Robust Control Applications to a Wind Turbine-Simulated System

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this chapter is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Experiments with the wind turbine simulator represent the instruments for assessing the main aspects of the developed control methodologies

Fault Diagnosis Techniques for a Wind Turbine System

The fault diagnosis and prognosis of wind turbine systems represent a challenging issue, thus justifying the research topics developed in this work with application to safety-critical systems. Therefore, this chapter addresses these research issues and demonstrates viable techniques of fault diagnosis and condition monitoring. To this aim, the design of the so-called fault detector relies on its estimate, which involves data-driven methods, as they result effective methods for managing partial information of the system dynamics, together with errors, model-reality mismatch and disturbance effects. In particular, the considered data-driven strategies use fuzzy systems and neural networks, which are employed to establish non-linear dynamic links between measurements and faults. The selected prototypes are based on non-linear autoregressive with exogenous input descriptions, since they are able to approximate non-linear dynamic functions with arbitrary degree of accuracy. The capabilities of the designed fault diagnosis schemes are verified via a high-fidelity simulator, which describes the normal and the faulty behaviour of a wind turbine plant. Finally, the robustness and the reliability features of the proposed methods are validated in the presence of uncertainty and disturbance implemented in the wind turbine simulator

Avionic air data sensors fault detection and isolation by means of singular perturbation and geometric approach

Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Perturbations leads to an innovative Fault Detection and Isolation system dedicated to the isolation of faults affecting the air data system of a general aviation aircraft. The isolation capabilities, obtained by means of the approach proposed in this work, allow for the solution of a fault isolation problem otherwise not solvable by means of standard geometric techniques. Extensive Monte-Carlo simulations, exploiting a high fidelity aircraft simulator, show the effectiveness of the proposed Fault Detection and Isolation system.Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Perturbations leads to an innovative Fault Detection and Isolation system dedicated to the isolation of faults affecting the air data system of a general aviation aircraft. The isolation capabilities, obtained by means of the approach proposed in this work, allow for the solution of a fault isolation problem otherwise not solvable by means of standard geometric techniques. Extensive Monte-Carlo simulations, exploiting a high fidelity aircraft simulator, show the effectiveness of the proposed Fault Detection and Isolation system

Data–Driven Wake Steering Control for a Simulated Wind Farm Model

Upstream wind turbines yaw to divert their wakes away from downstream turbines, increasing the power produced. Nevertheless, the majority of wake steering techniques rely on offline lookup tables that translate a set of parameters, including wind speed and direction, to yaw angles for each turbine in a farm. These charts assume that every turbine is working well, however they may not be very accurate if one or more turbines are not producing their rated power due to low wind speed, malfunctions, scheduled maintenance, or emergency maintenance. This study provides an intelligent wake steering technique that, when calculating yaw angles, responds to the actual operating conditions of the turbine. A neural network is trained live to determine yaw angles from operating conditions, including turbine status, using a hybrid model and a learning-based method, i.e. an active control. The proposed control solution does not need to solve optimization problems for each combination of the turbines’ non-optimal working conditions in a farm; instead, the integration of learning strategy in the control design enables the creation of an active control scheme, in contrast to purely model-based approaches that use lookup tables provided by the wind turbine manufacturer or generated offline. The suggested methodology does not necessitate a substantial amount of training samples, unlike purely learning-based approaches like model-free reinforcement learning. In actuality, by taking use of the model during back propagation, the suggested approach learns more from each sample. Based on the flow redirection and induction in the steady state code, results are reported for both normal (nominal) wake steering with all turbines operating as well as defective conditions. It is a free tool for optimizing wind farms that The National Renewable Energy Laboratory (USA) offers. These yaw angles are contrasted and checked with those discovered through the resolution of an optimization issue. Active wake steering is made possible by the suggested solution, which employs a hybrid model and learning-based methodology, through sample efficient training and quick online evaluation. Finally, a hardware-in-the-loop test-bed is taken into consideration for assessing and confirming the performance of the suggested solutions in a more practical setting

Data-Driven and Model-Based Control Techniques for a Wind Turbine Benchmark Model

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high–fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Extended simulations with the wind turbine benchhmark model and the Monte–Carlo tool represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model–reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics

Stochastic user equilibrium in the presence of state dependence

We consider the following two state-dependent effects at the level of route choice: inertia to change and, as a consequence of experience, lower perception variance for the currently used route. A heteroscedastic extreme value model embodying heterogeneity across alternatives in the mean of the random terms is used. Estimations based on stated preference data confirm the presence of both state-dependent effects. We introduce a new class of stochastic user equilibrium (SUE) models that take state-dependent effects into account. The class includes conventional SUE as special case. The equilibrium conditions are formulated as fixed-point states of deterministic day-to-day assignment processes. At the equilibrium (1) no user can improve her/his utility by unilaterally changing route, and (2) if each user shifts from her/his current route to her/his newly chosen route the observed route flows do not change. The existence of the equilibrium is guaranteed under usually satisfied conditions. A modified method of successive averages is proposed for solution. Examples related to a two arc network and to the Nguyen-Dupuis network illustrate the model

Reference-Dependent Stochastic User Equilibrium with Endogenous Reference Points

We consider the application of reference-dependent consumer choice theory to traffic assignment on transportation networks. Route choice is modelled based on random utility maximisation with systematic utility embodying loss aversion for the travel time and money expenditure attributes. Stochastic user equilibrium models found in the literature have considered exogenously given reference points. The paper proposes a model where reference points are determined consistently with the equilibrium flows and travel times. The referencedependent stochastic user equilibrium (RDSUE) is defined as the condition where (i) no user can improve her utility by unilaterally changing path, (ii) each user has as reference point the current travel time and the money expenditure of one of the available paths, and (iii) if each user updates the reference point to her current path the observed path flows do not change. These conditions are formally equivalent to a multi-class stochastic equilibrium where each class is associated with a path and has as reference point the current state on the path, and the number of users in each class equals the current flow on the path. The RDSUE is formulated as a fixed point problem in the path flows. Existence of RDSUE is guaranteed under usual assumptions. A heuristic algorithm based on the method of successive averages is proposed to solve the problem. The model is illustrated by two numerical examples, one relates to a two-link network and another to the Nguyen-Dupuit network. A reference-dependent route choice model calibrated on stated preference data is used. The second example serves also to demonstrate the algorithm. The impact on the equilibrium of different assumptions on the degree of loss aversion with respect to the travel time attribute are investigated

The role of stereotactic body radiation therapy in oligometastatic colorectal cancer

Rationale: Regorafenib is the new standard third-line therapy in metastatic colorectal cancer (mCRC). However, the reported 1-year overall survival rate does not exceed 25%. Patient concerns: A 55-year-old man affected by mCRC, treated with regorafenib combined with stereotactic body radiotherapy (SBRT), showing a durable response. Interventions: After 6 months of regorafenib, a PET/CT scan revealed a focal uptake in a solid lung nodule which was treated with SBRT, whereas continuing regorafenib administration. Fourteen months later, the patient had further progression in a parasternal lymph node, but treatment with regorafenib was continued. The regorafenib-associated side effects, such us the hand-foot syndrome, were favorable managed by reducing the dose from 160 to 120 mg/day. Outcomes: Patient-reported outcome was characterized by a progression-free survival of approximately 3 years. Lessons: in presence of oligometastatic progression, a local SBRT while retaining the same systemic therapy may be a better multidisciplinary approach. Moreover, disease progression is no longer an absolute contraindication for continuing the regorafenib treatment

Stand structure and influence of climate on growth trends of a marginal forest population of Pinus nigra spp. nigra

The Black pine of Villetta Barrea (Pinus nigra ssp. nigra var italica) is a variety of the nigra subspecies. It is naturally distributed only in the Abruzzo Region, near the village of Villetta Barrea, with a rear-edge marginal population.  A dendrochronological sampling of the population was implemented with the aim of studying its stand structure and the most probable interactions between growth trends and climate. Mensurational data were used to characterize the stand and, furthermore, the general correlation function (CF) and the moving correlation function (MCF), with a 30 years window, were used to assess the interrelation between the growth of the tree rings and the climate.The results indicated that the past forest management, mainly carried out with thinnings from below and selective cuttings, influenced the current structure of the forest (mean diameter) but no differences in growth trends were detected within the population.The survey on Villetta Barrea Black pine showed a positive and statistically significant correlation between the ring-width and the average temperatures of the months of December (before the ring formation - t-1), February and March; but it also showed a negative correlation with the temperatures of July, September and October of the current year (t).Moreover, the analysis with moving correlation functions suggested that, in the last decades, the population has negatively reacted to very few climate factors and, in particular, to the changes in temperatures (both minimum and maximum temperatures). This is especially true for the shifts occurred in September, the year of the ring formation.</p

Spectral-domain method of moments for the modal analysis of line waveguides

A rigorous full-wave modal analysis based on the method of moments in the spectral domain is presented for line waveguides constituted by two-part impedance planes with arbitrary anisotropic surface impedances. An integral equation is formulated by introducing an auxiliary current sheet on one of the two half planes and extending the impedance boundary condition of the complementary half plane to hold on the entire plane. The equation is then discretized with the method of moments in the spectral domain, by employing exponentially weighted Laguerre polynomials as entire-domain basis functions and performing a Galerkin testing. Numerical results for both bound and leaky line waves are presented and validated against independent results, obtained for isotropic surface impedances with the analytical Sommerfeld-Maliuzhinets method and for the general anisotropic case with a commercial electromagnetic simulator. The proposed approach is computationally efficient, can accommodate the presence of spatial dispersion, and offers physical insight into the modal propagation regimes.Comment: 10 pages, 13 figure