Predictive control approaches to fault tolerant control of wind turbines

This thesis focuses on active fault tolerant control (AFTC) of wind turbine systems. Faults in wind turbine systems can be in the form of sensor faults, actuator faults, or component faults. These faults can occur in different locations, such as the wind speed sensor, the generator system, drive train system or pitch system. In this thesis, some AFTC schemes are proposed for wind turbine faults in the above locations. Model predictive control (MPC) is used in these schemes to design the wind turbine controller such that system constraints and dual control goals of the wind turbine are considered. In order to deal with the nonlinearity in the turbine model, MPC is combined with Takagi-Sugeno (T-S) fuzzy modelling. Different fault diagnosis methods are also proposed in different AFTC schemes to isolate or estimate wind turbine faults.The main contributions of the thesis are summarized as follows:A new effective wind speed (EWS) estimation method via least-squares support vector machines (LSSVM) is proposed. Measurements from the wind turbine rotor speed sensor and the generator speed sensor are utilized by LSSVM to estimate the EWS. Following the EWS estimation, a wind speed sensor fault isolation scheme via LSSVM is proposed.A robust predictive controller is designed to consider the EWS estimation error. This predictive controller serves as the baseline controller for the wind turbine system operating in the region below rated wind speed.T-S fuzzy MPC combining MPC and T-S fuzzy modelling is proposed to design the wind turbine controller. MPC can deal with wind turbine system constraints externally. On the other hand, T-S fuzzy modelling can approximate the nonlinear wind turbine system with a linear time varying (LTV) model such that controller design can be based on this LTV model. Therefore, the advantages of MPC and T-S fuzzy modelling are both preserved in the proposed T-S fuzzy MPC.A T-S fuzzy observer, based on online eigenvalue assignment, is proposed as the sensor fault isolation scheme for the wind turbine system. In this approach, the fuzzy observer is proposed to deal with the nonlinearity in the wind turbine system and estimate system states. Furthermore, the residual signal generated from this fuzzy observer is used to isolate the faulty sensor.A sensor fault diagnosis strategy utilizing both analytical and hardware redundancies is proposed for wind turbine systems. This approach is proposed due to the fact that in the real application scenario, both analytical and hardware redundancies of wind turbines are available for designing AFTC systems.An actuator fault estimation method based on moving horizon estimation (MHE) is proposed for wind turbine systems. The estimated fault by MHE is then compensated by a T-S fuzzy predictive controller. The fault estimation unit and the T-S fuzzy predictive controller are combined to form an AFTC scheme for wind turbine actuator faults

Wind Turbine Controls for Farm and Offshore Operation

Development of advanced control techniques is a critical measure for reducing the cost of energy for wind power generation, in terms of both enhancing energy capture and reducing fatigue load. There are two remarkable trends for wind energy. First, more and more large wind farms are developed in order to reduce the unit-power cost in installation, operation, maintenance and transmission. Second, offshore wind energy has received significant attention when the scarcity of land resource has appeared to be a major bottleneck for next level of wind penetration, especially for Europe and Asia. This dissertation study investigates on several wind turbine control issues in the context of wind farm and offshore operation scenarios. Traditional wind farm control strategies emphasize the effect of the deficit of average wind speed, i.e. on how to guarantee the power quality from grid integration angle by the control of the electrical systems or maximize the energy capture of the whole wind farm by optimizing the setting points of rotor speed and blade pitch angle, based on the use of simple wake models, such as Jensen wake model. In this study, more complex wake models including detailed wind speed deficit distribution across the rotor plane and wake meandering are used for load reduction control of wind turbine. A periodic control scheme is adopted for individual pitch control including static wake interaction, while for the case with wake meandering considered, both a dual-mode model predictive control and a multiple model predictive control is applied to the corresponding individual pitch control problem, based on the use of the computationally efficient quadratic programming solver qpOASES. Simulation results validated the effectiveness of the proposed control schemes. Besides, as an innovative nearly model-free strategy, the nested-loop extremum seeking control (NLESC) scheme is designed to maximize energy capture of a wind farm under both steady and turbulent wind. The NLESC scheme is evaluated with a simple wind turbine array consisting of three cascaded variable-speed turbines using the SimWindFarm simulation platform. For each turbine, the torque gain is adjusted to vary/control the corresponding axial induction factor. Simulation under smooth and turbulent winds shows the effectiveness of the proposed scheme. Analysis shows that the optimal torque gain of each turbine in a cascade of turbines is invariant with wind speed if the wind direction does not change, which is supported by simulation results for smooth wind inputs. As changes of upstream turbine operation affects the downstream turbines with significant delays due to wind propagation, a cross-covariance based delay estimate is proposed as adaptive phase compensation between the dither and demodulation signals. Another subject of investigation in this research is the evaluation of an innovative scheme of actuation for stabilization of offshore floating wind turbines based on actively controlled aerodynamic vane actuators. For offshore floating wind turbines, underactuation has become a major issue and stabilization of tower/platform adds complexity to the control problem in addition to the general power/speed regulation and rotor load reduction controls. However, due to the design constraints and the significant power involved in the wind turbine structure, a unique challenge is presented to achieve low-cost, high-bandwidth and low power consumption design of actuation schemes. A recently proposed concept of vertical and horizontal vanes is evaluated to increase damping in roll motion and pitch motion, respectively. The simulation platform FAST has been modified including vertical and horizontal vane control. Simulation results validated the effectiveness of the proposed vertical and horizontal active vane actuators

A survey of differential flatness-based control applied to renewable energy sources

Conference ProceedingsThis paper presents an overview of various methods used to minimize the fluctuating impacts of power generated from renewable energy sources. Several sources are considered in the study (biomass, wind, solar, hydro and geothermal). Different control methods applied to their control are cited, alongside some previous applications. Hence, it further elaborates on the adoptive control principles, of which includes; Load ballast control, dummy load control, proportional integral and derivative (PID) control, proportional integral (PI) control, pulse-width modulation (PWM) control, buck converter control, boost converter control, pitch angle control, valve control, the rate of river flow at turbine, bidirectional diffuser-augmented control and differential flatnessbased controller. These control operations in renewable energy power generation are mainly based on a steady-state linear control approach. However, the flatness based control principle has the ability to resolve the complex control problem of renewable energy systems while exploiting their linear properties. Using their flatness properties, feedback control is easily achieved which allows for optimal/steady output of the system components. This review paper highlights the benefits that range from better control techniques for renewable energy systems to established robust grid (or standalone generations) connections that can bring immense benefits to their operation and maintenance costs

Mathematical Modelling and Control of Renewable Energy Systems and Battery Storage Systems

PhD, 262ppIntermittent nature of renewable energy sources like the wind and solar energy poses new challenges to harness and supply uninterrupted power for consumer usage. Though, converting energy from these sources to useful forms of energy like electricity seems to be promising, still, significant innovations are needed in design and construction of wind turbines and PV arrays with BS systems. The main focus of this research project is mathematical modelling and control of wind turbines, solar photovoltaic (PV) arrays and battery storage (BS) systems. After careful literature review on renewable energy systems, new developments and existing modelling and controlling methods have been analysed. Wind turbine (WT) generator speed control, turbine blade pitch angle control (pitching), harnessing maximum power from the wind turbines have been investigated and presented in detail. Mathematical modelling of PV arrays and how to extract maximum power from PV systems have been analysed in detail. Application of model predictive control (MPC) to regulate the output power of the wind turbine and generator speed control with variable wind speeds have been proposed by formulating a linear model from a nonlinear mathematical model of a WT. Battery chemistry and nonlinear behaviour of battery parameters have been analysed to present a new equivalent electrical circuit model. Converting the captured solar energy into useful forms, and storing it for future use when the Sun itself is obscured is implemented by using battery storage systems presenting a new simulation model. Temperature effect on battery cells and dynamic battery pack modelling have been described with an accurate state of charge estimation method. The concise description on power converters is also addressed with special reference to state-space models. Bi-directional AC/DC converter, which could work in either rectifier or inverter modes is described with a cost effective proportional integral derivative (PID/State-feedback) controller

A two-layer control architecture for operational management and hydroelectricity production maximization in inland waterways using model predictive control

This work presents the design of a combined control and state estimation approach to simultaneously maintain optimal water levels and maximize hydroelectricity generation in inland waterways using gates and ON/OFF pumps. The latter objective can be achieved by installing turbines within canal locks, which harness the energy generated during lock filling and draining operations. Hence, the two objectives are antagonistic in nature, as energy generation maximization results from optimizing the number of lock operations, which in turn causes unbalanced upstream and downstream water levels. To overcome this problem, a two-layer control architecture is proposed. The upper layer receives external information regarding the current tidal period, and determines control actions that maintain optimal navigation conditions and maximize energy production using model predictive control (MPC) and moving horizon estimation (MHE). This information is provided to the lower layer, in which a scheduling problem is solved to determine the activation instants of the pumps that minimize the error with respect to the optimal pumping references. The strategy is applied to a realistic case study, using a section of the inland waterways in northern France, which allows to showcase its efficacy.Peer ReviewedPostprint (author's final draft