Direct power control for grid-connected doubly fed induction generator using disturbance observer based control

A disturbance observer based control method for a grid-connected doubly fed induction generator is presented in this study. The proposed control method consists of a state-feedback controller and a disturbance observer (DO). The DO is used to compensate for model uncertainties with the aim of removing the steady-state error. The control objective consists of regulating the stator currents instead of the rotor currents in order to achieve direct control of the stator active and reactive powers. Such a control scheme removes the need for an exact knowledge of the machine parameters to achieve accurate control of the stator active and reactive powers. The main advantage of this control method is ensuring a good transient performance as per the controller design specifications, while guaranteeing zero steady-state error. Moreover, the proposed control method was experimentally validated on a small scale DFIG setup

Nonlinear PI control for variable pitch wind turbine

Wind turbine uses a pitch angle controller to reduce the power captured above the rated wind speed and release the mechanical stress of the drive train. This paper investigates a nonlinear PI (N-PI) based pitch angle controller, by designing an extended-order state and perturbation observer to estimate and compensate unknown time-varying nonlinearities and disturbances. The proposed N-PI does not require the accurate model and uses only one set of PI parameters to provide a global optimal performance under wind speed changes. Simulation verification is based on a simplified two-mass wind turbine model and a detailed aero-elastic wind turbine simulator (FAST), respectively. Simulation results show that the N-PI controller can provide better dynamic performances of power regulation, load stress reduction and actuator usage, comparing with the conventional PI and gain-scheduled PI controller, and better robustness against of model uncertainties than feedback linearization control

Wind turbine power coefficient models based on neural networks and polynomial fitting

The power coefficient parameter represents the aerodynamic wind turbine efficiency. Since the 1980s, several equations have been used in the literature to study the power coefficient as a function of the tip speed ratio and the pitch angle. In this study, these equations are reviewed and compared. A corrected blade element momentum algorithm is used to generate three sets of data representing different ranges of wind turbines, going from 2 to 10 MW. With this information, two power coefficient models are proposed and shared. One model is based on a polynomial fitting, whereas the other is based on neural network techniques. Both were trained with the blade element momentum model output data and showed good behaviour for all operating ranges. In the results, compared to all the algorithms found in the literature, the proposed models reduced the power coefficient error by at least 55% compared to the best numerical approximation from the literature. An error reduction in the power coefficient parameter may have a large impact on many wind energy conversion system studies, such as those treating dynamic and transient behavioursPublicad

Output power levelling for DFIG wind turbine system using intelligent pitch angle control

Blade pitch angle control, as an indispensable part of wind turbine, plays a part in getting the desired power. In this regard, several pitch angle control methods have been proposed in order to limit aerodynamic power gained from the wind turbine system (WTS) in the high-windspeed regions. In this paper, intelligent control methods are applied to control the blade pitch angle of doubly-fed induction generator (DFIG) WTS. Conventional fuzzy logic and neuro-fuzzyparticle swarm optimization controllers are used to get the appropriate wind power, where fuzzy inference system is based on fuzzy c-means clustering algorithm. It reduces the extra repetitive rules in fuzzy structure which in turn would reduce the complexity in neuro-fuzzy network with maximizing efficiently. In comparing the controllers at any given wind speed, adaptive neuro-fuzzy inference systems controller involving both mechanical power and rotor speed revealed better performance to maintain the aerodynamic power and rotor speed at the rated value. The effectiveness of the proposed method is verified by simulation results for a 9 MW DFIG WTS

Stability analysis and robust control of power networks in stochastic environment

The modern power grid is moving towards a cleaner form of energy, renewable energy to meet the ever-increasing demand and new technologies are being installed in the power network to monitor and maintain a stable operation. Further, the interactions in the network are not anymore localized but take place over a system, and the control centers are located remotely, thus involving control of network components over communication channels. Further, given the rapid integration of wind energy, it is essential to study the impact of wind variability on the system stability and frequency regulation. Hence, we model the unreliable and intermittent nature of wind energy with stochastic uncertainty. Moreover, the phasor measurement unit (PMU) data from the power network is transmitted to the control center over communication channels, and it is susceptible to inherent communication channel uncertainties, cyber attacks, and hence, the data at the receiving end cannot be accurate. In this work, we model these communication channels with stochastic uncertainties to study the impact of stochastic uncertainty on the stability and wide area control of power network. The challenging aspect of the stability analysis of stochastic power network is that the stochastic uncertainty appears multiplicative as well as additive in the system dynamics. The notion of mean square exponential stability is considered to study the properties of stochastic power network expressed as a networked control system (NCS) with stochastic uncertainty. We develop, necessary and sufficient conditions for mean square exponential stability which are shown in terms of the input-output property of deterministic or nominal system dynamics captured by the mean square system norm and variance of the channel uncertainty. For a particular case of single input channel uncertainty, we also prove a fundamental limitation result that arises in the mean square exponential stabilization of the continuous-time linear system. Overall, the theoretical contributions in this work generalize the existing results on stability analysis from discrete-time linear systems to continuous-time linear systems with multiplicative uncertainty. The stability results can also be interpreted as a small gain theorem for continuous-time stochastic systems. Linear Matrix Inequalities (LMI)-based optimization formulation is provided for the computation of mean square system norm for stability analysis and controller synthesis. An IEEE 68 bus system is considered, and the fragility of the decentralized load-side primary frequency controller with uncertain wind is shown. The critical variance value is shown to decrease with the increase in the cost of the controllable loads and with the rise in penetration of wind farms. Next, we model the power network with detailed higher order differential equations for synchronous generator (SG), wind turbine generator (WTG). The network power flow equations are expressed as algebraic equations. The resultant system is described by a detailed higher order nonlinear differential-algebraic model. It is shown that the uncertainty in the wind speed appears multiplicative in the system dynamics. Stochastic stability of such systems is characterized based on the developed results on mean square exponential stability. In particular, we study the stochastic small signal stability of the resultant system and characterize the critical variance in wind speeds, beyond which the grid dynamics becomes mean square unstable. The power fluctuations in the demand side and intermittent generation (from renewables) cause frequency excursions from the nominal value. In this context, we consider the controllable loads which can vary their power to achieve frequency regulation based on the frequency feedback from the network. Two different load-side frequency controller strategies, decentralized and distributed frequency controllers are studied in the presence of stochastic wind. Finally, the time-domain simulations on an IEEE 39 bus system (by replacing some of the traditional SGs with WTG) are shown using the wind speeds modeled as stochastic as well as actual wind speeds obtained from the wind farm located near Ames, Iowa. It can be seen that, with an increase in the penetration of wind generation in the network, the network turns mean square unstable. Furthermore, we capture the mean square unstable behavior of the power network with increased penetration of renewables using the statistics of actual wind analytically and complement them through linear and nonlinear time domain simulations. Finally, we analyze the vulnerability of communication channel to stochastic uncertainty on an IEEE 39 bus system and design a wide area controller that is robust to various sources of uncertainties that arise in the communication channels. Further, the PMU measurements and wide area control inputs are rank ordered based on their criticality

Robust Active and Reactive Power Control Schemes for a Doubly Fed Induction Generator Based Wind Energy Conversion System

In view of resolving rising environmental concern arising out of fossil fuel based power generation, more electricity has to be generated from renewable energy sources. Out of the several renewable energy options available today, wind energy is considered to be the most promising one due to its high energy conversion efficiency compared to one of its competitors, i.e. the solar photovoltaic system. Now-a-days, large wind farms are generating thousands of megawatts of power feeding to the grid. In literature, number of controllers such as conventional proportional integral (PI) control, linear parameter varying (LPV) control, gain scheduling control, robust control, model predictive control have been proposed for both torque and pitch control. In these controllers, some of the important issues such as robustness for nonlinear dynamics of wind turbine and stability are not considered simultaneously. Hence, it is necessary to design appropriate controllers for extracting maximum power from the wind turbine whilst the robustness and stability of the Wind Energy Conversion System (WECS) are ensured. Hence, in this thesis, firstly the focus is made to design control system for the wind turbine coupled with the DFIG (torque and pitch control) using one of the very promising robust control paradigm called sliding mode controller for achieving robustness, reducing chattering phenomenon and stability of the WECS. Since the number of terms in control inputs (i.e. torque and pitch angle) and outputs (i.e. DFIG output power and speed) are more in wind control dynamics, selection of significant terms is important for reducing the complexity of controlling. Therefore, a Nonlinear Autoregressive Moving Average with exogenous input (NARMAX) model of the WECS has been developed. The parameters of this NARMAX model are estimated by suitably designing an on-line adaptive Recursive Least squares (RLS) algorithm. Subsequently for controlling speed and achieving efficient power regulation of the WECS a nonlinear model predictive controller (NAMPC) has been developed in which the control variables (torque and pitch) are optimised by formulating a cost function. Subsequently for the WECS, the power converters connecting the DFIG to the grid have been designed. For controlling stator active and reactive power of DFIG connected to the grid, a state feedback controller for the DFIG has been developed using a linear quadratic optimal theory with preview concept. This Linear Quadratic Regulator Optimal Preview Control (LQROPC) scheme is employed with a stator voltage oriented control (SVOC) technique. This Optimal preview control is used to solve the tracking and rejection problems with an assumption that the signals to be tracked or rejected are available a priori by a certain amount of time. Even though the OPC provides very good tracking and disturbance suppression performance, but it is sensitive to the DFIG circuit parameters which makes the WECS system unstable. Hence, to address the parameter uncertainty of the DFIG, a sliding mode controller has been proposed and the robustness of the WECS have been verified by using the Lyapunov criterion. Then, a 2 kW DFIG based WECS experimental setup has been developed in the laboratory to study the effectiveness of the controllers developed

Advanced control of doubly-fed induction generator based variable speed wind turbine

This thesis deals with the modeling, control and analysis of doubly fed induction generators (DFIG) based wind turbines (DFIG-WT). The DFIG-WT is one of the mostly employed wind power generation systems (WPGS), due to its merits including variable speed operation for achieving the maximum power conversion, smaller capacity requirement for power electronic devices, and full controllability of active and reactive powers of the DFIG. The dynamic modeling of DFIG-WT has been carried out at first in Chapter 2, with the conventional vector control (VC) strategies for both rotor-side and grid-side converters. The vector control strategy works in a synchronous reference frame, aligned with the stator-flux vector, became very popular for control of the DFIG. Although the conventional VC strategy is simple and reliable, it is not capable of providing a satisfactory transient response for DFIG-WT under grid faults. As the VC is usually designed and optimized based on one operation point, thus the overall energy conversion efficiency cannot be maintained at the optimal point when the WPGS operation point moves away from that designed point due to the time-varying wind power inputs. Compared with VC methods which are designed based on linear model obtained from one operation point, nonlinear control methods can provide consistent optimal performance across the operation envelope rather than at one operation point. To improve the asymptotical regulation provided by the VC, which can't provide satisfactory performance under voltage sags caused by grid faults or load disturbance of the grid, input-output feedback linearization control (IOFLC) has been applied to develop a fully decoupled controller of the active $\&$ reactive powers of the DFIG in Chapter 3. Furthermore, a cascade control strategy is proposed for power regulation of DFIG-WT, which can provide better performance against the varying operation points and grid disturbance. Moreover, to improve the overall energy conversion efficiency of the DFIG-WT, FLC-based maximum power point tracking (MPPT) has been investigated. The main objective of the FLC-based MPPT in Chapter 4 is to design a global optimal controller to deal with the time-varying operation points and nonlinear characteristic of the DFIG-WT. Modal analysis and simulation studies have been used to verify the effectiveness of the FLC-based MPPT, compared with the VC. The system mode trajectory, including the internal zero-dynamic of the FLC-MPPT are carefully examined in the face of varied operation ranges and parameter uncertainties. In a realistic DFIG-WT, the parameter variability, the uncertain and time-varying wind power inputs are existed. To enhance the robustness of the controller, a nonlinear adaptive controller (NAC) via state and perturbation observer for feedback linearizable nonlinear systems is applied for MPPT control of DFIG-WT in Chapter 5. In the design of the controller, a perturbation term is defined to describe the combined effect of the system nonlinearities and uncertainties, and represented by introducing a fictitious state in the state equations. As follows, a state and perturbation observer is designed to estimate the system states and perturbation, leading to an adaptive output-feedback linearizing controller which uses the estimated perturbation to cancel system perturbations and the estimated states to implement a linear output feedback control law for the equivalent linear system. Case studies including with and without wind speed measurement are carried out and proved that the proposed NAC for MPPT of DFIG-WT can provide better robustness performance against the parameter uncertainties. Simulation studies for demonstrating the performance of the proposed control methods in each chapter, are carried out based on MATLAB/SIMULINK