MATLAB Simulation Research Articles

Robust MG control considering uncertain constant power load

The destabilization of Microgrids (MGs) is attributed to the negative incremental resistance of constant power loads (CPLs). Furthermore, the inadequately damped modes of the droop control, typically employed for distributed generation (DG) power sharing, contribute to disturbances in MG dynamic performance. This paper introduces a novel control strategy aimed at mitigating instability issues associated with both CPL and droop controllers in MGs. The proposed approach utilizes an optimized H∞ control scheme, incorporating the Whale Optimization Algorithm (WOA), to enhance stability on both the CPL and distributed generation (DG) sides of the MG. The proposed controller is modeled and validated through comprehensive simulations in MATLAB, demonstrating its effectiveness under various uncertainty scenarios and disturbance conditions. These simulations demonstrate the controller's capability to effectively mitigate disturbances, optimize DG power sharing, and regulate the DC voltage of the CPL, thereby enhancing overall microgrid stability and performance. Additionally, a comparison between the proposed robust H∞ and conventional droop control schemes highlights the superiority of the proposed approach, with results confirming improved MG dynamic performance. The proposed controller exhibits a notable enhancement in Minimum Voltage Deviation (MVD), showing a substantial improvement of 96.19%. Additionally, both the Critical Response Time (CRT) and Settling Response Time (SRT) witness significant improvements of 95.33% and 97.69%, respectively, when subjected to system disturbances. Finally, the implemented MG is validated on a Real-Time Digital Simulator (RTDS), affirming the practicality of the proposed compensation technique.

A Novel Optimization Algorithm Inspired by Egyptian Stray Dogs for Solving Multi-Objective Optimal Power Flow Problems

One of the most important issues that can significantly affect the electric power network’s ability to operate sustainably is the optimal power flow (OPF) problem. It involves reaching the most efficient operating conditions for the electrical networks while maintaining reliability and systems constraints. Solving the OPF problem in transmission networks lowers three critical expenses: operation costs, transmission losses, and voltage drops. The OPF is characterized by the nonlinearity and nonconvexity behavior due to the power flow equations, which define the relationship between power generation, load demand, and network component physical constraints. The solution space for OPF is massive and multimodal, making optimization a challenging concern that calls for advanced mathematics and computational methods. This paper introduces an innovative metaheuristic algorithm, the Egyptian Stray Dog Optimization (ESDO), inspired by the behavior of Egyptian stray dogs and used for solving both single and multi-objective optimal power flow problems concerning the transmission networks. The proposed technique is compared with the particle swarm optimization (PSO), multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawk optimization (HHO) and hippopotamus optimization (HO) algorithms through MATLAB simulations by applying them to the IEEE 30-bus system under various operational circumstances. The results obtained indicate that, in comparison to other used algorithms, the suggested technique gives a significantly enhanced performance in solving the OPF problem.

Monte Carlo Modeling and Simulation of Electron Dynamics in Low Temperature Methane Gas

This study examines the collisions of electrons with methane molecules to determine the cross-sections required for calculating electron transport coefficients in methane gas. Employing Monte Carlo Simulations in MATLAB, critical transport characteristics, including electron mobility and diffusion coefficients, were computed. These simulated coefficients are subsequently compared to experimental data to validate the accuracy of the current study’s findings. This comprehensive approach ensures the precision of the performed calculations and their alignment with empirical evidence, thereby enhancing the understanding of the complex interactions and dynamics between electrons and methane molecules in this system.

Probabilistic modeling and optimization of microgrids with EV parking lots and dispersed generation

Distributed generation sources provide self-governing power during outages, making microgrids and islanded distribution networks vital for service endurance, superior power quality, reliability, and operative efficiency. However, microgrids structure are difficult to control, particularly in islanded mode where no main power source exists if the main grid fails. Fast responses from discrete generation sources using power electronics can undermine the grid during faults or normal operations without proper regulations. The double-fed induction generator (DFIG) has become the preferred wind turbine generator owing to its low cost and flexibility to varying wind speeds. This paper presents a probabilistic scheduling for day-ahead microgrid programming that includes EV parking lots and dispersed generation resources. The microgrid works in both normal and islanded modes depending on main grid conditions. The uncertainty in EV parking lot usage is modeled hourly using the Z-number method, while wind and solar generation, market prices, and loads are modeled using the Monte Carlo method. Scenario-based incidents in the upstream grid that lead to microgrid islanding are considered, focusing on the time and duration of impact. The optimization model accounts for uncertainty, EV charging/discharging, and operational costs under normal and fault conditions. The fault ride-through (FRT) method for maintaining DFIG stability in islanded microgrids are proposed. In this technique stabilizes terminal voltage during faults by employing a resistor in series with the DFIG stator, enhancing voltage stability and FRT capability. Without these methods, the DFIG may lose stability after clearing transient errors, risking generator loss and threatening microgrid stability, particularly in islanded mode. The effectiveness of these control and protection strategies is validated through comprehensive simulations in MATLAB.

Dynamic modeling of flexible-link manipulators with time-varying link lengths operating in fluid medium

This paper presents the derivation of dynamic equations of motion for flexible-link manipulators with time-varying link lengths operating in stable fluid environments. The modeling considers the interaction between the manipulator and the surrounding fluid during simultaneous rigid-body and elastic motions. The time-varying link lengths introduce additional translational movements, which contribute to the overall rigid-body motion, comprising rotational and reciprocating motions, as well as link oscillations due to elasticity. These motions are influenced not only by the motor excitations but also by the interaction between the manipulator links and the fluid medium. Notably, the fluid interactions introduce resistive forces during linear motions, leading to deviations from the behavior of manipulators with fixed link lengths. Consequently, the fluid-robot interaction affects both the elastic and rigid-body modes. Although the equations share similarities with those for time-varying structures studied previously, the presence of resistive forces results in a complex model with new non-conservative forces. The system dynamic equations are derived using a recursive Gibbs-Appell formulation, accounting for changes in the fluid's mechanical properties, link elasticity, and the effects of system mass variations. The results are verified using MATLAB simulations. The outcomes show that the link's deformation increased by 100% when the medium's density changed from vacuum to fluid with a density of 100 kg/ m3.

Optimized control strategy for a three-phase grid connected inverter using PI controller and DQ frame

This paper provides a proportional-integral (PI) controller and direct-quadrature (DQ) frame transformation-based optimum control method for a three-phase grid-connected inverter. In terms of grid synchronization, voltage regulation, and harmonic abatement, the proposed control technique attempts to improve the inverter's performance. By separating the control of active and reactive power, the control structure is made simpler and independent regulation of these parameters is possible. This improves the inverter's capacity to quickly react to grid disruptions and track reference values accurately. In order to lower carbon emissions and improve grid dependability, it has become vital to integrate renewable energy sources into the current power grid. Grid-connected inverters are essential in this situation because they transform DC electricity from renewable sources into grid-safe AC power. This abstract outline a proportional-integral (PI) controller and direct-quadrature (DQ) frame-based optimal control method for a three-phase grid-connected inverter using a MATLAB simulation.

Low cost pulsed electric field generator using DC-DC boost converter and capacitor diode voltage multiplier

Traditional high-voltage pulse generators, like Marx generators often face challenges related to efficiency and complexity. In this paper, a solid-state multi-module high-voltage pulse generator that integrates capacitor-diode voltage multipliers (CDVM) with DC-DC boost converters and closed-loop voltage control is proposed to overcome these challenges. The system achieves high output voltage by coupling the pulsed output voltages of individual low-voltage DC sources in series across each module. The proposed design was modeled using MATLAB, and experimental testing was conducted on a single stage. Comparative analyses between timedomain parameters, proportional-integral (PI), and fractional order proportional integral derivative (FOPID) controllers were performed. Both MATLAB simulations and experimental validations demonstrate the effectiveness of this approach. The rise time, peak time, settling time, and steady-state error are all improved using an FOPID controller, decreasing from 0.32 to 0.31 seconds, 0.42 to 0.35 seconds, and 3.15 to 2.20 seconds, respectively. These findings indicate that a closed-loop FOPID controller enhances time-domain performance parameters more effectively than a PI controller for a two-stage DC-DC voltage multiplier.

Optimal DSSC’s deployment in power system using PSO

Owing to the high cost of installation and operation, distributed flexible AC transmission system (FACTS) technology gives opportunity to provide cost-effective solution in power system operation and control. A distributed static series compensator (DSSC) is a series FACTS device and it is placed equidistantly placed on the existing line to vary the line current. Active power can be controlled in the line with the help of DSSC. This paper presents DSSC for active power flow control to enhance system loadability index and to minimize reactive power generation by generators. Since DSSC is of low power. a small value of reactance is emulated in the line. Large number of DSSC’s are distributed along the transmission line at regular intervals to realize considerable change in the current. A particle swarm optimization is implemented to determine DSSC’s emulated reactance optimally. In this condition, all the lines flowing power in their limits with increased loading condition. Maximum system loadability index is evaluated by employing optimal number of DSSC’s on the line. A multiobjective problem is formulated. One objective function is formed such that no line would become overloaded even when loading is increased. Other objective is formulated to minimize reactive power generation by generators. A compromise solution is investigated for optimally connected of DSSC devices to achieve both the objective functions. IEEE 14 bus system is taken for MATLAB simulation of DSSC compensated system.

Research on nonlinear dynamic vertical vibration characteristics and control of roll system in cold rolling mill

In order to address the vertical vibration phenomenon of the working roll during the rolling process, taking into consideration the nonlinear factors of the rolling interface and the roll system, this study establishes a nonlinear excitation vertical vibration model based on dynamic rolling force. The impact of nonlinear factors on system stability and vibration characteristics is analyzed. The study reveals that nonlinear damping and linear stiffness exhibit significant effects through time delay characteristics. Nonlinear stiffness has a drastic impact on system stability, while increasing system damping effectively reduces the amplitude and narrows the resonance region. By employing averaging method and singular value theory, the stability of the cold rolling mill roll system is analyzed under non-autonomous and autonomous states. A combined time-delay feedback controller is proposed, which effectively suppresses the system’s large-scale vibration by adjusting the control gain and time delay parameters. MATLAB simulations are conducted to validate the accuracy of the control strategy, providing theoretical support for vibration prediction and system design in rolling mills.

Enhanced operation of PVWPS based on advanced soft computing optimization techniques

This study introduces three soft computing (SC) optimization algorithms aimed at enhancing the efficiency of photovoltaic water pumping systems (PVWPS). These algorithms include the Gorilla Troop Algorithm (GTO), Honey Badger Algorithm (HBA), and Snake Algorithm (SAO). The goal of the SC optimizers is to maximize the output power of the PV array (PPV) and enhance the efficiency of the DC motor (η), thereby optimizing the water flow rate (Q) of the pumping system. The analytical modeling approach proposed in this study involves forecasting the optimal duty cycle (Dop) for a buck-boost converter, taking into account variables such as solar radiation (G) and ambient temperature (T). A comparative analysis is conducted between the suggested SC optimizers and analytical modeling. MATLAB simulation is employed to explore an adaptive neuro-fuzzy inference system (ANFIS) trained for the proposed system. The objective is to assess system performance and accuracy. Findings indicate a strong convergence between the analytical model and the simulation model utilizing SC optimizers. Moreover, the neuro-fuzzy system trained offline, coupled with the proposed SC optimizers, demonstrates superior performance compared to traditional control methods like perturb and observe (P&O) and incremental conductance (IC). This superiority is evident across various metrics including motor efficiency (η), photovoltaic (PV) output power (PPV), water flow rate (Q), and time response.

Blood-glucose regulator design for diabetics based on LQIR-driven Sliding-Mode-Controller with self-adaptive reaching law.

Type I Diabetes is an endocrine disorder that prevents the pancreas from regulating the blood glucose (BG) levels in a patient's body. The ubiquitous Linear-Quadratic-Integral-Regulator (LQIR) is an optimal glycemic regulation strategy; however, it is not resilient enough to withstand measurement noise and meal disruptions. The Sliding-Mode-Controller (SMC) yields robust BG regulation effort at the expense of a discontinuous insulin infusion rate that perturbs the BG concentrations. Hence, the novel contribution of this article is the formulation of a hybridized LQIR-driven SMC strategy that retrieves the benefits of the aforesaid control schemes while avoiding their inherent problems. The proposed control approach is realized by linearly combining a glycemic LQIR law with an innovative sign function sliding mode reaching law that is driven by a customized LQIR-driven sliding surface. The hybridized control scheme generates optimal control decisions yielded by the LQIR while mimicking the robustness characteristic of SMC against bounded exogenous disturbances. Additionally, the SMC reaching law in the proposed control scheme is augmented with a nonlinear adaptation mechanism that flexibly modulates the control activity to effectively compensate for the external perturbations while minimizing the chattering content. The controller parameters are numerically optimized offline. The efficacy of the prescribed hybrid control law is analyzed via customized MATLAB simulations that normalize the patient's BG level to 80 mg/dL, under measurement noise and meal disruptions, from an initial hyperglycemic state. The results justify the improved BG regulation accuracy and disturbance-rejection capability of the proposed control procedure.

Rayleigh’s criterion of resolution: experimental demonstration and MATLAB simulation

Abstract This article presents a method for developing a low-cost apparatus to demonstrate Rayleigh’s criterion, which defines the resolution limit of optical instruments. The setup incorporates 3D-printed components, diode lasers, a circular aperture with a radius of 100 µm, and a screen. Additionally, we developed a MATLAB-based graphical user interface, enabling users to simulate and visualize diffraction patterns in both one and two dimensions. This interactive tool enhances the study of Rayleigh’s criterion by comparing simulated diffraction patterns with experimental results, offering an accessible educational approach.

Research on local path planning of unmanned vehicles based on improved driving risk field.

With the rapid development of the field of unmanned vehicles, motion planning based on field theory has become a research hotspot. A driving risk field is an effective means to evaluate driving safety in complex environments, and this method is frequently used in autonomous vehicle motion planning. However, existing risk field models are not sufficiently accurate for describing driving risks, often disregarding the size and driving direction restrictions of vehicles, amongst other aspects. Considering the aforementioned problems, this research improves and establishes a new risk field model, including a motor vehicle risk field, a road risk field and a pedestrian risk field. Simultaneously, it proposes a solution to the local minimum point problem caused by different scenarios and verifies the simulation in MATLAB. Finally, the Prescan and MATLAB/Simulink co-simulation platform is used to compare the traditional and improved field theory algorithms. Results show that the trajectory generated by the improved field theory algorithm is smoother, and the fluctuation amplitude and number of parameters, such as heading angle, yaw rate and roll angle during driving, are significantly reduced. These outcomes improve the stability of driving whilst smoothly reaching the target point, demonstrating high application potential for the proposed model.

EVM-Aware relay path decision with hierarchical modulation for reliable data transmission in cooperative relay networks

This paper presents a novel approach of integrating an error vector magnitude with hierarchical modulation in which HM dynamically adjusts modulation schemes to optimize spectral efficiency under varying channel conditions. Relay nodes are strategically selected based on Signal-to-Noise Ratio (SNR), with a focus on prioritizing high-SNR relays to ensure reliable data forwarding. To further enhance the reliability of the communication system, an EVM-aware relay path decision is proposed which selects the most suitable path based on EVM, considering the quality of the transmitted signal. By considering both SNR-based relay selection and EVM-aware path selection, the proposed system aims to minimize errors and optimize the overall performance of the network. The effectiveness of the proposed system is evaluated through extensive simulations in MATLAB, demonstrating that the proposed adaptive modulation scheme significantly outperforms fixed schemes, particularly under fluctuating channel conditions, ensuring robust and reliable communication across different SNR levels. This shows the potential of the proposed protocol to enhance performance in dynamic, making it particularly relevant for applications where reliable data transfer is critical.

Research on nonlinear second-order multi-agent systems containment control method with inherent time-delay based on compensator

In this study, a nonlinear second-order multi-agent systems containment control method based on compensator is discussed. In order to deal with nonlinear systems with inherent time delay, a new compensator-based control algorithm is proposed. The fully distributed compensator is designed to effectively converge the states of all followers to the convex hull formed by the states of leaders. By introducing a compensator, the errors in the system can be corrected and the influence of time delay on the system can be compensated to ensure that the system can achieve the desired performance and stability. Then, the original system is transformed into an error system by using matrix theory, and the stability of the error system is analyzed by using Lyapunov method, and the sufficient conditions for the solvability of the control problem are obtained. Finally, the effectiveness of the control algorithm is verified by matlab simulation.

IoT Integration of Failsafe Smart Building Management System

This research investigates the energy consumption of buildings managed by traditional Building Management Systems (BMSs) and proposes the integration of Internet of Things (IoT) technology to enhance energy efficiency. Conventional BMSs often suffer from significant energy wastage and safety hazards due to sensor failures or malfunctions. These issues arise when building systems continue to operate under unknown conditions while the BMS is offline, leading to increased energy consumption and operational risks. The study demonstrates that integrating IoT systems with existing BMSs can substantially improve energy efficiency in smart buildings. The research involved designing a system architecture prototype, performing MATLAB simulations, and a real-life case study which revealed that IoT devices are effective in reducing energy waste, particularly in Heating, Ventilation, and Air Conditioning (HVAC) systems and lighting. Additionally, an auxiliary bypass system was incorporated in parallel with the IoT system to enhance reliability in the event of IoT system failures. Preliminary findings indicate that the integration of IoT systems with traditional BMSs significantly boosts energy efficiency and safety in smart buildings. Simulation results reveal an hourly average power savings of 36.8 kw with the integrated failsafe model for all scenarios. This integration offers a promising solution for advancing energy management practices and policies, thereby improving both operational performance and sustainability in building management.

Navigation of a Team of UAVs for Covert Video Sensing of a Target Moving on an Uneven Terrain

Unmanned aerial vehicles (UAVs) have become essential tools with diverse applications across multiple sectors, including remote sensing. This paper presents a trajectory planning method for a team of UAVs aimed at enhancing covert video sensing in uneven terrains and urban environments. The approach establishes a feasible flight zone, which dynamically adjusts to accommodate line of sight (LoS) occlusions caused by elevated terrains and structures between the UAVs’ sensors and the target. By avoiding ‘shadows’—projections of realistic shapes on the UAVs’ operational plane that represent buildings and other obstacles—the method ensures continuous target visibility. This strategy optimizes UAV trajectories, maintaining covertness while adapting to the changing environment, thereby improving overall video sensing performance. The method’s effectiveness is validated through comprehensive MATLAB simulations at both single and multiple UAV levels, demonstrating its ability to prevent LoS occlusions while preserving a high level of camouflage.

Large Scale Ultrafast Manufacturing of Wireless Soft Bioelectronics Enabled by Autonomous Robot Arm Printing Assisted by a Computer Vision-Enabled Guidance System for Personalized Wound Healing.

A Customized wound patch for Advanced tissue Regeneration with Electric field (CARE), featuring an autonomous robot arm printing system guided by a computer vision-enabled guidance system for fast image recognition is introduced. CARE addresses the growing demand for flexible, stretchable, and wireless adhesive bioelectronics tailored for electrotherapy, which is suitable for rapid adaptation to individual patients and practical implementation in a comfortable design. The visual guidance system integrating a 6-axis robot arm enables scans from multiple angles to provide a 3D map of complex and curved wounds. The size of electrodes and the geometries of power-receiving coil are essential components of the CARE and are determined by a MATLAB simulation, ensuring efficient wireless power transfer. Three heterogeneous inks possessing different rheological behaviors can be extruded and printed sequentially on the flexible substrates, supporting fast manufacturing of large customized bioelectronic patches. CARE can stimulate wounds up to 10 mm in depth with an electric field strength of 88.8 mVmm-1. In vitro studies reveal the ability to accelerate cell migration by a factor of 1.6 and 1.9 for human dermal fibroblasts and human umbilical vein endothelial cells, respectively. This study highlights the potential of CARE as a clinical wound therapy method to accelerate healing.

Enhancing load frequency control and automatic voltage regulation in Interconnected power systems using the Walrus optimization algorithm

This paper introduces the Walrus Optimization Algorithm (WaOA) to address load frequency control and automatic voltage regulation in a two-area interconnected power systems. The load frequency control and automatic voltage regulation are critical for maintaining power quality by ensuring stable frequency and voltage levels. The parameters of fractional order Proportional-Integral-Derivative (FO-PID) controller are optimized using WaOA, inspired by the social and foraging behaviors of walruses, which inhabit the arctic and sub-arctic regions. The proposed method demonstrates faster convergence in frequency and voltage regulation and improved tie-line power stabilization compared to recent optimization algorithms such as salp swarm, whale optimization, crayfish optimization, secretary bird optimization, hippopotamus optimization, brown bear optimization, teaching learning optimization, artificial gorilla troop optimization, and wild horse optimization. MATLAB simulations show that the WaOA-tuned FO-PID controller improves frequency regulation by approximately 25%, and exhibits a considerable faster settling time. Bode plot analyses confirm the stability with gain margins of 5.83 dB and 9.61 dB, and phase margins of 10.8 degrees and 28.6 degrees for the two areas respectively. The system modeling and validation in MATLAB showcases the superior performance and reliability of the WaOA-tuned FO-PID controller in enhancing power system stability and quality under step, random step load disturbance, with nonlinearities like GDC and GDB, and system parameter variations.

MEMS capacitive pressure sensor: analysis, theoretical modeling, simulation and performance comparison of the effect of a conical notch

Abstract Micro-Electro-Mechanical Systems (MEMS) pressure sensors are widely used in various applications due to their high sensitivity, low power consumption, and compactness. This work involves the design and simulation of a MEMS-based Touch Mode Capacitive Pressure Sensor (TMCPS). The proposed sensor is based on a substrate with an integrated conical notch featuring a circular diaphragm, aiming to enhance the key performance parameters of the sensor. The integration of a conical notch in the substrate increases the touch area between the diaphragm and substrate compared to the literature, ensuring increased capacitance and capacitive sensitivity. In this work the mathematical analysis of deflection of a circular diaphragm employing thin plate theory, capacitance, and capacitive sensitivity, along with step-by-step explanations, is provided. The results are obtained from MATLAB simulations. The deflection of the diaphragm is validated through Finite Element Analysis (FEA) using COMSOL Multiphysics. The proposed work demonstrates a significant improvement in sensor sensitivity compared to the existing literature.