An improved real-time economic NMPC scheme for Wind Turbine control using spline-interpolated aerodynamic coefficients

For an efficient chemical reaction and a safe operation for both stationary and dynamic loads all values of anode and cathode gas pressures and stack temperature must fall within specific ranges. System error can result in values outside of these ranges, in turn causing serious damage to the fuel cell (FC). In addition the energy supply by the FC is at risk, because an emergency shutdown is possible. Therefore it is necessary to implement strategies for handling error cases in order to ensure safe operation. A common method for controlling the operation of PEM FC is a model predictive control, which allows for fault tolerance. In literature, error handling strategies are only considered for special parts of FC, but not for the whole system. This paper deals with real-time nonlinear model predictive control (NMPC) of electrical power with applied error handling strategies for a PEM FC system. Different types of errors are discussed in connection with various solution approaches. This fault tolerant NMPC handles all kinds of dynamic load changes and allows a safe operation of the FC in the event of errors, while maximizing power efficiency. Any emergency shutdown is not necessary and energy supply is still guaranteed. All experiments were verified on a test bench with a 4.4kW PEM FC.

In this paper, we propose a real-time nonlinear model predictive control (NMPC) strategy for stabilization of a vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear four-wheel vehicle model that neglects the wheel dynamics is coupled with a nonlinear tire model to design three MPC strategies of different levels of complexity that are implementable online: one that uses a linearized version of the vehicle model and then solves the resulting quadratic program problem to compute the necessary longitudinal slips on the rear wheels, a second one that employs the real-time iteration scheme on the NMPC problem, and a third one that applies the primal dual interior point method on the NMPC problem instead until convergence. Then, a sliding mode slip controller is used to compute the necessary torques on the rear wheels based on the requested longitudinal slips. After analyzing the relative tradeoffs in performance and computational cost between the three MPC strategies by comparing them against the optimal solution in a series of simulation studies, we test the most promising solution in a high-fidelity environment.

This report is the second in a series documenting a project whose main objective is to communicate the significant results of federal and privately funded large wind turbine (WT) generator development and field tests. This information is being communicated primarily to the electric utility industry.

Model Predictive Control (MPC) is a strong candidate for the control of large Multi-MegaWatt Wind Turbine Generators. Several MPC and some Nonlinear MPC scheme have been proposed in the literature, based on reference-tracking objective functions. While the resulting schemes offer very promising results, the difficulty of tuning a reference-tracking NMPC scheme for performance is likely to be a hindrance to the industrial success of NMPC-based WTG control. Because they directly maximize the system performance, economic NMPC schemes are more straightforward to tune. Economic NMPC schemes present, however, some known difficulties that are a serious obstacle to their real-time deployment. This paper presents an economic NMPC formulation for maximizing the generated power of wind turbine generators, which does not suffer from such difficulties. The relationship between the proposed and more classical reference-tracking approaches is formally established.

Unlike steady-state optimization and linear model predictive control, dynamic optimization and nonlinear model predictive control is underway to industry application. This paper discusses the implementation issues of two-level control strategy integrated dynamic real-time optimization and nonlinear model predictive control of a heat-integrated distillation column system. To realize this scheme, four nonlinear programming problems: economic operation optimization, data reconciliation and state estimation, parameter estimation and nonlinear model predictive control, are considered based on system rigorous dynamic model. The economic operation optimization is to minimize the cost of the plant. The unmeasured states are estimated by measurements of temperatures, pressures and flow rates. Time-variant parameters such as tray efficiencies and feed composition are updated on-line. The sequential quadratic programming and orthogonal collocation are used to solve the dynamic nonlinear optimization problems.

<div>The advancement in vision sensors and embedded technology created the opportunity in autonomous vehicles to look ahead in the future to avoid potential obstacles and steep regions to reach the target location as soon as possible and yet maintain vehicle safety from rollover. The present work focuses on developing a nonlinear model predictive controller (NMPC) for a high-speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles, and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in the MATLAB environment. The CasADi tool provides a platform to formulate the NMPC problem using symbolic expressions, which is an easy and efficient way of solving the optimization problem. In the present work, the vehicle lateral dynamics are modeled using the Pacejka nonlinear tire model. Further, a new algorithm is developed based on the box slope and box detection methods to process the stationary and moving obstacles. These methods use the vehicle’s current heading and generate a light detection and ranging (LIDAR) view through rectangular box regions. These box regions mimic the actual vision sensor regions, and logic can easily be applied to real vehicle conditions. From the results, it is observed that the vehicle avoids both moving and stationary lengthy obstacles and can safely navigate through a pool of obstacles that can mimic real-world off-road scenarios. Further, the simulations with Gaussian noise in vehicle state estimations and obstacle states confirmed that the developed algorithm can reach the target without collision by meeting all the vehicle safety constraints for the considered uncertainty limits.</div>

This paper presents the implementation and experimental validation of a central control framework. The presented framework addresses the need for a controller, which provides high performance combined with a low-computational load while being on-line adaptable to changes in the control scenario. Examples for such scenarios are cooperative control, task-based control and fault-tolerant control, where the system's topology, dynamics, objectives and constraints are changing. The framework combines a fast Nonlinear Model Predictive Control (NMPC), a communication interface with the Robot Operating System (ROS) as well as a modularization that allows an event-based change of the NMPC scenario. To experimentally validate performance and event-based adaptability of the framework, this paper is using a cooperative control scenario of Unmanned Aerial Vehicles (UAVs).

Nonlinear Model Predictive Control for Ecological Driver Assistance Systems in Electric Vehicles

Hierarchical nonlinear model predictive control for frequency support of wind farm

A lightweight wheel structure developed for rotor and stator use in large diameter direct-drive wind turbine generator is presented. The wheel structure uses layered sheet-steel elements to form the spokes and rim. This novel wheel structure is assumed to be more flexible than the existing conventional machine designs. Concerns with the dynamic response of the structure form the key motivation of the study presented here. As such, a computationally efficient methodology to predict the dynamic response has been presented. The dynamic model is employed to simulate the transient effects at startup and effects of cogging excitation on the direct-driven wind turbine.

Light Detection and Ranging (LIDAR) systems are able to measure the speed of incoming wind before it interacts with a wind turbine rotor. These preview wind measurements can be used in feedforward control systems designed to reduce turbine loads. However, the degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurate the incoming wind eld can be measured. This study examines the accuracy of dierent measurement scenarios that rely on coherent continuouswave Doppler LIDAR systems to determine their applicability to feedforward control. In particular, the impacts of measurement range and angular oset from the wind direction are studied for various wind conditions. A realistic case involving a scanning LIDAR unit mounted in the spinner of a wind turbine is studied in depth, with emphasis on choices for scan radius and preview distance. The eects of turbulence parameters on measurement accuracy are studied as well.

In this work a nonlinear model predictive control method for a floating wind turbine is presented. A reduced nonlinear model including disturbance preview of wind and waves is derived and implemented to compute optimal input trajectories for collective pitch and the generator torque. A cost functional is introduced which fulfills all desired constraints and controller goals for above rated wind conditions. The controller is tested for extreme and fatigue load cases and a significant reduction of the power and rotor speed deviations is obtained. Furthermore, ultimate tower loads and damage equivalent loads on shaft and blades are decreased. Although more detailed testing is necessary, this preliminary results show the advantages of nonlinear model predictive control for floating wind turbines.

Implicit Integrators for Linear Dynamics Coupled to a Nonlinear Static Feedback and Application to Wind Turbine Control

Individual tree level inventory performed using high density multi-return airborne Light Detection and Ranging (LiDAR) systems provides both internal and external geometric details on individual tree crowns. Among them, the parameters such as, the stem location, and Diameter at Breast Height of the stem (DBH) are very relevant for accurate biomass, and forest growth estimation. However, methods that can accurately estimate these parameters along the vertical canopy are lacking in the state of the art. Thus, we propose a method to locate and model the stem by analyzing the empty volume that appears within the 3D high density LiDAR point cloud of a conifer, due to the stem. In a high LiDAR density data, the points most proximal to the stem location in the upper half of the crown are very likely due to laser reflections from the stem and/or the branch-stem junctions. By locating accurately these points, we can define the lattice of points representing branch-stem junctions and use it to model the empty volume associated to the stem location. We identify these points by using a state-of-the-art internal crown structure modelling technique that models individual conifer branches in a high density LiDAR data. Under the assumption that conifer stem can be closely modelled using a cone shape, we regression fit a geometric shape onto the lattice of branch-stem junction points. The parameters of the geometric shape are used to accurately estimate the diameter at breast height, and height of the tree. The experiments were performed on a set of hundred conifers consisting of trees from six dominant European conifer species, for which the height and the DBH were known. The results prove the method to be accurate.

A novel Vernier type magnetically geared direct-drive generator for large wind turbines is introduced in this paper. Conventional Vernier-type machines and most of the direct-drive wind turbine generators use excessive amount of permanent magnet, which increases the overall cost and makes the manufacturing process challenging. In this paper, an electrically excited (PM_less) claw-pole type Vernier machine is presented. This new topology has the potential of reducing mass and cost of the generator, and can make the construction easy in manufacturing and handling. Analytical designs are verified using 3D finite-element simulations and several designs are evaluated to find the optimum design for a 7.5 MW, 12 rpm wind turbine application. It is shown, that the required torque can be achieved with an outer diameter of 7.5 m, and with a mass of 172 t (including the structural mass). The proposed generator is compared with commercial direct-drive generators, and it is found that the proposed generator has the highest torque density with 34.7 kNm/t.