Hybrid Optimized PI Controller Design for Grid Tied PV Based Electric Vehicle

Nowadays,researchers are becoming increasingly concerned about developing a highly efficient emission free transportation and energy generation system for addressing the pressing issue of environmental crisis in the form of pollution and climate change.The introduction of Electric Vehicles(EVs)solves the challenge of emission-free transportation while the necessity for decarbonized energy production is fulfilled by the installation and expansion of solar-powered Photovoltaic(PV)systems.Hence,this paper focuses on designing an effective PV based EV charging system that aids in stepping towards the achievement of a pollution free future.For overcoming the inherent intermittency associated with PV,a novel DC-DC converter is designed by integrating both Trans Z-source con-verter and Luo converter,which offers remarkable benefits of high conversion range,lesser voltage stress and excellent efficiency.A novel robust Lion Grey Wolf Optimized Proportional Integral(LGWO-PI)controller is designed for sig-nificantly strengthening the operation of the integrated converter in terms of peak overshoot,Total Harmonic Distortion(THD)and settling time.A 3’Voltage Source Inverter(VSI)is employed to convert the stable DC output from the PV sys-tem to AC,which is then used for driving the Brushless Direct Current Motor(BLDC)motor of EV.The speed of the BLDC is regulated using a PI controller.The BLDC motor gets the power supply from the grid during the unavailability of PV based power supply.The grid is integrated with the designed EV charging system through a 1’VSI and the process of grid voltage synchronization is carried out with the application of PI controller.The simulation for evaluating the operation of the presented EV charging system is done using MATLAB and the attained out-comes have validated that this introduced methodology delivers enhanced perfor-mance with optimal efficiency of 97.6%and lesser THD of 2.1%.

DC–DC Converter with Pi Controller for BLDC Motor Fuzzy Drive System

The Brushless DC Motor drive systems are used widely with renewable energy resources.The power converter controlling technique increases the performance by novel techniques and algorithms.Conventional approaches are mostly focused on buck converter,Fuzzy logic control with various switching activity.In this proposed research work,the QPSO(Quantum Particle Swarm Optimization algorithm)is used on the switching state of converter from the generation unit of solar module.Through the duty cycle pulse from optimization function,the MOSFET(Metal-Oxide-Semiconductor Field-Effect Transistor)of the Boost converter gets switched when BLDC(Brushless Direct Current Motor)motor drive system requires power.Voltage Source three phase inverter and Boost converter is controlled by proportional-integral(PI)controller.Based on the BLDC drive,the load utilized from the solar generating module.Experimental results analyzed every module of the proposed grid system,which are solar generation utilizes the irradiance and temperature depends on this the Photovoltaics(PV)power is generated and the QPSO with Duty cycle switching state is determined.The Boost converter module is boost stage based on generation and load is obtained.Single Ended Primary Inductor Converter(SEPIC)and Zeta converter model is compared with the proposed logic;the proposed boost converter achieves the results.Three phase inverter control,PI,and BLDC motor drive results.Thus the proposed grid model is constructed to obtain the better performance results than most recent literatures.Overall design model is done by using MATLAB/Simulink 2020a.

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit.Renewable energy sources are playing a significant role in the modern energy system with rapid development.In renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental changes.To develop energy resources,electric power generation involved huge awareness.The power and output voltages are plays important role in our work but it not considered in the existing system.For considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy system.The input information is collected from two input sources.After that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC Model.Hidden layer 1 is transferred to hidden layer 2.Gaussian activation is employed for determining the output voltage with help of the controller.At last,the output layer offers the last value in GPIC-MDCNNC Model.The designed method was confirmed using one and multiple sources by stable and unpredictable input voltages.GPIC-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art works.The control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

Performance of the Boost Chopper, Comparative Study between PI Control and Neural Control to Regulate Its Output Voltage

In this study, we investigate the performance of a boost converter regulating its output voltage using two control methods: Proportional-Integral (PI) control and neural control. Both methods are implemented on a simulation platform (Matlab/Simulink) and evaluated in terms of accuracy, response speed, and robustness to disturbances. Indeed, the output voltage of converters exhibits imperfections that require a control method to optimize efficiency when applying a variable load. Results show that neural control offers superior performance in terms of accuracy and response time, with faster and more precise regulation of the output voltage. On the other hand, PI control proves to be more robust against disturbances. These findings can help guide the selection of the appropriate control method for a boost converter based on the specific requirements of each application.

Genetic algorithm tuned PI controller on PMSM simplified vector control

A simple control structure in servo system is occasionally needed for simple industrial application which precise and high control performance is not exessively important so that the cost production can be reduced efficiently. Simplified vector control, which has simple control structure, is utilized as the permanent magnet synchronous motor control algorithm and genetic algorithm is used to tune three PI controllers used in simplified vector control. The control performance is obtained from simulation and investigated to verify the feasibility of the algorithm to be applied in the real application. Simulation results show that the speed and torque responses of the system in both continuous time and discrete time can achieve good performances. Furthermore, simplified vector control combined with genetic algorithm has a similar perfofmance with conventional field oriented control algorithm and possible to be realized into the real simple application in the future.

Neuron PI control for semi-active suspension system of tracked vehicle

A neuron proportion integration (PI) control strategy for semi-active suspension system of tracked vehicle was proposed based on its unique structure and the multiple and complex environment of the driving traffic. An adaptive genetic algorithm is used to optimize the parameters of the neuron PI controller. The simulation result of the neuron PI control for semi-active suspension system of tracked vehicle indicates that the vertical amplitude,pitch angle and vertical acceleration of the vehicle are well controlled. The root mean square (RMS) of the vertical amplitude decreases by 37.2%,and 45.2% for the pitch angle,38.6% for the vertical acceleration. The research of neuron PI control experiment for the semi-active suspension system of the tracked vehicle model mining in benthal indicates that the RMS of the weight acceleration vibrating along the vertical direction decreases by 29.5%,the power spectral density resonance peak of the acceleration of the car body decreases by 23.8%.

Optimized PI controller for 7-level inverter to aid grid interactive RES controller

With the huge rise of energy demand,the power system in the current era is moving to a new standard with increased access to renewable energy sources(RESs)integrated with distribution generation(DG)network.The RESs necessitate interfaces for controlling the power generation.The multilevel inverter(MLI)can be exploited for RESs in two diverse modes,namely,the power generation mode(stand-alone mode),and compensator mode(statcom).Few works have been carried out in optimization of controller gains with the load variations of the single type such as reactive load variation in different cases.Nevertheless,this load type may be unbalanced hence,to overcome such issues.So,a sophisticated optimization algorithm is important.This paper aims to introduce a control design via an optimization assisted PI controller for a 7-level inverter.In the present technique,the gains of the PI controller are adjusted dynamically by the adopted hybrid scheme,grey optimizer with dragon levy update(GD-LU),based on the operating conditions of the system.Here,the gains are adjusted such that the error between the reference signal and fault signal should be minimal.Thus,better dynamic performance could be attained by the present optimized PI controller.The proposed algorithm is the combined version of grey wolf optimization(GWO)and dragonfly algorithm(DA).Finally,the performance of the proposed work is compared and validated over other state-of-the-art models concerning error measures.

Synthesis of Fractional-order PI Controllers and Fractional-order Filters for Industrial Electrical Drives

This paper introduces an electrical drives control architecture combining a fractional-order controller and a setpoint pre-filter. The former is based on a fractional-order proportional-integral(PI) unit, with a non-integer order integral action, while the latter can be of integer or non-integer type. To satisfy robustness and dynamic performance specifications, the feedback controller is designed by a loop-shaping technique in the frequency domain. In particular, optimality of the feedback system is pursued to achieve input-output tracking. The setpoint pre-filter is designed by a dynamic inversion technique minimizing the difference between the ideal synthesized command signal(i.e., a smooth monotonic response) and the prefilter step response. Experimental tests validate the methodology and compare the performance of the proposed architecture with well-established control schemes that employ the classical PIbased symmetrical optimum method with a smoothing pre-filter.

PI control based on fuzzy set-point weighting tracking for hydraulic crane boom system

A PI control strategy based on fuzzy set-point weighting following was proposed for the active damping control of a hydraulic crane boom system （HCBS）. Two valve-controlled PI controllers, which include a proportional feedforward controller based on fuzzy set-point weighting following and a limited semi-integrator（LSI）, are designed respectively. LSI is used to limit output signal and to prevent wind up at the low frequency of the spectrum. By using a range camera and an electronic feedback control, the tip damping on the HCBS can be adjusted artificially. A collaborative control simulation technique of HOPSAN and MATLAB/SIMULINK is applied to the controller design. Simulation results show that the proposed PI control system has less overshoot as well as faster response. The tip damping on the HCBS during operation is improved.

BELBIC Tuned PI Controller Based Chopper Driven PMDC Motor

In orthopaedic surgeries, permanent magnet DC motors are used to drill the bone and fix the screws. The Motor drive employs an inner current and outer speed control loop with a conventional or modern controller. To enhance the performance of the drive, this paper proposes a Brain Emotional Logic Based Intelligent Controller based chopper drive. The proposed drive scheme has been simulated using Matlab/Simulink and physically realized for validation. A comparative analysis has been made between the conventional PI controller based drive and the proposed system in order to prove that the proposed scheme has an edge over the traditional PI controller scheme counterpart.

Design of Soft Computing Based Optimal PI Controller for Greenhouse System

Greenhouse system (GHS) is the worldwide fastest growing phenomenon in agricultural sector. Greenhouse models are essential for improving control efficiencies. The Relative Gain Analysis (RGA) reveals that the GHS control is complex due to 1) high nonlinear interactions between the biological subsystem and the physical subsystem and 2) strong coupling between the process variables such as temperature and humidity. In this paper, a decoupled linear cooling model has been developed using a feedback-feed forward linearization technique. Further, based on the model developed Internal Model Control (IMC) based Proportional Integrator (PI) controller parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to achieve minimum Integral Square Error (ISE). The closed loop control is carried out using the above control schemes for set-point change and disturbance rejection. Finally, closed loop servo and servo-regulatory responses of GHS are compared quantitatively as well as qualitatively. The results implicate that IMC based PI controller using PSO provides better performance than the IMC based PI controller using GA. Also, it is observed that the disturbance introduced in one loop will not affect the other loop due to feedback-feed forward linearization and decoupling. Such a control scheme used for GHS would result in better yield in production of crops such as tomato, lettuce and broccoli.

Comparative Study of Different Decoupling Schemes for TITO Binary Distillation Column via PI Controller

This paper presents a comparative study of different decoupling control schemes for a two-input, two-output(TITO) binary distillation column via proportional-integral(PI)controller. The key idea behind this paper is designing two novel fuzzy decoupling schemes that depend on human knowledge,instead of the system mathematical model used in conventional decoupling schemes. Based on conventional and inverted decoupling schemes, fuzzy and inverted fuzzy decoupling schemes are developed. The control effect is compared using simulation results for the proposed two schemes with conventional decoupling and inverted decoupling. The proposed fuzzy decoupling schemes are easy to realize and simple to design, besides they have a good decoupling capability. Two methods are used to prove asymptotic stability of each loop and the entire closed-loop system by applying the proposed fuzzy decoupling-based PI controller.The Wood and Berry model of a binary distillation column is used to illustrate the applicability of the proposed schemes.

Fuzzy and PI Controller Based SHAF for Mitigation of Current Harmonics with P-Q Method Using Matlab and RTDS Hardware

The main objective of this paper is to develop PI and Fuzzy logic controllers to analyse the performance of instantaneous real active and reactive power (p-q) control strategy for extracting reference currents of shunt active filters under balanced, un-balanced and balanced non-sinusoidal conditions. When the supply voltages are balanced and sinusoidal, then all controllers converge to the same compensation characteristics. However, when the supply voltages are distorted and/or un-balanced sinusoidal, these control strategies result in different degrees of compensation in harmonics. The p-q control strategy with PI controller is unable to yield an adequate solution when source voltages are not ideal. Extensive simulations were carried out;simulations were performed with balance, unbalanced and non sinusoidal conditions. Simulation results validate the dynamic behaviour of Fuzzy logic controller over PI controller. The 3-ph 4-wire SHAF system is also implemented on a Real Time Digital Simulator (RTDS Hardware) to further verify its effectiveness. The detailed simulation and RTDS Hardware results are included.

Delta Modulation with PI Controller—A Comparative Study

The paper discusses the application of PWM delta modulation with PI controller as an alternative to the standard PWM techniques for providing gating signals to the voltage source inverters. Its inherent characteristics of constant volts/Hertz control without feedback complexity and boosting of the fundamental voltage makes it an excellent choice for motor drive applications. This paper discusses the comparison between a basic delta modulation, the advanced Delta modulation with PI controller and an optimized DMPI. It is shown that in addition to the aforementioned advantages, VSI with optimized DMPI produces superior load current/voltage waveforms compared to simple DMPI, when IGBT is employed as a switching device for the inverter. Since the tacho feedback or other traditional speed sensing means is not permissible in sealed motor or the pump, a novel method is used to monitor the motor speed from the terminal quantities like voltage, current, and motor input power factor.

Age-of-information-aware PI controller for load frequency control

Open communication system in modern power systems brings concern about information staleness which may cause power system frequency instability.The information staleness is often characterized by communication delay.However,communication delay is a packet-centered metric and cannot reflect the requirement of information freshness for load frequency control(LFC).This paper introduces the age of information(AoI),which is more compre-hensive and informative than the conventional communication delay modeling method.An LFC controller and com-munication are integrated into the design for LFC performance improvement.An AoI-aware LFC model is formulated first,and considering each allowable update period of the smart sensor,different AoI-aware PI controllers are then designed according to the exponential decay rate.The right AoI-aware controller and update period are selected according to the degree of frequency fluctuation of the power system.Case studies are carried out on one-area and two-area power systems.The results show the superior performance of the AoI-aware controllers in comparison to the delay-dependent controllers.

基于PI参数的二阶线性自抗扰控制参数整定

线性自抗扰控制是一种不依赖于被控过程模型的控制方法,具有很好的工程应用潜力。目前,工业控制过程中大多采用PI控制器,为了确保由调试好的PI控制器平稳切换到线性自抗扰控制器,并使系统仍保持稳定状态,需要合理设置线性自抗扰控制器的参数。为此,在分析二阶线性自抗扰控制器的二自由度等效结构的基础上,推导出其反馈控制器与PID控制器的对应关系,给出一种基于现有PI控制参数直接获取二阶线性自抗扰控制初始参数的方法。最后,在MATLAB/Simulink平台上采用若干典型传递函数和双容水箱液位控制系统进行仿真研究,仿真结果验证了所提方法的有效性。

Consensus Analysis of Fractional Multi-Agent Systems with Delayed Distributed PI Controller

The consensus problem for fractional multi-agent systems(MASs)with time delay is considered.The distributed fractional proportional-integral(PI)-type controller is designed so that the consensus of the proposed systems is achieved.Moreover,explicit condition to determine the crossing directions is developed.The results show that with the increase of time delay,the closed-loop system has two different dynamic characteristics:From consensus to nonconsensus and consensus switching.Furthermore,delay margin within which consensus of MASs will always hold is determined.The results should provide useful guidelines in the consensus analysis and in the analytical design of the distributed controllers.

DAB储能变换器双闭环模型预测控制与PI补偿控制策略研究

针对直流微电网中分布式电源输入输出功率不稳定造成的直流母线电压波动问题,鉴于双有源桥(DAB)储能变换器具有能量双向流动的特点,提出一种基于内模原理的双闭环模型预测控制策略。在分析DAB变换器扩展移相调制原理与特性基础上,重点研究电压外环和电流内环的模型预测控制策略。针对模型预测控制性能依赖电路参数准确度问题,分别从电路主要参数失配对功率传输和直流母线稳压的影响两个角度出发,深入分析模型参数敏感度。最终设置直流母线电压稳态误差灵敏度区,在误差灵敏度区外仅采用双闭环模型预测控制以保证系统的动态响应性能,在误差灵敏度区内采用双闭环模型预测控制和PI补偿控制以消除系统稳态误差。搭建实验平台进行实验验证,结果表明:在系统受到扰动时,DAB储能变换器可实现快速地充放电对系统进行补偿,混合控制策略可快速保持母线电压的稳定,实现系统功率均衡,增强系统抗扰能力。

模糊整定PI参数的双柔性机械臂振动抑制

机械臂伺服驱动系统中柔性因素的存在易导致机械臂输出转速波动,甚至引发系统谐振.为抑制系统转速波动,使机械臂传动系统性能稳定,使用模糊理论整定PI控制器参数的控制策略.根据假设模态法和拉格朗日动力学方程建立了考虑LuGre摩擦模型的双柔性机械臂传动系统动力学方程,并分析了动力学方程中耦合非线性项对系统传动特性的影响.使用极点配置策略确定PI控制器参数的取值范围,后根据模糊规则实时调整控制器参数,以减小伺服系统输出转速的波动,进而抑制系统谐振.最后,借助数值仿真分析和机械臂控制实验,与传统PI控制策略对比,发现本文所述控制策略可使电机端转角跟踪误差绝对值的平均值降低43.066%,柔性负载转角误差的标准差降低46.506%,更加验证了所提模糊规则整定PI控制器参数抑振方法的有效性.

基于PI建模和反步滑模控制的主动波浪补偿策略

海上起重船受风、浪、涌影响会产生剧烈的船舶姿态变化,造成起重机和货物的位姿变化,对货物和人员存在安全隐患,波浪补偿平台的稳定性控制能有效减少复杂海况下船舶运动对海上作业安全性、稳定性和精准性的影响,对浮式起重船海上设备精准装载作业极其重要。针对补偿平台的迟滞非线性导致的建模困难和控制不精确问题,本文提出基于PI(Prandtle–Ishlinskii)建模和反步滑模控制的主动波浪补偿策略。首先,通过实验得到补偿系统的迟滞效应曲线,分析系统迟滞环建立PI迟滞模型,并采用递推最小二乘法辨识模型的各个参数,从而求得系统模型。然后,基于李雅普诺夫(Lyapunov)稳定性设计反步控制补偿方法,并结合滑模控制规律加快初始控制速度。最后,将反步滑模法应用于补偿系统,采用MATLAB软件仿真在规则波和不规则波下的响应来验证算法和模型的正确性,并在工控机中用C#编写控制程序,驱动运动控制卡控制伺服电机带动电缸进行补偿运动,同时通过传感器采集系统运动的实时数据,并反馈给工控机形成闭环,以期验证补偿平台在补偿规则波和不规则波下的补偿效果。实验结果表明,所建立的斯图尔特(Stewart)浮式平台中,PI迟滞模型具有良好的精度,反步终端滑模控制算法在Stewart平台的实际控制中能够很好地补偿波浪运动,相比比例–积分–微分控制(PID)、反步法、强化学习等控制方法,反步终端滑模方法能快速较好跟踪期望位移,补偿精度达到0.9729。