Adaptive Signal Processing Strategy for a Wind Farm System Fault Accommodation

In order to improve the availability of offshore wind farms, thus avoiding unplanned operation and maintenance costs, which can be high for offshore installations, the accommodation of faults in their earlier occurrence is fundamental. This paper addresses the design of an active fault tolerant control scheme that is applied to a wind park benchmark of nine wind turbines, based on their nonlinear models, as well as the wind and interactions between the wind turbines in the wind farm. Note that, due to the structure of the system and its control strategy, it can be considered as a fault tolerant cooperative control problem of an autonomous plant. The controller accommodation scheme provides the on-line estimate of the fault signals generated by nonlinear filters exploiting the nonlinear geometric approach to obtain estimates decoupled from both model uncertainty and the interactions among the turbines. This paper proposes also a data-driven approach to provide these disturbance terms in analytical forms, which are subsequently used for designing the nonlinear filters for fault estimation. This feature of the work, followed by the simpler solution relying on a data-driven approach, can represent the key point when on-line implementations are considered for a viable application of the proposed scheme

Robust control examples applied to a wind turbine simulated 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 modeling and control become challenging problems. On the one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behavior. Therefore, the development of modeling 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 the development of robust control strategies when applied to a simulated wind turbine plant. Extended simulations with the wind turbine benchmark 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.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 modeling and control become challenging problems. On the one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behavior. Therefore, the development of modeling 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 the development of robust control strategies when applied to a simulated wind turbine plant. Extended simulations with the wind turbine benchmark 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

Wind turbine simulator fault diagnosis via fuzzy modelling and identification techniques

For improving the safety and the reliability of wind turbine installations, the earliest and fastest fault detection and isolation are highly required, since it could be used also for accommodation purpose. Modern wind turbines consist of several important subsystems, which can be affected by malfunctions regarding actuators, sensors, and components. From the turbine control point-of-view they are extremely important since provide the actuation signals, the main functions, as well as the measurements. In this paper, a fault diagnosis scheme based on the identification of fuzzy models is described, in order to detect and isolate these faults in the most efficient way, in order also to improve the energy cost, the production rate, and reduce the operation and maintenance operations. Fuzzy systems are proposed here since the model under investigation is nonlinear, whilst the wind speed measurement is uncertain since it depends on the rotor plane wind turbulence effects. These fuzzy models are described as Takagi-Sugeno prototypes, whose parameters are estimated from the wind turbine measurements. The fault diagnosis methodology is thus developed using these fuzzy models, which are exploited as residual generators. The wind turbine simulator is finally employed for the validation of the obtained performances.For improving the safety and the reliability of wind turbine installations, the earliest and fastest fault detection and isolation is highly required, since it could be used also for accommodation purpose. Modern wind turbines consist of several important subsystems, which can be affected by malfunctions regarding actuators, sensors, and components. From the turbine control point–of–view they are extremely important since provide the actuation signals, the main functions, as well as the measurements. In this paper, a fault diagnosis scheme based on the identification of fuzzy models is described, in order to detect and isolated these faults in the most efficient way, in order also to improve the energy cost, the production rate, and reduce the operation and maintenance operations. Fuzzy systems are proposed here since the model under investigation is nonlinear, whilst the wind speed measurement is uncertain since it depends on the rotor plane wind turbulence effects. These fuzzy models are described as Takagi–Sugeno prototypes, whose parameters are estimated from the wind turbine measurements. The fault diagnosis methodology is thus developed using these fuzzy models, which are exploited as residual generators. The wind turbine simulator is finally employed for the validation of the obtained performances

Data-driven fault diagnosis of awind farm benchmark model

The fault diagnosis of wind farms has been proven to be a challenging task, and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances.The fault diagnosis of wind farms has been proven to be a challenging task, and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances

Data-driven techniques for the fault diagnosis of a wind turbine benchmark

This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances

Actuator Fault Reconstruction via Dynamic Neural Networks for an Autonomous Underwater Vehicle Model

This paper proposes the development of a scheme for the fault diagnosis of the actuators of a simulated model accurately representing the behaviour of an autonomous underwater vehicle. The Fossen model usually adopted to describe the dynamics of the underwater vehicle has been generalised in this paper to take into account time-varying sea currents. The proposed fault detection and isolation strategy uses a data-driven approach relying on multi-layer perceptron neural networks that include auto-regressive exogenous prototypes that provide the fault reconstruction. These tools are thus exploited to design a bank of dynamic neural networks for residual generation that are trained on the basis of the input and outputmeasurements acquired from the simulator. In this work, the residuals are designed to represent the reconstruction of the fault signals themselves. Moreover, the neural network bank is also able to perform the isolation task, in case of simultaneous and concurrent faults affecting the actuators. The paper firstly describes the steps performed for deriving the proposed fault diagnosis solution. Secondly, the effectiveness of the scheme is demonstrated by means of high-fidelity simulations of a realistic autonomous underwater vehicle, in the presence of faults and marine current

On characterisations of the input to state stability properties for conformable fractional order bilinear systems

This paper proposes for the first time the theoretical requirements that a fractional-order bilinear system with conformable derivative has to fulfil in order to satisfy different input-to-state stability (ISS) properties. Variants of ISS, namely ISS itself, integral ISS, exponential integral ISS, small-gain ISS, and strong integral ISS for the general class of conformable fractional-order bilinear systems are investigated providing a set of necessary and sufficient conditions for their existence and then compared. Finally, the correctness of the obtained theoretical results is verified by numerical example

Soil is a net source of methane in tropical African forests

Research Highlights: Monitoring of soil CH4 fluxes in African tropical forest conducted run for almost two years, contributing to the scant information on greenhouse gas (GHG) fluxes from forests available from this region. Data showed that the forest soil acted as a net yearly source of CH4. Hotspots of CH4 emissions were measured both in upland and lowland areas of the forest, and on an annual basis they overcame the soil CH4 sink during drier periods or in well‐drained areas. Background and Objectives: Atmospheric studies indicate that tropics are a strong CH4 source. Regional budgets attribute the majority of this source to wetland ecosystems and flooded lowland forests, whereas un‐flooded forests are considered net CH4 sinks, although few studies in tropical forests, in particular in Africa, are available. The present work aims to contribute to this knowledge gap. Materials and Methods: Monitoring campaigns were conducted along the year in the tropical forest of the Ankasa National Park, Ghana, in two contrasting environments, uphill and downhill, using close static chambers coupled with gas chromatography. Results: The uphill area was a net weak CH4 sink with mean daily fluxes ranging from −1.29 to 0.44 mg CH4 m−2 d−1. The downhill area was a significant CH4 source with mean daily fluxes ranging from −0.67 to 188.09 mg CH4 m−2 d−1 and with peaks up to 1312 mg CH4 m−2 d−1 in the wet season. Conclusions: The net annual soil CH4 budget for the Ankasa Park, normalizing the proportion of downhill areas over the whole park surface, was a source of about 3.3 kg CH4 ha−1 yr−1. Overlooking such areas might lead to underestimates of the total CH4 source strength of forested areas

Mode Confinement in Photonic Quasi-Crystal Point-Defect Cavities for Particle Accelerators

In this Letter, we present a study of the confinement properties of point-defect resonators in finite-size photonic-bandgap structures composed of aperiodic arrangements of dielectric rods, with special emphasis on their use for the design of cavities for particle accelerators. Specifically, for representative geometries, we study the properties of the fundamental mode (as a function of the filling fraction, structure size, and losses) via 2-D and 3-D full-wave numerical simulations, as well as microwave measurements at room temperature. Results indicate that, for reduced-size structures, aperiodic geometries exhibit superior confinement properties by comparison with periodic ones.Comment: 4 pages, 4 figures, accepted for publication in Applied Physics Letter