Conventional Proportional Integral Controller Research Articles

Performance Improvement of Predictive Voltage Control for Interlinking Converters of Integrated Microgrid

Voltage quality in Renewable Energy System may be harmed by varying power demand and fluctuating energy source outputs. The model predictive voltage (MPV) control approach is suggested in this study. The proposed control strategy enables appropriate power interactions between the AC and DC subgrids while sharing power fluctuations in a coordinated way. Both the AC and DC subgrids support each other in accordance with their normalized relative changes in AC frequency and DC voltage, respectively. A typical photovoltaic (PV) wind-battery-based hybrid AC/DC microgrid is modelled and investigated, and the usefulness of the proposed scheme is validated under the renewable energy variations and load perturbations. MATLAB/Simulink has validated the efficacy of the suggested strategy. A set of comparative tests was conducted between conventional Proportional-Integral (PI) control, Sliding Mode Control (SMC) strategies, and a novel Model Predictive Voltage Control (MPV) approach under diverse conditions. The aim was to comprehensively assess the advantages of the proposed MPV method. Results demonstrate that, in contrast to traditional PI and SMC control strategies, the MPV technique exhibits distinct benefits in terms of superior control performance and achieves exceptionally low Total Harmonic Distortion (THD) values. Ultimately, the proposed solution not only raises the performance standard but also ensures the stable operation of the Renewable Energy System (RES).

Artificial Neural Network Control Applied to a Photovoltaic-Battery Microgrid System

This paper deals with artificial neural network (ANN) applied to control a standalone microgrid in French Guiana. ANN is an artificial intelligence technique used to control non-linear and complex systems. ANN associated with the Levenberg–Marquardt (LM) algorithm has many advantages, such as rapid decision-making and improved system transients. Therefore, this technique should be adapted for the control of photovoltaic (PV) systems in the tropical climate of French Guiana with high variation in irradiance. The microgrid is composed of a PV source and a storage battery to supply an isolated building which is modeled by a DC load. The PV source is controlled by an ANN-based MPPT (Maximum Power Point Tracking) controller. To validate our ANN-MPPT, we compared it with one of the very popular MPPT algorithms, which is the P&O-MPPT algorithm. The comparison results show that our ANN-MPPT works well because it can find the maximum power point quickly. In the case of battery control, we tested two feed-forward backpropagation neural network (FFBNN) configurations called method1 and method2 associated with the Levenberg–Marquardt (LM) algorithm. We varied the number of hidden layers in each of these two FFBNN configurations to obtain the optimal number of hidden layers for each configuration which optimizes battery control. Method1 is chosen because it is better than method2, in a sense that it respects the maximum amplitude of the battery current for our application and improves the transient regimes of this current. This best configuration (method1) is then tested with two other learning algorithms for comparison: Bayesian regularization (BR) and scaled conjugate gradient (SCG) methods. The system performance with LM algorithm is better than SCG and BR algorithms. LM algorithm improves the performance of the system in transient regimes while the results obtained with the SGG and BR algorithms are similar. Then, we focused on the advantage of using ANN control compared to the conventional proportional integral control (PI control). The comparison results showed that ANN control associated with the LM algorithm (ANN-LM) made it possible to reduce battery current peaks by 26% in transient regimes compared to conventional PI control. Finally, we present and discuss the results of our simulation obtained with the MATLAB Simulink software.

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station’s performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system’s simulation results are provided and scrutinized to confirm the concept’s suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations.

Model predictive control (MPC) applied to a simplified model, magnetic nanoparticle hyperthermia (MNPH) treatment process

Magnetic nanoparticle hyperthermia (MNPH) has emerged as a promising cancer treatment that complements conventional ionizing radiation and chemotherapy. MNPH involves injecting iron-oxide nanoparticles into the tumor and exposing it to an alternating magnetic field (AMF). Iron oxide nanoparticles produce heat when exposed to radiofrequency AMF due to hysteresis loss. Minimizing the non-specific heating in human tissues caused by exposure to AMF is crucial. A pulse-width-modulated AMF has been shown to minimize eddy-current heating in superficial tissues. This project developed a control strategy based on a simplified mathematical model in MATLAB SIMULINK® to minimize eddy current heating while maintaining a therapeutic temperature in the tumor. A minimum tumor temperature of 43 [°C] is required for at least 30 [min] for effective hyperthermia, while maintaining the surrounding healthy tissues below 39 [°C]. A model predictive control (MPC) algorithm was used to reach the target temperature within approximately 100 [s]. As a constrained MPC approach, a maximum AMF amplitude of 36 [kA/m] and increment of 5 [kA/m/s] were applied. MPC utilized the AMF amplitude as an input and incorporated the open-loop response of the eddy current heating in its dynamic matrix. A conventional proportional integral (PI) controller was implemented and compared with the MPC performance. The results showed that MPC had a faster response (30 [s]) with minimal overshoot (1.4 [%]) than PI controller (115 [s] and 5.7 [%]) response. In addition, the MPC method performed better than the structured PI controller in its ability to handle constraints and changes in process parameters.

Analysis of interval type-2 fuzzy controller based interleaved sepic PFC converter for PQ enhancement in power solutions

The Power solutions have become indispensable for all the devices in recent years with an appropriate power conversion circuitries and control methods to ensure good dynamic response, improved stability, reliability and efficiency. The main intent of this article is to impart the designing of interval type-2 fuzzy logic controller (IT2FLC) based interleaved Sepic power factor correction (PFC) converter. This work also involves the careful design of the robust controller with enhanced precision and good power quality (PQ) performance at the AC mains. In addition, the development of IT2FLC based power solution improves the overall power conversion with stabilized output in the perspective of its quick rise time, less overshoot and fast settling time in comparison to other traditional controllers. Further, the uncertainties and issues associated with the conventional proportional integral (PI) and fuzzy logic controllers (FLCs) are handled effectively by the proposed IT2FLC controller. Moreover, this preferred converter is modeled with an internal parasitics and its performances are evaluated and compared with other conventional Zeigler Nicholas (ZN) tuned PI controller and FLC by dint of MATLAB/Simulink platform. Finally, the experimental test bench set up of 250 W, 48 V power circuitry is devised and the test outcomes confirm the excellent transient behavior and PQ performances of the modeled power solution.

Research on adaptive feedforward control method for Tiltrotor Aircraft/Turboshaft engine system based on radial basis function neural network

The paper proposes an adaptive feedforward control approach based on a radial basis function neural network to address the issue of poor disturbance rejection capability in conventional integrated control methods for tiltrotor aircraft propulsion systems. Firstly, a comprehensive simulation model of the tiltrotor aircraft and turboshaft engine is established, and its accuracy is validated against the results of the General Tiltrotor Integrated Simulation Code. Building upon this, a cascade Proportional-integral control parameter design method based on separation principles is proposed to tackle the problem of designing control parameters for the Proportional Integral main loop structure. Subsequently, an adaptive feedforward control method that takes into account both aircraft-induced disturbances and model uncertainties for turboshaft engines is presented using a Radial Basis Function neural network. This approach includes a nonlinear estimator capable of estimating load disturbances and a Radial Basis Function adaptive feedforward controller. Furthermore, stability proof of the closed-loop system is provided based on Lyapunov function analysis. Finally, digital simulations and hardware-in-the-loop experiments are conducted based on a typical tiltrotor flight mission. The results demonstrate that the proposed method effectively mitigates the power turbine drop compared to the conventional Proportional Integral controller with collective pitch feedforward.

Temperature control of beer fermentation based on variable domain fuzzy PID and neural network technology and its application analysis

AbstractTemperature control in the beer manufacturing process is crucial for product quality. Given the gap between China's automation in beer production and the international level, improving the technology in this area has gradually become a core issue in optimizing domestic beer production. This study combines a proportional integral derivative controller with a fuzzy modeling strategy and incorporates a variable‐domain structure to propose a variable‐domain fuzzy proportional integral derivative controller control method. To cope with the challenges of production interaction, the study also introduces neural network technology. The experimental data indicated that the variable‐domain fuzzy proportional integral derivative controller outperforms the conventional proportional integral derivative controller and the fuzzy proportional integral differential controller in terms of overshooting, with a maximum overshoot of only 1.0, compared with 0.50 and 0.70, respectively. The variable‐domain fuzzy proportional integral differential controller exhibited a minimal overshoot of only 0.01 when the model parameter is increased by 20%. In comparison, the other methods reach overshoot values of 0.92 and 1.0. The proposed method maintained superior stability even under the influence of impulse disturbance, step disturbance, and modeling variations. These results demonstrated that the research method is significantly more stable than both the proportion integration differentiation (PID) controller and fuzzy PID controller in complex dynamic parameter environments. The proposed method involved 60 rounds of neural network control, which was successfully implemented. The temperature readings T1 and T2 remained stable within the range of 1.0%–1.02% throughout the experiments. The study demonstrates that the proposed methods have higher accuracy and less fluctuation in actual application, making them more available. Taken together, the above results show that the combination of variable‐domain fuzzy PID controller and neural network technology in beer production has achieved excellent control results. This study not only provides a strong technical support for the progress of beer production technology in China, but also has important industrial application value and wide promotion prospects.

Dynamic performance enhancement of nonlinear AWS wave energy systems based on optimal super-twisting control strategy

This paper presents a Super-Twisting Algorithm (STA) sliding mode control technique as an alternative to the conventional Proportional-Integral (PI) control system. This application is specifically in the context of a nonlinear Archimedes Wave Swing (AWS) device, which functions as a wave energy converter system (WECS) connected to the power grid. The main goal is dynamic performance enhancement of such wave energy systems under network disturbances. Two STA controllers areessential for the rectifier control system in the grid-connected system to capture the most power from waves while limiting losses in the AWS linear generator. In addition, four STA controllers are incorporated into the inverter control loop to preserve the DC link and common coupling voltages at the preset values. The Honey-Badger Algorithm (HBA) is applied for the optimization of gain parameters of the STA controllers, which are compared to the PI gains adjusted using the coot, the hybrid augmented grey wolf optimizer and cuckoo, and PSO search techniques to assess the worthiness of adopting this new control approach. The grid-connected AWS experiences a variety of faults, includingsymmetrical and unsymmetrical faults with successful and unsuccessful breaker reclosures. Finally, the system is experimentally validated using the OP4510 real-time simulator. The experimental results reveal that the HBA-STA controllers outperformPI controllers in omitting fluctuations in the regulated variables, making the STA a strong contender as a control method.

Improved frequency control performance of restructured multi-area power system through optimized controller design

This paper presents an optimal cascaded three degree of freedom proportional integral (3DOF PI) controller and compares its performance with conventional proportional integral (PI) controller for load frequency control of two area deregulated power system. The 3DOF PI controller enhances the degree of freedom and gives better dynamic response. Genetic algorithm (GA) is used to get the optimal gains of the proposed controllers. In the considered two area thermal-gas-fuel cell system, the thermal system consists of a reheat turbine with appropriate generation rate constraint non linearity. Battery storage and unified power flow controller (UPFC) are also assumed to be present. The performance of the proposed controllers is evaluated under different operating conditions that take place in deregulated power market. To tackle any contract violation this article emphasizes on the ancillary services provided by electric vehicles (EVs) and distributed generations. A robustness analysis is also presented by varying parameters from their nominal values.

Modular Open Source Solar Photovoltaic-Powered DC Nanogrids with Efficient Energy Management System

Initially the concept of a DC nanogrid was focused on supplying power to individual homes. Techno-economic advances in photovoltaic (PV) technology have enabled solar PV stand-alone nanogrids to power individual devices using device-specific architectures. To reduce costs and increase accessibility for a wider range of people, a modular open-source system is needed to cover all applications at once. This article introduces a modular PV-powered nanogrid system, consisting of a do it yourself (DIY) PV system with batteries to allow for off-grid power. The resultant open-source modular DC nanogrid can deliver DC power to loads of different voltage levels, which is possible because of the efficient and parametric energy management system (EMS) that selects modes of operation for the grid based on DC bus voltage and state of charge of batteries. Simulation results verify the coordination between the EMS and the PV-battery system under varying PV power generation and load conditions. This EMS has potential to enable easy personalization of a vast area of applications and expand appropriate technology for isolated communities. A thorough stability analysis has been conducted, leading to the development of an LQR (Linear Quadratic Regulator) controller as a replacement for the conventional PI (Proportional - Integral) controllers for better transient stability of the system.

Enhancing wind power with permanent magnet synchronous generator control strategies

AbstractDue to the substantial rise in wind power generation, the direct-drive permanent magnet synchronous generator has emerged as a leading technology for efficient variable speed operation, meeting grid demands effectively. This paper presents a comparative analysis of control strategies for permanent magnet synchronous generator based wind turbine using real variable wind speed data from a 2 MW of Tetouan wind farm in Morocco. The proposed approach is based on evaluating two primary control strategies: the adaptive fuzzy-proportional-integral controller and the conventional proportional-integral controller aimed at enhancing the wind turbine's output power. The simulation performed on MATLAB-Simulink indicates that pitch control mechanisms play a crucial role in optimizing power generation, also demonstrating its ability to achieve satisfactory performance.

A Control Architecture for Regulating Voltage and Power Flows in a Networked Microgrid System

This paper presents a unique control system to regulate power exchanges and load bus voltage in a networked microgrid (NMG) system comprising AC and DC microgrids. During the islanding of a microgrid in this NMG system, load voltage and power balance can get disturbed. A control system and associated converter and inverter control methods are presented to rectify these issues. An efficient model predictive control (MPC) method, which gives a tracking error of 50% lower than a conventional proportional-integral (PI) controller, is used to control multiple inverters in the NMG system. Simulation studies are conducted to test the NMG in islanding and load change scenarios. With the help of these studies, it is verified that the MPC-controlled inverters can provide better tracking accuracy in achieving desired power flows in the NMG system.

A novel control strategy of a variable-speed doubly-fed-induction-generator-based wind energy conversion system

Abstract This paper aims to address the issue of control of a variable-speed wind turbine based on doubly-fed induction generators. In this work, an effort is made to extract the maximum efficiency from a doubly-fed induction generator-based variable-speed wind turbine by controlling the rotor current. In the first step, a maximum power point tracking technique is used to extract the maximum power from the turbine. Then a stator-flux-oriented vector control strategy is employed to control the rotor-side current. Subsequently, a grid voltage vector-oriented control strategy is used to control the grid-side system of the grid-connected generator. Considering the nonlinearity and parameter uncertainty of the system, an active disturbance rejection controller with a sliding-mode-based extended-state observer is developed for the above-mentioned control strategies. Furthermore, the stability of the controller is tested and the performance of the controller is compared with the classical proportional–integral controller based on disturbance rejection, robustness and tracking capability in a highly non-linear wind speed variation scenario. Modelling, control and comparison are conducted in the MATLAB®/Simulink® environment. Finally, a real-time hardware set-up is presented using the dSPACE ds-1104 R&D processing board to validate the control scheme. From the result of the experiments, it is seen that the proposed controller takes 10–15 control cycles to settle to its steady-state values, depending on the control loop, whereas the conventional proportional–integral controller takes 60–75 control cycles. As a result, the settling time for the proposed control scheme is shorter than that of the proportional–integral controller.

Honey Badger Algorithm Based PI Controller for DC Link Voltage Control of Solar Photovoltaic System Connected to Grid for Enhanced Power Quality

The parameters of the proportional integral (PI) controller used for DC link voltage control play a vital role in regulating the DC link voltage of grid-connected solar photovoltaic system that experiences heavy DC voltage fluctuation and various power quality issues due to integration of non-linear loads, varying irradiance and loading conditions. The existing strategies might fail to respond to the above complications due to fixed PI gains which restrict their scope of controlling domain. Therefore, adaptive PI gain values should be selected to quickly adjust to the varying conditions and dynamically obtain the optimal control settings. This article employs the honey badger algorithm (HBA), developed in Matlab to adaptively tune the PI controller gains value by minimizing the DC link voltage deviation. The advantages of using this technique over conventional PI controllers are its quick response, adaptability, stability, and optimal self-tuning process. The proposed algorithm has been tested under steady-state and dynamic conditions for different irradiation and loading conditions. Further, results of the proposed algorithm have been compared with PI controller and particle swarm optimization-PI controller to validate the efficacy of the proposed algorithm for grid-connected solar photovoltaic systems. It has been observed that the proposed algorithm maintains THD <3% in all the tested conditions.

Improved Proportional Integral (PI) controller for water level control in open channel systems: A case study of the Middle Route Project for South-to-North Water Transfer

Study regionThe three pools near the terminal of the main canal of the Middle Route Project for South-to-North Water Transfer in China. Study focusThe conventional Proportional Integral (PI) controller neglects the potential for improved control performance by utilizing a PI controller with adaptable control parameters based on different water level conditions, particularly in large-scale open channels with significant water level variations. To address this limitation, this study proposes a novel hierarchical Proportional Integral (H-PI) control approach. The H-PI approach incorporates a weight coefficient into the control logic of the PI controller, allowing flexible adjustment of control parameters. These parameters are tuned using a hydrodynamic model integrated with a genetic algorithm for optimization. New hydrological insights for the regionThe simulation results of the study area manifest the efficacy of the proposed method in substantively diminishing gate control frequencies in contrast to other methods, specifically for gate deadbands of 3 cm and 6 cm, respectively. Moreover, the proposed method exhibits higher sensitivity to significant water level deviation changes. The robustness of the proposed method is evaluated under different disturbance scenarios, confirming its resilience and practical feasibility for implementation in the study area. In summary, the proposed H-PI control approach offers distinct advantages in minimizing water level deviation and gate control frequency. Furthermore, its robustness under different disturbance scenarios further supports its practical utility.

ANFIS Controlled MMC-UPQC to Mitigate Power Quality Problems in Solar PV Integrated Power System

This paper focuses on the implementation of a Modular Multilevel Converter (MMC) based Unified Power Quality Conditioner (UPQC) in a grid-integrated Photovoltaic (PV) system. The integration of photovoltaic (PV) systems into the grid brings numerous benefits in terms of renewable energy utilization like sustainable energy generation, energy security, grid resilience, and economic growth. However, the voltage source converters (VSCs) employed in PV systems can introduce power quality (PQ) issues, including voltage fluctuations, harmonics, and reactive power imbalances and grid instability. The objective of this work is to enhance power quality by mitigating voltage and current fluctuations and harmonics generated due to VSC, compensating reactive power requirements by the load and PV system, and regulating the DC link voltage. For DC voltage regulation, an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is employed to control for MMC-UPQC. The proposed control strategy is simulated using MATLAB/SIMULINK software and the results are compared with conventional Proportional Integral (PI) and fuzzy controllers. Various dynamic conditions such as voltage sag and swell at the point of common coupling (PCC), changes in irradiance for the PV system, and load variations are considered. The simulation results demonstrate that the ANFIS-controlled MMC-based UPQC outperforms the PI and fuzzy controllers in terms of power quality improvement. It effectively suppresses harmonics, maintains a stable DC link voltage, and compensates for reactive power fluctuations. The proposed control strategy provides superior performance and achieves better power quality compared to conventional controllers. The findings of this work highlight the potential of using an ANFIS controller in MMC-based UPQC systems for grid-integrated PV systems.

Performance analysis of FEC based SR motor drive using fuzzy tuned PI controller

Purpose In this paper, a new front end converter (FEC) topology has been proposed for the switched reluctance (SR) motor drive. This study aims to present the performance analysis of FEC-based SR motor drive using various types of control schemes like conventional proportional integral (PI) controller, fuzzy logic controller (FLC) and fuzzy-tuned proportional integral controller (Fuzzy-PI). Design/methodology/approach The proposed FEC-based SR motor drive with various control strategies is derived for the torque ripple minimization and speed control. Findings The steady state and the dynamic response of the FEC-based SR motor drive are analyzed using three different controllers under change in speed and loading conditions. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller. Originality/value The hardware prototype has been implemented for the FEC-based SR motor drive by using the Xilinx SPARTAN 6 FPGA processor. The experimental verification has been conducted and the results have been measured under steady state and dynamic conditions.

Parameter Estimation and Predictive Speed Control of Chopper-Fed Brushed DC Motors

This paper presents an effective speed control method for brushed DC motors fed by a DC chopper using the concept of Finite Control Set-Model Predictive Control (FCS-MPC). As this control algorithm requires the parameters of the controlled object, the estimation of motor parameters is first performed by using two types of data. The first data includes the output speed response corresponding to the step input voltage to obtain the transfer function in the no-load regime. The second data consists of the motor speed and armature current when a load torque is applied to the motor shaft. The discrete-time equation of the motor armature circuit is used to obtain the future values of the armature circuit current and the motor speed. A cost function is defined based on the difference between the reference and predicted motor speed. The optimal switching states of the DC chopper are selected corresponding to the maximum value of the cost function. The performance of the proposed speed control algorithm is validated on an experimental system. The simulation and experimental results obtained show that the MPC controller can outperform the conventional proportional-integral (PI) controller.

Comparison of control strategies for thermoelectric generator emulator

Thermoelectric generator (TEG) can directly convert heat energy into electrical energy. It improves the power efficiency of the energy generation system by converting the power loss in the form of heat produced during the generation process into additional electrical energy. The TEG emulator (TEGE) is a power converter that produces a similar current-voltage characteristic as the TEG. It is a valuable device used to develop and test the TEG-based energy generation system. Nonetheless, the research on the TEGE is still in the early stage. This paper proposed a proper, low-cost, and high-efficient TEGE design using the buck converter. The contribution of the paper covers the TEG model in the form of an array, the buck converter design tailored to the TEGE, and 4 new control strategies proposed for the TEGE. The control strategies are the direct referencing method (DRM), perturb and observed (PnO) method, resistance comparison method (RCM), and resistance feedback method (RFM). The conventional proportional-integral controller is used to maintain a smooth operation during transient and steady-state periods. The results show the merits or demerits for each proposed control strategy based on the accuracy, transient response, stability, overshoot, and efficiency.

Hierarchical bi-level load frequency control for multi-area interconnected power systems

To overcome the shortcomings of proportional–integral (PI) control, a linear quadratic regulator (LQR) is proposed in this study, to determine the set points of the PI controllers in a two-level hierarchical manner. The application of the proposed scheme is studied on load frequency control (LFC) of a power system with multiple control areas (CAs). At the lower control level, a discrete PI controllers are used to regulate the frequency and tie-line powers of each of the CAs. To achieve an improved closed loop performance, the local PI controllers are optimally tuned using moth flame optimization (MFO) algorithm by minimizing the Integral Square Error (ISE) of the state errors with respect to local information and interactions with neighboring CAs. While at the upper control layer, an optimized finite-horizon LQR is applied as a guide to establish the optimal set-points of the lower level PI controllers. Since only few of the state variables can be accessed, Kalman filter (KF) is applied as an observer for the estimation of the remaining states. The efficacy of the proposed scheme is verified by implementing it on a three-CA system perturbed with a step and random net disturbances formed by combining the fluctuations in the output power of DFIG-based wind turbine generator and random load demand. From the simulation results, it is established that the proposed LQR-PI supervisory scheme has outperformed the conventional PI control scheme in optimality and stability.