Optimal PI Controller Research Articles

Optimal disturbance rejection for PI controller with constraints on relative delay margin

Performance optimization with robustness constraints is frequently encountered in process control. Motivated by the analytical difficulties in dealing with the conventional robustness index, e.g., maximum sensitivity, we introduce the relative delay margin as a good alternative, which gives much simpler robust analysis. This point is illustrated by designing an optimal PI controller for the first-order-plus-dead-time (FOPDT) model. It is first shown that the PI controller parameters can be analytically derived in terms of a new pair of parameters, i.e., the phase margin and gain crossover frequency. The stability region of PI controller is subsequently obtained with a much simpler procedure than the existing approaches. It is further shown that a certain relative delay margin can represent the robustness level well and the contour can be sketched with a simpler procedure than the one using maximum sensitivity index. With constraints on the relative delay margin, an optimal disturbance rejection problem is then formulated and analytically solved. Simulation results show that the performance of the proposed methodology is better than that of other PI tuning rules. In this paper, the relative delay margin is shown as a promising robustness measure to the analysis and design of other advanced controllers.

An Optimized PI Controller Design for Three Phase PFC Converters Based on Multi-Objective Chaotic Particle Swarm Optimization

The compound active clamp zero voltage soft switching (CACZVS) three-phase power factor correction (PFC) converter has many advantages, such as high efficiency, high power factor, bi-directional energy flow, and soft switching of all the switches. Triple closed-loop PI controllers are used for the three-phase power factor correction converter. The control objectives of the converter include a fast transient response, high accuracy, and unity power factor. There are six parameters of the controllers that need to be tuned in order to obtain multi-objective optimization. However, six of the parameters are mutually dependent for the objectives. This is beyond the scope of the traditional experience based PI parameters tuning method. In this paper, an improved chaotic particle swarm optimization (CPSO) method has been proposed to optimize the controller parameters. In the proposed method, multi-dimensional chaotic sequences generated by spatiotemporal chaos map are used as initial particles to get a better initial distribution and to avoid local minimums. Pareto optimal solutions are also used to avoid the weight selection difficulty of the multi-objectives. Simulation and experiment results show the effectiveness and superiority of the proposed method.

Direct Torque Control for Permanent Magnet Synchronous Motor Based on NARMA-L2 controller

This paper investigates the improvement of the speed and torque dynamic responses of three phase Permanent Magnet Synchronous Motor (PMSM) using Direct Torque Control (DTC) technique. Different torques are applied to PMSM at different speeds during operation to ensure the robustness of the controller for wide torque variations. Optimal PI controller is used to modify the response of DTC. The optimal gains of PI controller are tuned by Particle Swarm Optimization (PSO) technique. Neural Network controller is called the Nonlinear Autoregressive-Moving Average (NARMA-L2) which is trained based on optimal PI controller (PI-PSO) data. The results show the superiority performance of using NARMA-L2 controller on PI-PSO controller for different speeds and load change. The overall simulation and design of the scheme are implemented Using MATLAB/Simulink program.

Advanced Optimal PSO, Fuzzy and PI Controller with PMSM and WTGS at 5Hz Side of Generation and 50Hz Side of Grid

To use different control systems, Like Classical PI Controller, Expert System, Fuzzy Logic Controller and Optimization PSO Controller. It used to control for PMSM which worked in integration system to Wind Energy. Wind Energy content of wind turbine, PMSM, Rectifier, DC bus, Inverter, Filter, Load and Grid. In the first step, to run the PMSM with different speeds to get different frequency to selected the frequency in side of generation with the rated speed. Second step, solve the mathematical equation to use different values of wind speed with selected (15,20 m/s and less than with more than 15&20m/s). Third step, Calculation the power generation with wind speed (15 m/5 & 20 m/s). Fourth step, using these component system Rectifier, DC bus, Inverter, Filter, Load & Grid with WTGS & PMSM. Final step, use different control systems, Like Classical PI Controller, Expert System Fuzzy Logic Controller and Optimization PSO Controller With PMSM to analysis all results after using the simulation model of proposed variable speed based WECS. The wind turbine coupled with PMSM.A closed loop control system with a PI control,Fuzzy, PSO in the speed loop with current controllers .The simulation circuits for PMSM, inverter, speed and current controllers include all realistic components of the drive system. These results also confirmed that the transient torque and current never exceed the maximum permissible value.

Pitch control for wind turbine systems using optimization, estimation and compensation

Delays caused by hydraulic pressure driven units in wind turbines may degrade and even destabilize pitch control system, leading to the performance degradation or collapse of the whole wind turbine energy conversion system. As a result, there is a strong motivation to improve pitch control technique to overcome the adverse effects from the unknown delays caused by the hydraulic driven units. In this study, a novel pitch control approach is developed by integrating optimization, delay-perturbation estimation, and signal compensation techniques. Specifically, an optimal PI controller is designed by applying direct search optimization to ideal delay-free pitch model. A delay estimator is next designed to estimate the perturbation caused by the delay. The signal compensation technique is then implemented to remove the effect from the delay-perturbation to the turbine output so that the actual output is consistent with the optimal output of the ideal delay-free model. Furthermore, the technique is extended to PID-based pitch control systems. The blade-tower dynamics models of three wind turbines, respectively with rated powers 1.5 MW, 275 kW and 50 kW, are investigated and the effectiveness of the proposed techniques are demonstrated by detailed simulation studies. It is worthy to point out a priori on the delay is not necessary in this design, which much meets the situations in practical wind turbine systems. Finally, a 4.8 MW benchmark wind turbine energy conversion system is investigated via the proposed pitch control technique, which has shown that delay perturbation estimate based compensation can effectively improve the pitch control performance in the real-time wind turbine energy conversion system.

Multi objective load frequency control using hybrid bacterial foraging and particle swarm optimized PI controller

Excessive load demand with reliability in power availability, demands for interconnection of large number of generating units over existing tie lines. Due to sudden change in demand, the power transfer over existing tie lines working close to their thermal limits results in low frequency power oscillations. Thus, in modern power systems the study of mitigation of these frequency oscillations is more involved and formulates the area of Load Frequency Control (LFC). Many conventional and heuristic control techniques have been recently applied to address the issue of LFC. This paper investigates load frequency control of large interconnected power system consisting of conventional and renewable energy sources, using hybrid heuristic approach. The proposed strategy is shown to result in improved system damping resulting in faster mitigation of low frequency oscillations.

Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems

This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices.

Optimal Voltage And Frequency Control Of A Microgrid System Using A Teaching-Learning Based Optimization (TLBO) Algorithm

The stability is an important topic to plan and manage the energy in the microgrids as the same as the conventional power systems. The voltage and frequency stability is one of the most important issues recently studied in microgrids. The objectives of this paper are the modelling and designing of the components and optimal controllers for the voltage and frequency control of the AC/DC hybrid microgrid under the different disturbances. Since the PI controllers have the advantages of simple structure and easy implementation, so they were designed and modeled in this paper. The teaching- learning based optimization (TLBO) algorithm is used to optimize the controllers' parameters. According to the achieved results, the optimal PI controllers have a good performance in voltage and frequency control of the microgrid.

ICA-based speed control of induction motor fed by wind turbine

This paper proposes the speed control of induction motor fed by wind turbine using Imperialist Competitive Algorithm (ICA). The wind turbine plays as a prime mover to a connected DC generator. Pulse-width modulation is used to get three-phase AC voltage from the output of DC generator. The proposed design problem of speed controller is established as an optimization problem. ICA is adopted to search for optimal controller parameters by minimizing the time domain objective function. The behavior of the proposed ICA has been estimated with the behavior of the conventional Ziegler---Nichols and genetic algorithm in order to prove the superiority of the proposed ICA in tuning PI controller. Also, the behavior of the proposed controller has been tested over a wide range of operating conditions. Simulation results confirm on the better behavior of the optimized PI controller based on ICA compared with other algorithms.

Basic Compensation Principle and Reference Current Generation

Reactive power compensation is an important aspect in the control of distribution systems. Reactive current in addition to increasing the distribution system losses, introduces various power quality problems like, harmonics, voltage sag, swell and poor load power factor. These power quality issues result in the malfunction of sensitive equipments. A Distribution Static Compensator (DSTATCOM) proves to be a viable solution for the mitigation of such power quality issues. The aim of this paper is to compare an optimized PI and a Fuzzy controlled DSTATCOM for reactive power compensation and harmonic mitigation. Here the PI controllers are first optimized by using error minimization criteria through Genetic algorithms and then replaced by a Fuzzy controller. Through various simulations it is concluded that the transient response of fuzzy controllers is better than the optimized PI controllers

The Enhanced – DQF Algorithm and Optimal Controller Design for Shunt Active Power Filter

This paper proposes the performance enhancement of harmonic detection using a dq–frame with Fourier algorithm (e–DQF) for a shunt active power filter (SAPF) in ideal voltage conditions. The reference current calculation for all harmonic orders and some harmonic orders mitigation using the e–DQF algorithm is described. Moreover, the optimal PI controller design of the compensating current control is also presented. The artificial intelligence (AI) technique called adaptive tabu search (ATS) is applied to design this controller. The performance comparison of the PI controller designed by both conventional and AI approaches are fully presented. The hardware in the loop (HIL) simulation based on eZdspTM F28335 board is used to simulate the harmonic elimination with SAPF in the system. The results show that the controller designed by the AI approach can provide the minimum total harmonic distortion of source currents (%THDi,av) after compensation compared with the conventional controller design.

Model Predictive Control of Two-Area Load Frequency Control Based Imperialist Competitive Algorithm

Imperialist Competitive Algorithm (ICA) has recently been explored to develop a novel algorithm for distributed optimization and control. This paper proposes a Model Predictive Control (MPC) of Load Frequency Control (LFC) based ICA to enhance the damping of oscillations in a two-area power system. A two-area non-reheat thermal system is considered to be equipped with Model Predictive Control (MPC). ICA is utilized to search for optimal controller parameters by minimizing a time-domain based objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller, and PI controller tuned by ICA in order to demonstrate the superior efficiency of the proposed MPC tuned by ICA. Simulation results emphasis on the better performance of the optimized MPC based on ICA in compare to optimized PI controller based on ICA and conventional one over wide range of operating conditions, and system parameters variations.

Model Predictive Control of Two-Area Load Frequency Control Based Imperialist Competitive Algorithm

Imperialist Competitive Algorithm (ICA) has recently been explored to develop a novel algorithm for distributed optimization and control. This paper proposes a Model Predictive Control (MPC) of Load Frequency Control (LFC) based ICA to enhance the damping of oscillations in a two-area power system. A two-area non-reheat thermal system is considered to be equipped with Model Predictive Control (MPC). ICA is utilized to search for optimal controller parameters by minimizing a time-domain based objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller, and PI controller tuned by ICA in order to demonstrate the superior efficiency of the proposed MPC tuned by ICA. Simulation results emphasis on the better performance of the optimized MPC based on ICA in compare to optimized PI controller based on ICA and conventional one over wide range of operating conditions, and system parameters variations.

VSC-HVDC system optimized PI controllers using bacterial foraging algorithm

The strategy of control for high-voltage direct current system on voltage-source converter VSC-HVDC is based on an important element which is PI controller. Because of its simple structure and strong robustness in a wide range of serving conditions, it has been used in the control system in the last few years. Therefore, it is important for the VSC-HVDC system permanent regime to choose proper PI parameters. The use of conventional techniques to find suitable PI parameters creates a number of challenges for the system operators, because it is a difficult process and time consuming. In this paper, a new swarm intelligence algorithm called bacterial foraging optimization is introduced to find the optimal parameters of the PI controller. This approach offers great flexibility for the permanent regime recovery and improves the stability of the VSC-HVDC link compared to conventional techniques. The obtained results are presented to show the effectiveness of the proposed swarm implementation for optimal controller design for VSC-HVDC transmission. MATLAB/Similink simulations are provided to demonstrate the performance of the proposed approach.

Active disturbance rejection control of a rotary flexible joint employing an optimal PI controller

Active disturbance rejection control of a rotary flexible joint employing an optimal PI controller

Adaptive GSA-based optimal tuning of PI controlled servo systems with reduced process parametric sensitivity, robust stability and controller robustness.

This paper suggests a new generation of optimal PI controllers for a class of servo systems characterized by saturation and dead zone static nonlinearities and second-order models with an integral component. The objective functions are expressed as the integral of time multiplied by absolute error plus the weighted sum of the integrals of output sensitivity functions of the state sensitivity models with respect to two process parametric variations. The PI controller tuning conditions applied to a simplified linear process model involve a single design parameter specific to the extended symmetrical optimum (ESO) method which offers the desired tradeoff to several control system performance indices. An original back-calculation and tracking anti-windup scheme is proposed in order to prevent the integrator wind-up and to compensate for the dead zone nonlinearity of the process. The minimization of the objective functions is carried out in the framework of optimization problems with inequality constraints which guarantee the robust stability with respect to the process parametric variations and the controller robustness. An adaptive gravitational search algorithm (GSA) solves the optimization problems focused on the optimal tuning of the design parameter specific to the ESO method and of the anti-windup tracking gain. A tuning method for PI controllers is proposed as an efficient approach to the design of resilient control systems. The tuning method and the PI controllers are experimentally validated by the adaptive GSA-based tuning of PI controllers for the angular position control of a laboratory servo system.

Active Disturbance Rejection Control of Shell Gasifier in IGCC Power Plants

A unique control strategy based on Active Disturbance Rejection Control (ADRC) is introduced to the Shell gasifier control system. Firstly, the gasifier control system structure is illustrated based on the analysis of gasifier process dynamics and its control targets. Then a decentralized ADRC-based gasifier control system is designed and the controller parameter tuning procedure is given. The interaction of control loops, the uncertainties in the plant and the ignored dynamics in modeling process are all regarded as the general disturbance, which can be estimated by Extended State Observer (ESO) and then actively compensated in real time. The performance of the proposed control strategy is tested on a Shell gasifier model by simulation experiments, and the Monte-Carlo method is also adopted to test the robustness of the control system. In comparison with the optimized PI controllers with probabilistic robustness, the simulation results show that the designed control system has advantages of better reference-tracking and disturbance-rejection capability, strong robustness and easy tuning procedure.

Decoupling PI controller design for aeroengine

In discrete-time domain,the optimal PI controller design problem for aeroengine was researched. Firstly,a general coupling PI controller design method was presented. The key ideas were to construct Lyapunov function to guarantee the closed-loop stability,and the performance was optimized under the given evaluation index. The controller design results can be cast as a convex optimization problem involving a set of linear matrix inequalities( LMIs),which can be solved efficiently with commercially available software. Then,considering engineering application status in China,to suit for the large thrust turbo fan aeroengine model characteristics and meet the control requirements,a decoupling PI controller design method was developed based on the general method,by constructing special PI controller and matrix variables. The influence of performance evaluation index matrix selection to controller design results was discussed,and the suggested structure of performance evaluation index matrix was presented. Finally,an example was presented to verify the proposed theoretical results.

Optimal PI controller design for active power in grid-connected SOFC DG system

This paper is concerned with optimal control of grid-connected solid oxide fuel cell (SOFC) stack based on a model developed and validated in the literature. Recent studies have shown that control of SOFC is challenging due to its slow dynamics and firm operating limits. For an SOFC, while the primary objective is to supply the demand active power, it is crucial to operate fuel cell within its safe operating constraints. In order to meet these requirements, a proper control strategy is developed in this study. This control strategy employs an optimal robust PI controller to control active power of the plant and at the same time satisfies physical and operating constraints via employing two proportional-gained controllers, of fuel utilization factor controller and anode–cathode pressure difference controller in such a way to maintain the fuel utilization factor at its optimal value of 85% and also keep pressure difference between anode and cathode within the safe bound of 0–0.08atm under transient conditions. A distributed generation (DG) system including an SOFC stack connected to the power grid through an IGBT inverter is implemented in MATLAB/SIMULINK™ environment. Moreover, dynamics of fuel processor are included. In addition, differential evolution (DE) algorithm evaluated by integral of time multiplied by absolute error (ITAE) criterion is used to search for optimal values of PI controller parameters. Dynamics of the DG system are analyzed for the cases of conventional and proposed PI controllers under different load changes and short circuit condition to verify the performance of proposed PI controller. Simulation results show that the proposed controller can provide satisfactory performance for load changes and short circuit condition at the cost of keeping the SOFC performance beyond its operating constraints.

Performance evaluation of optimal PI controller for ALSTOM gasifier during coal quality variations

Coal gasifier, an essential part of Integrated Gasification Combined Cycle (IGCC) converts coal into synthesis gas (syngas or producer gas) under certain pressure and temperature. The quality of syngas is highly influenced by quality of coal (calorific value) and hence greatly affects the power generation. Gasifier control seems to be highly difficult since it involves many variables and inherent nonlinearity. The baseline PI controller provided with ALSTOM benchmark challenge II (benchmark model of coal gasifier) fails to satisfy the constraints at 0% load for sinusoidal pressure disturbance and coal quality variations (±18%). This paper evaluates the tuning parameters of ALSTOM benchmark challenge II using Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm. Robustness of the optimal PI controller is tested under sinusoidal and step pressure disturbance tests at 100%, 50% and 0% load conditions with decreased and increased coal quality variations. Test results show that the optimal PI controller meets all the constraints comfortably at all load conditions and provides better results for coal quality variations.