Gain Scheduled PI Controller Research Articles

Fuzzy Gain Scheduling PI Controller Based PSS for SMIBS

Fuzzy Gain Scheduling PI Controller Based PSS for SMIBS

Multiobjetive H2/H control of a pitch regulated wind turbine for mechanical load reduction.

The paper deals with the design of acontrol system for a variable-speed pitch-regulatedwind turbine in the operating region correspond-ing to wind speeds above rated wind speed. Thecontrol objectives are mostly to regulate the plantoutput power and to reduce the mechanical fatigueof the plant components. The controller is designedfrom a non linear model of the system which takesinto account the blades, shaft and tower flexibil-ities. A Linear Matrix Inequalities (LMI) formu-lation of the control problem permits to obtaina Linear Parameter Varying (LPV) controller op-timizing, throughH2/H∞norm minimization, acriteria close to mechanical fatigue affecting theplant. The proposed controller performances arethen compared in simulation with those of a gainscheduling Linear Quadratic Gaussian (LQG) con-troller and with a gain scheduling PI controller, andis seen to be more efficient.

Automated Tuning of Gain-Scheduled PI Controllers Based on SANARX Models

This paper presents a data-driven method to automatically tune gain-scheduled PI controllers for linear parameter-varying (LPV) systems. First, input-output data from a system is used to train a neural network (NN) based simplified additive nonlinear autoregressive exogenous (SANARX) model. After reformulating this into a state space representation, an H∞ method is used to obtain the PI parameters for any sampled working point. Throughout the entire pipeline, this work uses data from a real-world hydraulic test rig, which also serves as the system where the resulting gain-scheduled controller is evaluated on. Overall, no prior system knowledge is necessary to utilize this method.

Multi model robust control design for a floating offshore variable speed wind turbine with tension leg platform

This paper presents a multi-model robust control (MMRC) design for an offshore variable speed wind turbine with tension leg platform. The proposed control scheme covers the model uncertainty in the above rated wind speed, and it provides a reliable control for power regulation while minimizing the mechanical loads on the wind turbine structure. For this purpose, the above rated wind speed region is divided into several wind speed groups, and a set of linearized models are obtained from the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) simulator for various mean wind speeds of each group. Using Weibull wind speed distribution, a nominal model with additive uncertainty is generated for each wind speed group, to be used in the multi-model robust control design. The MATLAB/Simulink software environment is used to verify the performance of the proposed control strategy using the National Research Energy Laboratory (NREL) three-bladed horizontal axis 5-MW baseline wind turbine. The performance of the proposed multi-model robust controller is compared with the state-of-the-art gain-scheduling PI (GSPI) controller. The results show improvement in power regulation and platform movement minimization while reducing the mechanical loads of the wind turbine. By using the proposed control scheme, the severe oscillation in the blade pitch actuation and power output, which is due to the changes in the operating point of the wind turbine, decreases drastically.

Hybrid Stepper Motor: Model, Open-loop Test, Traditional PI, Optimized PI, and Optimized Gain Scheduled PI Controllers

Hybrid Stepper Motor: Model, Open-loop Test, Traditional PI, Optimized PI, and Optimized Gain Scheduled PI Controllers

Nonlinear analysis of gain scheduled controllers for the NREL 5-MW turbine blade pitch control system

Gain-scheduling is a conventional approach for adjusting the wind turbine controller performance against operating point variations in industrial applications. Although the scheduling idea seems uncomplicated, several configurations available in research papers and technical reports are subject to inaccuracy. This paper discusses the effect of inaccurate controller realization by choosing NREL 5-MW, the ubiquitous wind turbine in the research community, and citing various controller configurations available in the literature. By performing a nonlinear analysis, it is shown that hidden terms emerge from inaccurate realizations, which could deteriorate the controller performance or lead to controller structures with different performance indexes from the original design. To remedy this issue, four implementation modifications are introduced to maintain the intended performance of the gain-scheduled controller. Although the paper is focused on the baseline gain-scheduled PI controller, modifications are generalized to arbitrary pitch control systems. Simulations with the full-order dynamics of the FAST code for the pitch angle-based and wind speed-based gain-scheduled baseline controllers and a more complex gain-scheduled fractional-order PI controller verify the presented analysis.

Practical Implementation of a Hydro Power Unit Active Power Regulation Based on an MPC Algorithm

Traditionally, proportional-integral-derivative (PID) based hydro turbine governors are mainly used due to their robustness and simple implementation. However, the weakness of these controllers is their design based on linear models and fixed parameters structure. This implies that the controller's parameters are usually calculated for the critical operating point leading to the controller's underperformance when operating away from the critical operating point. This paper introduces a Model Predictive Control (MPC) based hydro turbine's governor load/frequency controller whose linear prediction model parameters are updated depending on the operating point. The main intention of the paper is to reduce the gap between theoretical contributions and industrial practice. The experimental validation is achieved by implementing the introduced MPC algorithm to a laboratory hydro power plant's governor Programmable Logic Controller (PLC) and comparing its response with the responses of the gain-scheduled PI (GS-PI) controller, PI controller based on Particle Swarm Optimization (PSO-PI) and the controller based on exponential control law (EXP).

Nonlinear modelling and adaptive control of smart rotor wind turbines

This paper develops a nonlinear mid-fidelity aeroservoelastic model for smart rotor wind turbines and studies the turbulent load alleviation of the wind turbines with trailing edge flaps (TEFs) actuated by a novel proportional-derivative model-free adaptive control (PD-MFAC) algorithm. This nonlinear model contains a structural model for the tower and blades represented by geometrically non-linear composite beams and an aerodynamic model for the rotor using a vortex panel method coupled with a stall delay process. The capability of the new aerodynamic model to deal with flow separations and analyze the detailed flow field enables it to simulate the dynamic response of the wind turbine blade sections with TEFs with arbitrary size and deflection angles. It is shown that the TEF alters the aerodynamic coefficients in a complex manner which could be explained by the evolution of the detailed vortical field. Furthermore, three independent PD-MFAC TEF controllers are designed to alleviate the turbulent load acting on the wind turbines. The effectiveness of the controller in terms of turbulent load alleviation is evaluated by the root mean square value of the blade root-bending moment (RBM). A traditional Gain-scheduled PI (GS-PI) controller is also designed as a comparison to the PD-MFAC controller. Simulation results show that to reduce the RBM and blade tip deflection (BTD) caused by external disturbances, the PD-MFAC flap controller shows more effective performance than the GS-PI flap controllers.

Torque ripple and noise control of switched reluctance motor using an adaptive fuzzy PI control with the aid of AR algorithm

In recent days switched reluctance motor is widely used for numerous industrial applications due to its simple structure, minimum cost and maximum efficiency. Regardless of numerous exclusive benefits of the switched reluctance motor (SRM), acoustic noise of this motor is high and it is important to accomplish more analysis on the noise lessening, which is the primary goal of this paper. The major causes of acoustic noise in a SRM are torque ripple and radial magnetic force. Since radial magnetic force is highly influential by the design of motor, torque ripple control is analysed in this article for acoustic noise control. Torque ripple control of SRM is proposed using optimization in direct torque control (DTC) method. Nowadays, optimisation plays a crucial role in motor drives for enhanced control. In this paper, artificial raindrop algorithm is proposed in DTC of SRM to minimise torque ripple. Performance of proposed ARA based DTC of four-phase 8/6 SRM is analysed using Matlab and compared with the performance of fuzzy gain scheduling PI controller based DTC.

Stochastic Distribution Controller for Wind Turbines with Doubly Fed Induction Generator

The major purpose of this work is to design the controllers for controlling thevariable speed, variable pitch wind turbine (WT) with doubly fed inductiongenerator (DFIG). Vector control strategy is adopted for controlling theDFIG active and reactive power. Generator torque is control to provide theregulated real power with minimum fluctuation. The fixed gain proportional-integral (PI) controller designed to the converter of rotor side and gridside has limited operating range and inherent overshoot. Gain schedulingPI controller is designed to minimize the overshoot and fluctuation existsin proportional-integral controller. Since DFIG based wind energy conver-sion system (WECS) works in uncertain wind speed, stochastic distributioncontrol (SDC) method is proposed to control the probability distributionfunction (PDF) of DFIG based WECS. It copes with nonlinearities in theWECS and contiguous variations at operating point and provides satisfactoryperformance for the whole operating region. It improves the performancetogether with power quality of generated electric power thereby maximizing the lifespan of installation and ensures secure and acceptable operation of theDFIG based WECS.

Speed Control and Torque Ripple Reduction of SRM Using Fuzzy Gain Scheduling PI Controller

Speed Control and Torque Ripple Reduction of SRM Using Fuzzy Gain Scheduling PI Controller

Performance analysis of a robust adaptive fuzzy logic controller for wind turbine power limitation

Power limitation and load reduction are considered the main challenges for the large scale wind turbine when the system operates in the above rated wind speeds. The controller design still a complex and critical task in the presence of the system nonlinearities and the wind turbulent behavior to produce a cleaner energy without switching harmonics. In this context, an adaptive controller based on fuzzy logic concept is proposed to limit the extracted power at rated value with improving its quality and to mitigate the loads applied on the turbine and the drive train in full loads conditions by adjusting the pitch angle. First, the different parts of the DFIG wind system are modeled. Thereafter, the main control structures in different operating regions are briefly described. The second part is focused on the pitch control technique. The initial parameters of fuzzy logic controller (FLC) are defined based on literature studies. Then, those parameters are tuned to derive the optimized FLC. Finally, the scaling factors of this last configuration are automatically adjusted by means of a correction factor to develop the Adaptive FLC. By the end, a technical and economic comparative studies are conducted between the three fuzzy controller configurations and the classical structures which are classical PI controller and Gain scheduled PI controller using several performance indicators. The responses assessment obtained from virtual simulation using MATLAB/SIMULINK show that the adaptive FLC is the best solution for the pitch control in terms of rapidity, stability and robustness and allow producing higher electrical energy with lowest harmonics rate.

Design of Fuzzy Gain Scheduled PI Controller for an Isolated Wind Energy Conversion System

Design of Fuzzy Gain Scheduled PI Controller for an Isolated Wind Energy Conversion System

Power and motion control of a floating wind turbine: an original solution based on adaptive second order sliding mode control

A new control scheme based on adaptive super-twisting algorithm is proposed for a floating wind turbine equipped by a permanent magnet synchronous generator. The adaptive control method is especially efficient for systems with uncertainties and external perturbations and, therefore, is well adapted to wind turbines systems. Such controller can be implemented with very limited knowledge of system model (only the relative degree is required) that greatly reduces the controller gains tuning effort. Simulations are made on FAST software and compared with a standard gain-scheduled PI controller.

Adaptive control design and implementation for collective pitch in wind energy conversion systems

This paper introduces a discrete-time L1 adaptive controller design for collective pitch control for variable speed variable pitch wind turbines. In this control algorithm, the blades’ pitch angles are regulated to manipulate the generator speed and generated power during high speeds wind airflow. The main merits of the proposed controller are its robustness against the wind turbine model uncertainties, guaranteeing the closed loop system stability and performance, and applicability during implementation in real life. Commonly used industrial gain-scheduled PI controller is used to be compared with the proposed controller. The proposed scheme is simulated using a 5-MW offshore turbine model and the obtained results illustrates the superior performance of the proposed controller. Further, a reduced scale wind turbine emulator is used to provide hardware in the loop to test the implementation of the proposed controller. The controller is implemented using laboratory equipment to demonstrate the feasibility of the controller for real time applications. The obtained experimental results validate the closed loop system performance enhancement due to replacing the traditional PI controller with the proposed design.

Modeling and Control Unmanned Surface Vehicle (USV) with Hybrid Drivetrain

With the rapid development of unmanned surface vehicle (USV), the development of hybrid electric vehicles on USV is a technology that has a very important role to overcome the problem of how long the vehicle can be used. In hybrid electric vehicles (HEV), the main components consist of generators, electric motors, and batteries. Modeling component is intended to design models that fit on unmanned surface vehicle and represent the state of USV with hybrid drivetrain. This paper focuses on modeling USV with hybrid drivetrain. This vehicle uses a series configuration so that the generator act as a power generating system. The power system on USV was chosen using hybrid sources of DC generator and lithium-ion battery pack. The optimal size of DC generator and battery pack are very important to ensure that USV can work for a long period of time. This paper describes the simple characteristics of a DC generator with a PI controller and gain-scheduling PI controller which is accurate in generating 380V output voltage according to the initial plan to be built. We also described the model of permanent magnet dc motor as a driver in USV and described simulation about 200Ah 36V lithium-ion batteries. The development of this model will be represented in MATLAB/Simulink.

Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines

In the variable rotor speed and variable blade pitch wind-turbines system, the pitch controller is crucial in the high wind speed range to adjust the generator speed so as to produce the rated power. The main challenges in designing the pitch controller are the wind turbine’s nonlinearities, constraints on pitch-angle, the variations in wind-speed, and the unstructured model dynamics. From this perspective, a new pitch controller is proposed by employing partial offline quasi-min-max fuzzy model-predictive control to investigate the variable-speed wind turbine performance. Based on the fuzzy modeling, the online optimization problem is simplified as a partial offline optimization problem (offline design and online synthesis). The key advantage of this controller is the guaranteed stability with actuator constraints using LMI constraints and less computational burden. Also, this controller is compared with standard gain scheduled-PI (proportional integral) controller, which has been utilized profusely in the wind-turbine industry. Furthermore, a typical 5 MW benchmark wind-turbine is employed to validate the results from the nonlinear mathematical model. Several case studies are made to prove the proposed controller effectiveness. The results show the superiority of the proposed controller over the gain scheduled-PI controller and two other advanced control techniques.

Adaptive super-twisting control of floating wind turbines with collective blade pitch control

Abstract This paper proposes an adaptive super-twisting controller for floating wind turbine based on collective blade pitch control. This adaptive second order sliding mode control scheme being efficient for systems with uncertainties and external perturbations, which is compared with the traditional gain scheduled PI (GSPI) controller. It is shown that this kind of controller requires reduced knowledge about the system (only the relative degree) and the gain tuning effort is also reduced given that the operating domain is large versus GSPI controller. Simulation results show high performances of the proposed controller for rotor speed regulation and reduction of platform oscillations in Region 3 of the floating wind turbine.

Analysis and Suppression for Frequency Oscillation in a Wind-Diesel System

In a wind-diesel system with weak network architecture, many techniques are utilized to enhance its stability. However, the frequency oscillation between the grid-side converter and the diesel generator may be caused by their interactions, and there are only a few studies considering this issue, which is a major challenge for the stability. To tackle this challenge, the cause of frequency oscillation is studied, and an adaptive fuzzy PI controller based on the variable universe is proposed to suppress oscillation in this paper. The control parameter of the wind turbine generator (WTG) converter's dc-link voltage loop and wind speed has an important influence on the oscillation. Our main idea is to suppress frequency oscillation by the optimizing control parameter. In order to analyze the interaction between WTG and diesel generator, we develop the small-signal models of the wind-diesel generator. Subsequently, the adaptive fuzzy PI controller based on the variable universe is proposed in the dc-link voltage loop to optimize control parameter instead of gain-scheduled PI controller, and its key feature is the innovative use of an extension factor with d-axis current in the output universe. The function of the extension factor is to adapt WTG to different wind speeds. The experimental results demonstrate that the proposed controller can significantly suppress frequency oscillation. The oscillation amplitude of isolated grid frequency is also significantly reduced by up to 0.175 Hz.

Decentralized neuro-fuzzy controllers of nonlinear quadruple tank system

This paper presented two decentralized neuro-fuzzy controllers to control the level in lower two tanks in nonlinear quadruple tank system (QTS). The controllers are designed based on adaptive neuro-fuzzy inference system technique. The relation between inputs/outputs was proved using relative gain array, and then, we can divide the quadruple tank system into two subsystems and control each of them separately. The first controller is a neuro-fuzzy inverse nonlinear (NFIN) model, which predicts the voltage required to control the level to track the referred one. So, the voltage was fed to neuro-fuzzy forward nonlinear model (NFFN) to obtain the desired level. The second is neuro-fuzzy nonlinear gain scheduling PI controller, which is designed to control the nonlinear QTS at any operating point. The results show that the NFIN controller has a more accurate tracking level and less computational time in both minimum and non-minimum phases.