Proportional-integral Controller Parameters Research Articles

DC Motor Speed Control using Particle Swarm Optimization based on Labview

In Industrial applications, DC motors are commonly applied because of high reliability, ease of control and ability to provide accurate speed. However, to get accurate speed control under several operation conditions such as disturbances and changes in the load is significant challenge. This research explores the implementation of particle swarm optimization (PSO) to tune parameters of proportional-integral (PI) controller. PSO that is a population-based optimization technique, is inspired by the social behavior of swarms. It is a population-based optimization technique. By automation process in the algorithm. Using the tuning process of PSO, it can effectively obtain the parameters of PI controller. experimental hardware using DIGIAC 1750 is used to assess the performance of the proposed method. The parameters of and are 0.7492 and 0.2007, respectively. The results show that the performance of the DC motor using PSO tuned by PI for , , and are 0.3687 s, 0.5106 s, and 0.6051 s, respectively. Furthermore, when the system is given a disturbance, the response can come back again following the setpoint and when the setpoint is changed, the response can follow the setpoint quickly as well. The proposed method can address the challenges associated with DC motor speed control.

Enhancing DC Motor Speed Control Performance Using Heuristic Optimization and Comparative Analysis of Control Methods

Direct Current (DC) motors are an important component that converts electrical energy into mechanical energy, used in a wide range of applications from industrial applications to home appliances. DC motor speed control has an important role in industrial processes to increase efficiency, realize precise movements and optimize energy consumption. In this study, various control methods and parameter optimization techniques for speed control of DC motors, which have a wide range of applications, have been systematically analyzed. The aim of the study is to develop an effective control strategy to ensure that DC motors reach the determined target speed by monitoring them in real time at different speeds and to minimize fluctuations caused by variable loads or external factors. In our study, Proportional-Integral-Derivative (PID), Proportional-Integral (PI), and Proportional-Derivative (PD) control methods were used. The parameters of these controllers were tuned using Matlab Tuned, The Cheetah Optimizer (CO) Algorithm, a new generation heuristic optimization method, and Particle Swarm Optimization (PSO), a widely accepted optimization method. The performances of the controllers were determined using criteria such as Integral of Absolute Error (IAE), Integral Squared Error (ISE), and Integral of Time multiplied by Absolute Error (ITAE). According to the results obtained, it was found that the PID, PI and PD control parameters determined using the CO Algorithm performed better than the controllers created using Matlab Tuned and PSO methods. New optimization methods, such as the CO Algorithm, have been found to have significant potential to improve the performance of control systems. Thanks to this study, it offers a practical approach for optimizing DC motor speed control in industrial processes. As a result, it has been found that the control parameters determined by the CO Algorithm have significant potential in improving the performance of DC motor speed control and control systems compared to other optimization methods.

Adaptive Quasi-Super-Twisting Sliding Mode Control for Flexible Multistate Switch

The mathematical model of a flexible multistate switch (FMSS) exhibits nonlinear and strong coupling characteristics, whereas traditional power decoupling control makes it difficult to completely decouple the output power. The traditional proportional–integral control parameters are difficult to adjust, and their robustness and dynamic performance are poor, which affects the stability of the voltage of the power distribution network and feeder power. To address these problems, this study first converted the original system into a linear system via coordinate transformation using feedback-accurate linearization to decouple active and reactive currents. Thereafter, a super-twisting sliding mode control (ST-SMC) algorithm was introduced, and an adaptive quasi-super-twisting sliding mode control (AQST-SMC) algorithm comprising the quasi-sliding mode function and adaptive proportional term was proposed. An FMSS double closed-loop controller was designed to achieve improved vibration suppression and convergence speed. A three-port FMSS simulation model was developed using MATLAB/Simulink, and the simulation results show that the proposed control strategy enhances the robustness and dynamic performance of the system.

Enhanced proportional integral controller for DSTATCOM with particle swarm optimization algorithm in power system

Power system expansion planning is supported by using distributed generation (DG) instead of installing new main power system generators. The portability and autonomous operation of the DG make it capable of integrating into the distribution system. Power electronics devices for home loads, mainly electric vehicles, mobile phones, and laptops, introduce harmonics and related power quality issues to the distribution system. This load creates power quality issues like an increase in total harmonic distortion (THD) and a reduction in power factor. This paper uses the DG to supply important power electronics devices for the mitigation of power quality problems in distributed systems with non-linear loads, such as electric vehicles, called the “distributed static compensator (DSTATCOM)”. Wind and photovoltaic (PV) generators supply power to a common DC source. To improve power quality D-STATCOM powered by PV and wind energy systems at the DC link is incorporated. The direct axis/quadrature axis method (DQ method) controls the real and reactive components of the power. Proportional integral (PI) and particle swarm optimization (PSO) based PI controller parameter estimation are developed, and results are compared for power quality performance. In terms of improving power quality, the PSO-based parameter estimate of the PI controller performs better than the traditional PI controller.<br /><div class="extension_reset_css AnnotateNet_widget AnnotateReviewNotesButton" style="display: none; z-index: 2147483644;" data-id="ExtensionWidget"><div class="extension_reset_css AnnotateNet_widget annotateclickable" style="z-index: 8; display: none;" title="Resize" data-id="Widget"> </div></div>

Enhanced Randomized Harris Hawk Optimization of PI controller for power flow control in the microgrid with the PV-wind-battery system

Microgrids, characterized by their ability to work individually or in combination with the main power system, play a pivotal role in addressing the growing demand for reliable and sustainable energy solutions. This work concentrates on the integration of sustainable energy sources, specifically photovoltaic (PV), and wind generation and a battery storage system within a microgrid framework. Additionally, a power flow control strategy is implemented to enhance the dynamic behaviour and stability of the microgrid. The proportional-integral (PI) controller is a fundamental component in regulating the microgrid’s power flow, ensuring optimal performance under varying operating conditions. However, tuning the PI controller parameters is a difficult task because of the dynamic and nonlinear nature of renewable energy sources. In this work, the application of the Enhanced Randomized Harris Hawk Optimization (ERHHO) to fine-tune the PI controller is proposed, using the algorithm’s ability to mimic the hunting behaviour of hawks in finding optimal solutions. The PV-Wind-Battery microgrid system is modelled, and the proposed algorithm is employed to optimize the PI controller parameters for efficient energy management. The ERHHO algorithm’s exploration-exploitation balance is harnessed to navigate the complex solution space and converge to optimal PI controller settings, thereby enhancing the microgrid’s stability and performance. The study evaluates the effectiveness of the proposed ERHHO-based PI controller tuning through comprehensive simulations. Performance metrics such as transient response, overshoot, settling time, and steady-state error are analysed to validate the robustness and efficiency of the proposed method. Compared to its nearest optimization algorithm, with the proposed algorithm rise time is reduced by 50%, overshoot is reduced by 75%, settling time is reduced by 66%, and finally, a percentage of reduction of steady-state error is 45%. The outcomes of this research contribute to the advancement of microgrid control strategies, offering a novel approach to PI controller tuning in the context of diverse renewable energy sources. The integration of the Harris Hawk Optimization algorithm provides a promising avenue for enhancing the operational efficiency and reliability of microgrids, paving the way for sustainable and resilient energy systems in the aspect of growing energy landscapes.

Grey prediction-based proportional-integral controller applied to solar energy systems

Proportional-integral (PI) controller with simple architecture as well as convenient design makes it widely adopted in the control of solar energy systems. Due to partial shading as well as fault influence, the solar panels tend to be trapped at regional extremely values, attenuating the dynamic and steady-state response. For the sake of strengthening the system control effectiveness, this paper employs a grey prediction-based PI controller to attain a global maximization of the wattage pointed following in sun power plate, providing a pure sine waveform with lower percentage of total harmonic distortion, in addition to fast transience of the solar energy system output voltage. The PI performs tracking control and simultaneously the grey prediction uses a logistic grey model to accurately predict the system state as well as to adjust the PI control parameters for establishing robust system response. The results reveal that the output of the solar system can produce fast dynamics and high efficient steady-state performance, thereby supporting the theoretical validity.

An iterative gradient descent-based reinforcement learning policy for active control of structural vibrations

A contemporary policy gradient-based optimization scheme is presented for active structural control by exerting the concept of reinforcement learning (RL). The RL-based control algorithm is demonstrated both in proportional (P) state, and proportional-integral (PI) state-output feedback approaches, wherein, the latter strategy is deliberated based on the theory of servo-mechanism. The search for optimal P and PI controller parameters in a training sequence is attained by engaging an efficient gradient descent-based optimization strategy. The utilization of gradient-based sequence within the RL framework accelerates the learning protocol, ensuring effective dissipation of structural energy, and achieving suboptimal control. The proposed algorithms are validated through numerical experiments on two different structural systems: (i) a quarter car model subjected to periodic road excitation, having a single actuator and presented in continuous time, and (ii) an 8-story building model subjected to random seismic excitation, having multiple actuators and presented in discrete time. Practical implementation concerns of control strategies are thoroughly investigated by considering perturbations in model parameters and input forces. The findings from this study indicate that the RL-based P and PI controllers exhibit high stabilizing performance, and are applicable in both analog and digital domains. Finally, it has affirmed that the proposed RL algorithms exhibit relatively low computational complexity in real-time, and thus, open up a wide range of perspectives for its application in large-scale complex structures.

Performance Analysis of Marine-Predator-Algorithm-Based Optimum PI Controller with Unified Power Flow Controller for Loss Reduction in Wind–Solar Integrated System

Transmission line losses are a crucial and essential issue in stable power system operation. Numerous methodologies and techniques prevail for minimizing losses. Subsequently, Flexible Alternating Current Transmission Systems (FACTSs) efficiently reduce transmission losses, and the Unified Power Flow Controller (UPFC) is a reactive power compensation controller. The parameter strength of the proportional–integral (PI) controller was calibrated with the Marine Predator Algorithm (MPA), a recent metaheuristic algorithm. An MPA-based optimum PI controller with a UPFC evaluates the optimal location of the UPFC and PI controller parameters to accomplish the desired research objective. The power rating of the UPFC was determined depending on the voltage collapse rating and power loss and an evaluated performance analysis of the MPA–PI-controlled UPFC on a modified IEEE-30 bus transmission network in MATLAB Simulink code. The Newton–Raphson method was used to perform the load flow analysis. Hence, the proposed MPA–PI controller was examined in contrast to preferred heuristic algorithms, the Artificial Bee Colony (ABC) and Moth Flame Optimization algorithms (MFO); the results showed that the MPA–PI controller exhibited better performance with an improved voltage profile and surpasses active power losses with the optimal placement of the UPFC device under different loading conditions. The active power loss, considering a UPFC with the proposed algorithm, reduced from 0.0622 p.u to 0.0301 p.u; consequently, the voltage profile was improved in the respective buses, and the loss percentage reduction during a 100% base load was 68.39%, which was comparatively better than the ABC and MFO algorithms.

Adaptive Control Strategy for a Pumping System Using a Variable Frequency Drive

Currently, the most implemented solution for driving a pumping system (PS) at variable speed is using a variable frequency drive (VFD). Because most of the VFDs have integrated only one proportional-integral (PI) frequency controller whose parameters (proportional gain/the integration time) can be off-line tuned but cannot be changed during real-time operation, and many PS must operate in different regimes (at variable speed, variable flow rate, at variable pumping head or even at variable power, e.g., those powered by renewable energy sources), the adaptive control strategy proposed in this paper overcomes these problems with very good performances. The proposed adaptive control strategy uses only simple PI controllers for managing several operating regimes. The adaptive character is not ensured by re-tuning the PI controller parameters as in self-tuning controllers but by readjusting the control law through a change in the control loop depending on the controlled output of the process (pump speed, pump discharge, pumping head or absorbed power). The deviations of the mentioned controlled outputs from their referenced values are converted into electrical frequency error (the deviation of the current value from its reference), which is the regular input to the already tuned controller; therefore, no controller re-tuning is required.

Optimization of PI Controller Parameters by GWO Algorithm for Five-Phase Asynchronous Motor

Operation at low speed and high torque can lead to the generation of strong ripples in the speed, which can deteriorate the system. To reduce the speed oscillations when operating a five-phase asynchronous motor at low speed, in this article, we propose a control method based on Gray Wolf optimization (GWO) algorithms to adjust the parameters of proportional–integral (PI) controllers. Proportional–integral controllers are commonly used in control systems to regulate the speed and current of a motor. The controller parameters, such as the integral gain and proportional gain, can be adjusted to improve the control performance. Specifically, reducing the integral gain can help reduce the oscillations at low speeds. The proportional–integral controller is insensitive to parametric variations; however, when we employ a GWO optimization strategy based on PI controller parameters, and when we choose gains wisely, the system becomes more reliable. The obtained results show that the hybrid control of the five-phase induction motor (IM) offers high performance in the permanent and transient states. In addition, with this proposed strategy controller, disturbances do not affect motor performance.

A simple fault-tolerant control method for open-phase three-phase induction motor drives

Fault-Tolerant Control (FTC) strategies for Three-Phase Induction Motors (TPIMs) against an open-phase fault have attracted considerable interest in safety-critical applications. In the available literature, open-phase FTC methods for TPIMs are typically implemented using suitable Current Transformation (CT) and Voltage Transformation (VT) matrices. However, the controller design becomes complicated due to different transformations. In addition, the model based on CT and VT suffers from slow dynamics due to several Proportional–Integral (PI) controllers. This paper proposes an open-phase FTC technique for TPIM drives with proof of its asymptotic stability. It is designed based on a new CT for stator current components. Using this CT, the traditional vector control method can be used for Open-Phase TPIMs (OPTPIMs). Furthermore, considering that parameters of the PI controller have an important effect on the OPTPIM drive performance, a design technique is presented to achieve the best values of PI controller parameters. Additionally, an open-phase fault detection technique is presented based on the third-difference operator. The proposed FTC has a simple structure and fast dynamic responses. Matlab simulation and experimental results using DSP/TMS320F28335 approve the ability and good performance of the introduced FTC technique for TPIM drive systems under different conditions.

Virtual synchronous generator based superconducting magnetic energy storage unit for load frequency control of micro-grid using African vulture optimization algorithm

An isolated microgrid has significant frequency stability issues due to the erratic nature of renewable energy sources, stochastic load behaviour, and low system inertia. Consequently, this paper presents a Virtual Synchronous Generator (VSG) based Superconducting Magnetic Energy Storage (SMES) unit for frequency stability enhancement of microgrid. A newly developed metaheuristic algorithm named African Vulture Optimization Algorithm (AVOA) is used to optimize the proportional-integral controller parameters with the help of the Integral Time Absolute Error criterion. The proposed Load Frequency Control (LFC) model of an isolated microgrid is evaluated against numerous scenarios and well-known algorithms like Genetic Algorithm, Particle Swarm Optimization, Grey Wolf Optimization, and Harris Hawks Optimization. The MATLAB simulation results of the proposed system demonstrate a significant improvement in the dynamic response pertaining to maximum overshoot, settling duration, and frequency oscillations when contrasted to the other algorithms. Also, observed that the LFC system with VSG-based SMES unit improves the frequency response of 77.37 % and 86.56 % in peak overshoot and settling time respectively as compared to the system without energy storage units. Finally, real-time simulations are carried out in OPAL-RT (OP-4500) to validate the proposed system.

BBO tuned PI controller for the stability of TCP networks

The congestion is the most important issue that effects on the performance of data transition over internet networks. One of the important techniques developed is active queue management (AQM) that prepares an efficient congestion control by reducing losing packets, queue size, and energy consumption. Therefore, AQM technique deemed as a base of many congestion control algorithms schemes. This work suggested the using of proportional integral (PI) controller as an AQM and then use an optimized control system such as biogeography-based optimization (BBO) with PI controller as (BBO-PI). The optimal control (BBO-PI) is characterized by access to design and fine-tuning of defining the shapes of the optimal parameters of PI controller. The BBO algorithm was implemented by using the mathematical system model by M-file/Matlab and Simulink. The simulation results showed the best performance for the transmission control protocol (TCP) network when compared the system with using the PI controller and using optimal control (BBO-PI), the ratio of enhancing the system with using of BBO-PI better than using a PI controller only in terms of rising time is 1.11, settling time is 2.85 and overshooting is 85%. Therefore, the proposed method was very fast and required few iterations.

Fuzzy and PSO tuned PI controller based SAPF for Harmonic Mitigation

The development of a reliable power filter is essential for meeting the need for high-quality power. Current and voltage harmonics are a major contributor to poor power quality and must be eliminated. Shunt active power filters (SAPFs) can be installed to reduce the negative effects of harmonics. Fuzzy logic and proportional integral (PI) controllers excel at regulating DC link voltage in shunt active power filters (SAPFs). This research assessed how well Particle Swarm Optimization controls the DC link voltage in a Shunt Active Power Filter (SAPF), thereby mitigating harmonics. The appropriate PI control parameters are first determined using the Particle Swarm Optimization technique. The PSO-tuned PI parameters are combined with a fuzzy logic controller for the SAPF simulation. It has been demonstrated in simulations that a Shunt Active Power Filter (SAPF) with reference currents are generated in accordance with the instantaneous real and reactive power (p-q) theory, employing fuzzy and PSO -PI controller for diode rectifier type non-linear load. It is extremely effective for harmonic correction in distribution networks. The simulation work is carried out using MATLAB-SIMULINK.

Adaptive PI control of ultrasonic motor using iterative learning methods

PI (proportional integral) controller is widely utilized in ultrasonic motor control systems. However, PI controllers which use fixed parameters is often difficult to ensure that the control performance meets the expectations. Based on the idea of iterative learning control (ILC), a new online adaptive adjustment method for PI control parameters is proposed. Simple P (proportional) type and PD (proportional differential) type iterative learning controllers are designed to adjust the proportional and integral coefficients of PI controller online. Using this method, the coefficients of PI controller are adaptively adjusted with the change of motor characteristics to ensure the control performance meets expectations. This method can improve the nonlinear expression ability of the controller, so that its input–output relationship can better match the characteristics of the controlled object, thereby improving the control performance. Results of comparative experiments indicate the validity of the proposed control strategy.

Dynamic Simulator for Three-Phase Gravity Separators in Oil Production Facilities.

In this study, a dynamic simulator for three-phase gravity separators in oil production facilities is proposed. The mass conservation equation is established to calculate the pressure, water level, and oil level in the separator and the mass balance equation of the dispersed phase to calculate the oil-water separation efficiency. The proportional integral controllers are applied to control the water level, oil level, and pressure in the separator by setting the opening of the three outlet valves of oil, gas, and water. The model is verified using field data by means of the given valve opening and given proportional integral controller parameters, respectively. Subsequently, the verified simulator is applied to study the dynamic behavior of the separator filling process and the effect of pressure, oil level, and water level setpoint changes on the separator operating status. A detailed analysis of the changes in the liquid level, pressure, and opening of three outlet valves is presented. Then, the effects of operating conditions such as the inlet flow, water setpoint, and weir height on the separation efficiency are discussed. This simulator can be applied for the design of oil, gas, and water three-phase separation processes. In addition, through this simulator, the parameters that are difficult to be measured by instruments during the operation of the separator can be calculated, providing technical support for the construction of the digital twin of the separator.

Performance Enhancement of Doubly-Fed Induction Generator-Based-Wind Energy System

Nowadays, the challenging errand is enhancing the wind energy system (WES) performance to be more competitive and economically viable. Most researchers believe that one of the best ways to enhance the performance of the doubly-fed induction generator (DFIG)-based-WES is the optimization of the proportional-integral (PI) controllers for the variable frequency converter system. Many objectives with different optimization techniques have been used in literature to achieve optimal performance. Each choice has its advantages and disadvantages; thus, the research area is still open for getting optimal performance. This paper presents a new design approach for better performance of PI controllers and, hence DFIG over a wide range of operating conditions through two main themes. The first is by introducing a new multi-objective formulation, while the second is utilizing recent optimization techniques like Grey Wolf Optimizer and Whale Optimization Algorithm. The proposed approach is applied to get the best parameters of the related PI controllers for rotor and grid side converters of the simulation model of a 6 MW wind farm located in Jabal Alzayt along the Red Sea Coast in Egypt and directly connected to the grid. The results confirmed the effectiveness of the proposed approach to help the DFIG-based-WES to agree with the Egyptian Grid Code during disturbances compared with the traditional objective formulation.

Harmonics Mitigation Using MMC Based UPFC and Particle Swarm Optimization

The application of non-linear loads in the power electronic device causes serious harmonic issues in the power system since it has the intrinsic property of retrieving harmonic current and reactive power from Alternating Current (AC) supply that leads to voltage instability. To maintain a reliable power flow in the power system, an innovative Unified Power Flow Converter (UPFC) is utilized in this proposed approach. The conventional series converter is replaced with the Modular Multilevel Converter (MMC) that improves the power handling capability and achieves higher modular level with minimized distortions. The shunt compensator assists in minimizing the voltage fluctuations and maximizing the voltage stability under different load constraints. The Direct Quadrature (DQ) theory is utilized in this study to separate the harmonic components by generating reference frame current. The Proportional Integral (PI) controller aids in maintaining the direct current potential difference in the constant mode whereas the Pulse Width Modulation (PWM) generator helps in delivering optimized output to the MMC. The gain parameters of PI controller are optimized with the aid of employing Particle Swarm Optimization (PSO). The entire work is validated using MATLAB simulink and the obtained outcomes proves that this new UPFC is highly beneficial in minimizing the distortions with reduced Total Harmonic Distortion (THD) of 2.21%.

Improved Interleaved Single-Ended Primary Inductor-Converter for Single-Phase Grid-Connected System

The generation of electricity based on renewable energy sources, particularly Photovoltaic (PV) system has been greatly increased and it is simply instigated for both domestic and commercial uses. The power generated from the PV system is erratic and hence there is a need for an efficient converter to perform the extraction of maximum power. An improved interleaved Single-ended Primary Inductor-Converter (SEPIC) converter is employed in proposed work to extricate most of power from renewable source. This proposed converter minimizes ripples, reduces electromagnetic interference due to filter elements and the continuous input current improves the power output of PV panel. A Crow Search Algorithm (CSA) based Proportional Integral (PI) controller is utilized for controlling the converter switches effectively by optimizing the parameters of PI controller. The optimized PI controller reduces ripples present in Direct Current (DC) voltage, maintains constant voltage at proposed converter output and reduces overshoots with minimum settling and rise time. This voltage is given to single phase grid via Voltage Source Inverter (VSI). The command pulses of VSI are produced by simple PI controller. The response of the proposed converter is thus improved with less input current. After implementing CSA based PI the efficiency of proposed converter obtained is and the Total Harmonic Distortion (THD) is found to be . The dynamics and closed loop operation is designed and modeled using MATLAB Simulink tool and its behavior is performed.

MSP designing with optimal fractional PI–PD controller for IPTD processes

Abstract An effective tuning methodology of modified Smith predictor (MSP) based fractional controller designing for purely integrating time delayed (IPTD) processes is reported here. IPTD processes with pole at the origin are truly difficult to control; exhibit large oscillations once get disturbed from their steady state. Proposed MSP design consists of fractional PI (proportional-integral) and fractional PD (proportional-derivative) controllers together with P (proportional) controller. Fractional controllers are competent to provide improved closed loop responses due to flexibility of additional tuning parameters. Fractional tuning parameters of PI and PD controllers are derived through optimization algorithms where integral absolute error (IAE) is considered as cost function. Efficacy of the proposed methodology is validated for IPTD processes having wide range of time delay. Stability and robustness issues are explored under process model uncertainties with small gain theorem. Performance of the proposed MSP-FO(PI–PD) controller is validated through simulation study relating five IPTD process models. Overall satisfactory closed loop responses are observed for each case during transient as well as steady state operational phases.