Injection torque is controlled by PI‒adaline NN in the second controller step. The simple Adaline NN improves two-stage controller performance. The Multi-Verse Optimization algorithm found the ideal RISE parameter to improve EV drive system performance. A plug-in EV’s linear speed is controlled by the Electronic Differential Controller (EDC). It uses the driver’s reference speed and steering angle to set each driving wheel’s reference speed. EDC adjusts wheel speeds to enhance traction and stability during cornering, accelerating, and decelerating. Utilizing this information, the EDC can effectively distribute power and torque to the wheels, thereby enhancing vehicle handling and overall performance. Three distinct road scenarios and the designated driving route topology have been used to act and demonstrate the resistive forces that affected the EV while it was traveling down the road. By using Matlab (Simulink), EV’s roadworthiness and efficiency will be evaluated.

Introduction. In the poultry industry, the use of local LED lighting systems with changeable light and correlated color temperature of the luminaire optical radiation during the photoperiod is promising. To maintain light intensity accurately at a specified level, the use of an automatic control system (ACS) of lighting is required. Aim of the Study. The study at aimed at determining the parameters for the PI controller of the digital ACS of lighting in all-age cages, providing the required quality indicators of the regulatory process. Materials and Methods. For the study there were used the elements of mathematical statistics, experimental research design theory, correlation and regression analysis, automatic control theory and computer simulation. Real experiments were carried out on a laboratory sample of a digital dynamic local lighting system. Computer simulation was performed in the SimInTech program. Results. A computer model for a digital ACS of lighting has been developed. The tunings of the PI controller were calculated using the CHR, VTI, and Kopelovich methods. A comparative estimation of the control process quality was carried out using graphs of transient processes obtained by computer simulation. The reliability of the results of computer simulation of ACS lighting has been confirmed experimentally. Discussion and Conclusion. High regulator performance indicators for the digital ACS of lighting were ensured by a PI controller with kP = 14.027∙10−3 and TI = 145.72 ms calculated using the Kopelovitch method. A comparative assessment of transient processes in a real ACS and in a computer model of this system allows concluding that the developed model is highly accurate and the choice of simulation parameters in the SimInTech program is correct. It has been experimentally established that the required quality of the control process of ACS is ensured for a wide range of specified lighting: from 1 to 25 lux.

Introduction. The integration of Renewable Energy Sources (RESs), particularly Wind Energy Conversion Systems (WECS), is vital for reducing reliance on fossil fuels and addressing climate change. However, this transition poses challenges, including ensuring grid stability in the face of intermittent RESs. Compliance with grid codes is crucial, with a focus on Low Voltage Ride Through (LVRT) capability. Problem. The intermittent nature of RESs, specifically in Permanent Magnet Synchronous Generator (PMSG) based WECS, presents challenges to grid stability during voltage dips. Goal. To enhance voltage stability and LVRT capability in PMSG-based WECS by integrating a Dynamic Voltage Restorer (DVR) with an energy storage device. This involves regulating the input DC voltage to the DVR using a type 2 fuzzy controller, adapting intelligently to changing conditions. Methodology. DVR, powered by an energy storage device, is strategically integrated with WECS. A type 2 fuzzy controller regulates the DC voltage to DVR. The rectified WECS output undergoes processing through an isolated flyback converter. A 31-level Cascaded H-Bridge Multilevel Inverter (CHBMLI) with PI control ensures high-quality AC output. Results. The validation of developed system is executed using MATLAB/Simulink revealing a reduced Total Harmonic Distortion (THD) value of 1.8 %, ensuring significance in LVRT capability. Originality. The strategic integration of DVR with PMSG-based WECS, addresses the LVRT challenges. The use of type 2 fuzzy controller for intelligent voltage regulation and a sophisticated multilevel inverter contributes to the uniqueness of proposed solution. Practical value. The developed system provides benefits by ensuring reliable LVRT capability in PMSG-based WECS with reduced THD of 1.8 % indicating improved grid compatibility. References 26, tables 5, figures 20.

Laser metal deposition (LMD) is a technology for the production of near-net-shape components. It is necessary to control the manufacturing process to obtain good geometrical accuracy and metallurgical properties. In the present study, a closed-loop control method of melt pool temperature for the deposition of small Ti6Al4V blocks in open environment was proposed. Based on the developed melt pool temperature sensor and deposition height sensor, a closed-loop control system and proportional-integral (PI) controller were developed and tested. The results show that with a PI temperature controller, the melt pool temperature tends to the desired value and remains stable. Compared to the deposition block without the controller, a flatter surface and no oxidation phenomenon are obtained with the controller. [ABSTRACT FROM AUTHOR]

The global shift toward renewable energy sources in response to the depletion of fossil fuel reserves and environmental concerns has spotlighted photovoltaic (PV) systems as key players in sustainable power generation. This research presents a novel approach aimed at maximizing PV module power generation and seamlessly integrating it with the electrical grid. The innovation lies in the development of a clamped Z-source boost converter, enhancing PV voltage to facilitate efficient power transfer to the grid. Additionally, a Multi-Objective Genetic Algorithm Optimized Radial Basis Function Neural Network is proposed for continuous Maximum Power Point Tracking, ensuring optimal power extraction and enhanced converter performance. A 1Φ voltage source inverter with a proportional integral controller regulates power injection into the grid while ensuring synchronization. The study's outcomes, validated through a combination of FPGA controller and MATLAB simulations, showcase real-time control and high-speed processing by the FPGA, coupled with comprehensive analysis and optimization through MATLAB simulations. The proposed converter achieves an impressive 96.8% efficiency and maximizes the voltage gain ratio. This research contributes significantly to the field by providing a cutting-edge solution for efficient PV power integration with the grid, addressing key challenges in renewable energy systems. [ABSTRACT FROM AUTHOR]

Microgrid systems have emerged as a favourable solution for addressing the challenges associated with traditional centralized power grids, such as limited resilience, vulnerability to outages, and environmental concerns. As a consequence, this paper presents a hybrid renewable energy source (HRES)-based microgrid, incorporating photovoltaic (PV) system and wind to achieve sustainable and reliable power generation. A novel modified Z-source Zeta converter is incorporated in the system to enhance PV voltage together with hybridized grey wolf optimized sea lion algorithm-assisted PI controller for maintaining DC-link voltage. The wind system utilizes a doubly-fed induction generator (DFIG) for efficient power generation. To ensure effective functioning of DFIG, a proportional-integral (PI)-based droop control strategy is adopted, which regulates the power transfer from wind turbine to grid. In this proposed system, a bidirectional converter with a battery is utilized for energy storage and management. The battery can be charged and discharged in accordance with the needs of the system thanks to the bidirectional converter. Excess power generated by HRES systems is stored in the battery for later use during periods of low energy generation or high demand. Additionally, a dump load is included in the system to dissipate surplus power when battery is fully charged. MATLAB software is utilized validate the effectiveness and feasibility of proposed system, demonstrating improved power quality, grid integration, and energy management. [ABSTRACT FROM AUTHOR]

A major challenge with the integration of renewable energy sources (RES) into microgrids is their inherent variability and intermittency, affecting the power supply's reliability and quality. Photovoltaic (PV) and wind power conversion systems (WECS) are two examples of RES. The hybrid optimization and control techniques addressed in this work contribute to stable, reliable and efficient microgrid operation, which is essential for the successful integration of RES and the overall resilience of energy systems. Enhancing a PV system's performance within a hybrid energy microgrid is the main goal of this effort. Microgrid components consist of a PV system, a doubly fed induction generator (DFIG)-based WECS and a supercapacitor because power generated from PV and WECS sources is intermittent. To effectively manage the PV system's output, a control strategy is employed using an improved Landsman converter and a hybrid whale artificial bee colony optimization (WABCO)-based proportional–integral (PI) controller. Pulse width modulation (PWM) rectifier coupled to the WECS produces a DC output that is stabilized with the help of the PI controller. A synchronous PI current controller approach is also utilized to synchronize grid voltage. The proposed control methodology is validated through comprehensive simulations conducted in MATLAB, demonstrating the dependable and consistent operation of the designed microgrid. [ABSTRACT FROM AUTHOR]

In this study, a peak current mode controller design and implementation is carried out for a high-gain integrated Z-Source DC–DC Converter. In this context, firstly, a small-signal dynamic modeling method for switching converters, based on volt–charge balance methodology, has been proposed and then applied to the Z-Source converter. With the proposed method, the modeling procedure of the integrated complex converters with multiple operating points can be simplified. To verify the obtained model, a small-signal simulation of the converter is also performed by using the Frequency Response Estimator tool of MATLAB/Simulink. The transfer function from the control current to the output was then obtained from the developed converter model to design the current mode controller. Then, a PI (proportional–integral) controller was designed according to the obtained transfer function, and a simulation study was performed with PLECS. Finally, an experimental study was carried out and compared with the simulation study. The results of the simulation and experimental studies show that the designed control method maintains the output voltage at desired levels regardless of the changes in load conditions and input voltage, successfully. [ABSTRACT FROM AUTHOR]

The challenge regarding the output voltage regulation control of quasi-resonant converters while concurrently fulfilling zero-current switching is addressed in this study. In particular, an alternative to the usual practice of considering fixed duty cycle operation is presented to deal with the narrow robustness margin against load variations exhibited by this condition. The main contribution was the introduction of an additional block in the control loop that implements a new linear relationship between the duty cycle and the switching frequency in terms of the load current. This block proportionally modifies the duty cycle with the switching frequency that, as usual, is used to regulate the output voltage. The structure of the contribution was obtained by exploiting the knowledge of the differential equations that describe the dynamical behavior of the topology. Although it was shown that this modification could be used regardless of the control scheme implemented for the operation of the converter, its usefulness was illustrated by presenting a modified implementation of a classical P I control scheme. It was shown via numerical evaluations that the robustness of the converter under classical P I control was drastically improved for both increases and decreases in the load value. From the implementation perspective, this contribution is attractive since it exhibits a simple structure and neither requires the use of auxiliary switches nor increases the cost of current solutions. [ABSTRACT FROM AUTHOR]

This paper presents a powerful performance optimization approach for load frequency control in the off-grid mode of a delayed fuel cell microgrid with PI controller. The new approach based on stability boundary locus is used. Thanks to this, the search is only carried out in PI control parameter space keeping stable the system in the proposed optimization. The superiority of the proposed approach in terms of iteration history, fitness values, and time responses of the system is presented by comparing the results obtained. According to the results obtained, the proposed approach reaches the global minimum in a very short number of iterations and achieves this by both guaranteeing the stability of the system and providing the desired time response performances compared to the other method. • The stability of a delayed fuel cell microgrid system is a very important issue, especially in off grid mode. • A delayed fuel cell microgrid system should provide the desired quality of energy to the load. • The optimization approach provides the desired quality of energy to the load ensuring the stability of the microgrid. • Proposed optimization approach has great advantages in terms of iteration history and fitness value. [ABSTRACT FROM AUTHOR]

Introduction. The integration of Renewable Energy Sources (RESs), particularly Wind Energy Conversion Systems (WECS), is vital for reducing reliance on fossil fuels and addressing climate change. However, this transition poses challenges, including ensuring grid stability in the face of intermittent RESs. Compliance with grid codes is crucial, with a focus on Low Voltage Ride Through (LVRT) capability. Problem. The intermittent nature of RESs, specifically in Permanent Magnet Synchronous Generator (PMSG) based WECS, presents challenges to grid stability during voltage dips. Goal. To enhance voltage stability and LVRT capability in PMSG-based WECS by integrating a Dynamic Voltage Restorer (DVR) with an energy storage device. This involves regulating the input DC voltage to the DVR using a type 2 fuzzy controller, adapting intelligently to changing conditions. Methodology. DVR, powered by an energy storage device, is strategically integrated with WECS. A type 2 fuzzy controller regulates the DC voltage to DVR. The rectified WECS output undergoes processing through an isolated flyback converter. A 31-level Cascaded H-Bridge Multilevel Inverter (CHBMLI) with PI control ensures high-quality AC output. Results. The validation of developed system is executed using MATLAB/Simulink revealing a reduced Total Harmonic Distortion (THD) value of 1.8 %, ensuring significance in LVRT capability. Originality. The strategic integration of DVR with PMSG-based WECS, addresses the LVRT challenges. The use of type 2 fuzzy controller for intelligent voltage regulation and a sophisticated multilevel inverter contributes to the uniqueness of proposed solution. Practical value. The developed system provides benefits by ensuring reliable LVRT capability in PMSG-based WECS with reduced THD of 1.8 % indicating improved grid compatibility. References 26, tables 5, figures 20. [ABSTRACT FROM AUTHOR]

Renewable energy sources are being incorporated into power networks to guarantee dependable and inexpensive electricity for the urban and industrial sectors. Recent research approximates microgrids powered by renewable energy and controlled by smart grids may provide more reliable and efficient energy systems economically. Its natural interaction among renewable energy sources, electric demands, and energy storage devices makes DC microgrid a contemporary electrical grid technology. In this paper, a nonlinear control approach with outstanding precision, robustness, and simplicity of tuning and implementation, is made to deal with the control of the DC microgrid system to bring out the efficacy of the system at higher points. A closed-loop continuous time sliding mode controller is proposed for a boost converter with a cascade filter. DC microgrid system with sliding mode controller is simulated and the analysis has been carried outs with motor load to check the adequacy of the proposed system. [ABSTRACT FROM AUTHOR]

In the past three decades, proportional‐integral/PI‐differential (PI/PID) controllers and model predictive controller (MPCs) have predominantly governed complex chemical process control. Despite their advancements, these approaches have limitations, with PI/PID controllers requiring scenario‐specific tuning and MPC being computationally demanding. To tackle these issues, we introduce the long‐short‐term‐memory (LSTM)‐controller (LSTMc), a model‐free, data‐driven framework leveraging LSTM networks' robust time‐series prediction capabilities. The LSTMc predicts subsequent manipulated inputs by evaluating state evolution and error dynamics from both the current and previous time‐steps, which proved effective in our dextrose batch crystallization case study. Remarkably, the LSTMc achieves less than 2% set‐point deviation, three times better than MPCs, and retains robustness even with 10%–15% sensor noise. With these results, LSTMc emerges as a promising alternative for process control, adeptly adjusting to changing process conditions and set‐points, providing efficient computation for an optimal input profile, and effectively filtering out common industrial process noise. [ABSTRACT FROM AUTHOR]

This work aims to design, model and analyse an adaptive control method for voltage source converters (VSCs) under unbalanced PV sources and non-linear load operations. Both utility grids and stand-alone grid systems can use the proposed system. The VSC will inject Solar Photovoltaic(SPV) Power into the grid, to compensate fundamental component and harmonic reactive power, adjust the power factor, and balance the system's three-phase load when there is an imbalanced load. Shunt active power filters (APFs) have proven to be effective in filtering harmonic currents and correcting reactive power for linear/nonlinear loads. This research proposed a unique method for estimating the reference compensation currents of a three-phase shunt active power filter (APF) in a steady state under distorted and unbalanced source voltages (PV Sources). The suggested method is compared with three prior evaluated shunt APF reference compensating techniques. Even when the source voltages are significantly distorted and unbalanced due to variation in temperature and irradiation, the proposed system is compensating reactive power and harmonic/neutral currents of the load more effecetively. Furthermore, the suggested technique results in a simplified design of the shunt APF controller with three phase voltage source converter. [ABSTRACT FROM AUTHOR]

Due to the environment's instability, to reduce inconsistent power supply it is preferable to connect two or more RES in a grid. Harmonics and other power quality issues are introduced into the distribution grid as a result of integrating these Hybrid Renewable Energy Storage (HRES) with nonlinear loads, which is a significant concern for the utility and the consumers, respectively. With the use of the Unified Power Quality Conditioner (UPQC), power quality problems such as voltage interruptions, actual power, and reactive power can be reduced. For optimal operation of the series and shunt compensator of UPQC, the outputs of the converters are controlled by PWM signals, which are optimally tuned by controllers implemented with updated algorithms. An optimized Hybrid metaheuristic-assisted collateral controller comprising of fractional-order proportional integral derivative (FOPID) controller cum (proportional integral (PI) controller for the enhancement of the Three-Phase HRES system-based Distribution Grid integrated with UPQC is developed in this paper. The UPQC reduces the harmonics that non-linear loads feed into the power supply and power quality concerns. The DC link voltage regulation and controller optimization are achieved with the optimal tuning of gain parameters, fractional orders and weight parameters using a hybrid Metaheuristic Algorithm named Amplified Slime Mould with WildeBeest Herd Optimization (ASM-WHO) Algorithm. The proposed system is developed in MATLAB Simulink to compare the effectiveness of compensation for voltage sags and surges and total harmonic distortion (THD) to more traditional approaches, UPQC with PI controller, FOPID-PI controller, FOPID-PI with Chicken Swarm Optimization (CSO), SOA, SMA, and WHO optimization. The settling time for the proposed FOPID-PI controller with ASM –WHO is 147.27 s, which is 1.7%, 13.78%, 1.338% and 13.66% better than the controller without optimization, the proposed collateral controller method with either SOA or CSO or SMA, WHO optimization and PI controller respectively. [ABSTRACT FROM AUTHOR]

Few industries use manually controlled robots or automobiles to carry material to the desired position, and in some cases, man power are used due to a lack of money. This cannot be used all the time, in all places and all conditions. So, it is very tranquil to have robots which can follow a specific human by following the unique coloured object held by that person. So, we propose a robotic system that uses robot vision and deep learning to get the required linear and angular velocities, which are ν and ω, respectively, making the robot avoid static and dynamic obstacles when following the unique coloured object. We propose a novel LSTM cell called TF-LSTM, which makes the proposed methodology very accurate in tracking the object in 3D space. TF-LSTMs or target follower LSTMs are inspired by the traditional LSTMs and give a meagre error in linear and angular velocity prediction. The PI controller, which was used to control the linear and angular velocities, which in turn controls the position of the robot, gave us impressive results, and this methodology outperforms all other methodologies for precise target tracking in performance comparision. The proposed TF-LSTM gave us an accuracy of 96.1%, average linear jerk of 0.4 m/s3, average angular jerk of 30 degrees/s3, average clearance of 0.514 m, maintaining no collosions with the obstacles. [ABSTRACT FROM AUTHOR]

In this work, a relay based automatic tuning method is proposed for the Predictive PI control scheme. The proposed method comprises of two steps, namely process model identification by utilizing the standard relay feedback test and ultimate gain determination by once applying again the test of standard relay feedback. First Order Plus Dead-Time model is identified from the first relay feedback test information. By using that model, the dynamic part of the controller is designed and augmented with the process. Once again, feedback test for relay is performed for process augmented with dynamic part of the controller to find the ultimate loop gain. Then, the controller gain is tuned for the user specified gain margin specification. Simulated outputs are exhibited to show the performance and easiness of the proposed automatic tuning method. Further, real-time validation on two-tank non-interacting system is presented. The proposed relay based method is more suitable for industrial process control applications due to the autotuning feature, easiness and performance. [ABSTRACT FROM AUTHOR]

Due to a non-linearity characteristic for linear synchronous reluctance motor (LSRM) magnetic circuit, which generate significant overshoots and oscillations in the device's movement. Two different kinds of controllers are used to verify the position of the motor: a traditional Proportional Integral (PI) controller and a modified camel traveling algorithm (MCTA) with PI controller. In the first the dynamic model of LSRM is presented in d-q reference frame and simulated using MATLAB Simulink program (MATLAB 2022a) with using actual position as a feedback signal. The motor position is tested in three different reference position trajectories: trapezoidal reference position trajectory, linear reference position trajectory, and non-linear reference position trajectory. The outcomes demonstrate how well the employed controllers improved the position and velocity responses of the motor performance under various reference position trajectories. [ABSTRACT FROM AUTHOR]

Many applications require variable speed operation. The motor speed needs to be controlled to save more power. It became highly imperative for further research to find out an efficient method that can yield an optimal speed control. This study presents the design and modeling of a proportional derivative controller for a three-phase induction motor speed control. The scalar control methodology was used to investigate the performance of motor speed control. For the controller approach, proportional-integral (PI) and proportional-derivative (PD) controllers were investigated. Simulation results using MATLAB (Simulink) software is presented. Furthermore, investigation of the PI, and PD performance shows that the PI controller performed poorly due to existence of high peaks, overshoots and rise time. However, the PD controller yielded satisfactory results. Hence, PD controller revealed a better performance with a 1.01s settling time and a steady state error of 1.0 as against that of the PI controller which was unstable, an overshoot of approximately 96% and a steady state error of 1.22. [ABSTRACT FROM AUTHOR]

Wind energy conversion systems (WECS) are emerging as among the most reliable alternative energy sources, requiring innovative control strategies to improve performance and reduce operating costs. The objective of this paper is the implementation of sliding mode control (SMC) for a permanent magnet synchronous generator (PMSG) to optimize the dynamical response and wind turbine system (WTS) stability. The control architecture comprises the PMSG, an AC-DC power converter, and a DC bus link, with a particular focus on extracting the optimum wind turbine power during variable wind speeds. A complete system model was developed and tested in MATLAB for a PMSG of 2 MW subjected to uncertain fluctuations in wind speed. The simulation results confirm that the SMC technique significantly improves speed-tracking accuracy, minimizes current ripple, and improves overall system stability. In addition, the proposed method ensures robust performance under non-linear and uncertain conditions, making it a viable solution for large-scale wind applications. These results highlight the potential of SMC to enhance the accuracy and efficiency of the WECS.

To encourage an eco-friendly environment and pollution-free transportation, most of the automobile industries are promoting electric vehicles. However, with the adoption of electric vehicles, various power quality problems are encountered mainly during vehicle battery charging. Thus, this research work focuses on power quality improvement in electric vehicle battery charging stations. In this article, a closed-loop battery current-controlled zeta converter with a PI controller is introduced to achieve quality power to charge electric vehicles. The proposed converter enhances the overall performance of the system by reducing voltage fluctuations, harmonic content, and frequency variations. Besides, this suggested closed-loop battery-controlled zeta converter improves the power factor and overall efficiency of the system. The converter provides a wide range of output with ripple-free current. In the proposed scheme, the vehicle battery current feedback to the PI controller generates the switching pulses, thereby generating the desired duty ratio to operate the converter to maintain a constant current. The entire system is implemented in MATLAB/Simulink and various power quality parameters namely voltage and current characteristics, active and reactive power characteristics, frequency, total harmonic distortion (THD), power factor, and efficiency are measured. To validate the usefulness of the proposed scheme, it is compared with conventional buck converter-based charging station and conventional zeta converter-based charging station. From the results, it is found that the proposed closed-loop battery current-controlled zeta converters charging station produce improved power quality characteristics over conventional methods. It achieved a voltage THD of 4.93%, current THD of 1.9%, power factor of 0.96, and efficiency of 91.8%, which are far better than the conventional buck and zeta models.

This paper introduces a novel method to improve the efficiency of grid-connected wind-solar cogeneration systems. It involves the integration of Voltage-Source Converters (VSCs) with a Proportional-Integral (PI) tuning controller that has been optimized using Particle Swarm Optimization (PSO). Integrating renewable energy sources into power grids presents a set of challenges in terms of ensuring stability and optimizing power flow. VSCs are essential for effectively managing power flow between renewable sources and the grid. The PI controller is vital in maintaining voltage levels and ensuring stable operation. Conventional PI controller tuning methods commonly rely on heuristic approaches, which might only partially optimize performance across various operational conditions. In order to tackle this issue, PSO is utilized to fine-tune the parameters of the PI controller automatically. This results in a significant reduction in system deviations and a notable improvement in efficiency, even when faced with fluctuating wind and solar conditions. The simulation results conducted in MATLAB/Simulink confirm the effectiveness of the proposed approach in enhancing system stability and reducing response times. The PI controller optimized using PSO showcases exceptional adaptability to varying environmental and grid conditions, resulting in minimized power fluctuations and improved grid reliability. This study makes a valuable contribution to the field of renewable energy integration. It provides valuable insights into enhancing the performance of grid-connected wind-solar cogeneration systems through advanced control techniques and optimization algorithms such as PSO.

DC-DC converters play a critical role in various applications, including DC microgrids, electrical vehicles, data centres and aerospace power systems. This article provides a description of a non-isolated Dual Input Single Output Quadratic Boost DC-DC Converter (DISOQBC) designed for DC microgrids. PV module and battery are low dc voltage power generators. DC microgrid requires high voltage for its operation effectively. By utilizing the proposed converter, the PV module and battery are integrated with separate sources. Achieving a high efficiency and voltage conversion ratio, the proposed converter is distinguished by low components used. The operation of the converter, component design, steady state analysis, and small-signal model has been reported. The proposed converter's benefits include the converter's ability to handle more than one source, deliver more gain than the conventional converter, simple control and reduced losses. The outcome of simulating the converter validates the proposed converter's theoretical operation. The laboratory prototype is ultimately employed to validate and verify the performance of the proposed DISOQBC.