Health-aware model predictive control of wind turbines using fatigue prognosis

This is the peer reviewed version of the following article: Sánchez, H. E., Escobet, T., Puig, V., Fogh, P. Health-aware model predictive control of wind turbines using fatigue prognosis. "International journal of adaptive control and signal processing", 1 Abril 2018, vol. 32, núm. 4, p. 614-627, which has been published in final form at https://onlinelibrary.wiley.com/doi/abs/10.1002/acs.2784. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived VersionsWind turbine components are subject to considerable fatigue because of extreme environmental conditions to which they are exposed, especially those located offshore. Wind turbine blades are under significant gravitational, inertial, and aerodynamic loads, which cause their fatigue and degradation during the wind turbine operational life. A fatigue problem is often present at the blade root because of the considerable bending moments applied to this zone. Interest in the integration of control with fatigue load minimization has increased in recent years. This paper investigates the fatigue assessment using a rainflow counting algorithm and the blade root moment information coming from the sensor available in a high-fidelity simulator of a utility-scale wind turbine. Then, the integration of the fatigue-based system health management module with control is proposed. This provides a mechanism for the wind turbine to operate safely and optimize the trade-off between components' life and energy production. In particular, this paper explores the integration of model predictive control with the fatigue-based prognosis approach to minimize the damage of wind turbine components (the blades). A control-oriented model of the fatigue based on the rainflow counting algorithm is proposed to obtain online information of the blades' accumulated damage that can be integrated with model predictive control. Then, the controller objective function is modified by adding an extra criterion that takes into account the accumulated damage. The scheme is implemented and tested in a well-known wind turbine benchmark.Peer Reviewe

AN OPTIONS APPROACH TO QUANTIFY THE VALUE OF DECISIONS AFTER PROGNOSTIC INDICATION

Safety, mission and infrastructure critical systems have started adopting prognostics and health management, a discipline consisting of technologies and methods to assess the reliability of a product in its actual life-cycle conditions to determine the advent of failure and mitigate system risks. The output from a prognostic system is the remaining useful life of the host system; it gives the decision-maker lead-time and flexibility in maintenance. Examples of flexibility include delaying maintenance actions to use up the remaining useful life and halting the operation of the system to avoid critical failure. Quantifying the value of flexibility enables decision support at the system level, and provides a solution to the fundamental tradeoff in maintenance of systems with prognostics: minimize the remaining useful life thrown while concurrently minimizing the risk of failure. While there are cost-benefit models to quantify the value of implementing prognostics, they are applicable to the fleet level, they do not incorporate the value of decisions after prognostic indication (value of flexibility or contingency actions), and do not use PHM information for dynamic maintenance scheduling. This dissertation develops a decision support model based on `options' theory- a financial derivative tool extended to real assets - to quantify maintenance decisions after a remaining useful life prediction. A hybrid methodology based on Monte Carlo simulations and decision trees is developed. The methodology incorporates the value of contingency actions when assessing the benefits of PHM. The model is extended and combined with least squares Monte Carlo methods to quantify the option to wait to perform maintenance; it represents the value obtained from PHM at the system level. The methodology also allows quantifying the benefits of PHM for individualized maintenance policies for systems in real-time, and to set a dynamic maintenance threshold based on PHM information. This work is the first known to quantify the flexibility enabled by PHM and to address the cost-benefit-risk ramifications after prognostic indication at the system level. The contributions of the dissertation are demonstrated on data for wind farms

Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview

Wind turbines are playing an increasingly important role in renewable power generation. Their complex and large-scale structure, however, and operation in remote locations with harsh environmental conditions and highly variable stochastic loads make fault occurrence inevitable. Early detection and location of faults are vital for maintaining a high degree of availability and reducing maintenance costs. Hence, the deployment of algorithms capable of continuously monitoring and diagnosing potential faults and mitigating their effects before they evolve into failures is crucial. Fault diagnosis and fault tolerant control designs have been the subject of intensive research in the past decades. Significant progress has been made and several methods and control algorithms have been proposed in the literature. This paper provides an overview of the most recent fault diagnosis and fault tolerant control techniques for wind turbines. Following a brief discussion of the typical faults, the most commonly used model-based, data-driven and signal-based approaches are discussed. Passive and active fault tolerant control approaches are also highlighted and relevant publications are discussed. Future development tendencies in fault diagnosis and fault tolerant control of wind turbines are also briefly stated. The paper is written in a tutorial manner to provide a comprehensive overview of this research topic

Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency

Health-aware model predictive control of wind turbines using fatigue prognosis

Wind turbines components are subject to considerable fatigue due to extreme environmental conditions to which are exposed, especially those located offshore. Interest in the integration of control with fatigue load minimization has increased in recent years. The integration of a system health management module with the control provides a mechanism for the wind turbine to operate safely and optimize the trade-off between components life and energy production. The research presented in this paper explores the integration of model predictive control (MPC) with fatigue-based prognosis approach to minimize the damage of wind turbine components (the blades). The controller objective is modified by adding an extra criterion that takes into account the accumulated damage. The scheme is implemented and tested using a high fidelity simulator of a utility scale wind turbine.Peer ReviewedPostprint (author's final draft

Blade-pitch Control for Wind Turbine Load Reductions

Large wind turbines are subjected to the harmful loads that arise from the spatially uneven and temporally unsteady oncoming wind. Such loads are the known sources of fatigue damage that reduce the turbine operational lifetime, ultimately increasing the cost of wind energy to the end users. In recent years, a substantial amount of studies has focused on blade pitch control and the use of real-time wind measurements, with the aim of attenuating the structural loads on the turbine blades and rotor. However, many of the research challenges still remain unsolved. For example, there exist many classes of blade individual pitch control (IPC) techniques but the link between these different but competing IPC strategies was not well investigated. In addition, another example is that many studies employed model predictive control (MPC) for its capability to handle the constraints of the blade pitch actuators and the measurement of the approaching wind, but often, wind turbine control design specifications are provided in frequency-domain that is not well taken into account by the standard MPC. To address the missing links in various classes of the IPCs, this thesis aims to investigate and understand the similarities and differences between each of their performance. The results suggest that the choice of IPC designs rests largely with preferences and implementation simplicity. Based on these insights, a particular class of the IPCs lends itself readily for extracting tower motion from measurements of the blade loads. Thus, this thesis further proposes a tower load reduction control strategy based solely upon the blade load sensors. To tackle the problem of MPC on wind turbines, this thesis presents an MPC layer design upon a pre-determined robust output-feedback controller. The MPC layer handles purely the feed-forward and constraint knowledge, whilst retaining the nominal robustness and frequency-domain properties of the pre-determined closed-loop. Thus, from an industrial perspective, the separate nature of the proposed control structure offers many immediate benefits. Firstly, the MPC control can be implemented without replacing the existing feedback controller. Furthermore, it provides a clear framework to quantify the benefits in the use of advance real-time measurements over the nominal output-feedback strategy

Power System Online Stability Assessment using Synchrophasor Data Mining

Traditional power system stability assessment based on full model computation shows its drawbacks in real-time applications where fast variations are present at both demand side and supply side. This work presents the use of data mining techniques, in particular the Decision Trees (DTs), for fast evaluation of power system oscillatory stability and voltage stability from synchrophasor measurements. A regression tree-based approach is proposed to predict the stability margins. Modal analysis and continuation power flow are the tools used to build the knowledge base for off-line DT training. Corresponding metrics include the damping ratio of critical electromechanical oscillation mode and MW-distance to the voltage instability region. Classification trees are used to group an operating point into predefined stability state based on the value of corresponding stability indicator. A novel methodology for knowledge base creation has been elaborated to assure practical and sufficient training data. Encouraging results are obtained through performance examination. The robustness of the proposed predictor to measurement errors and system topological variations is analyzed. A scheme has been proposed to tackle the problem of when and how to update the data mining tool for seamless online stability monitoring. The optimal placement for the phasor measurement units (PMU) based on the importance of DT variables is suggested. A measurement-based voltage stability index is proposed and evaluated using field PMU measurements. It is later revised to evaluate the impact of wind generation on distribution system voltage stability. Next, a new data mining tool, the Probabilistic Collocation Method (PCM), is presented as a computationally efficient method to conduct the uncertainty analysis. As compared with the traditional Monte Carlo simulation method, the collocation method could provide a quite accurate approximation with fewer simulation runs. Finally, we show how to overcome the disadvantages of mode meters and ringdown analyzers by using DTs to directly map synchrophasor measurements to predefined oscillatory stability states. The proposed measurement-based approach is examined using synthetic data from simulations on IEEE test systems, and PMU measurements collected from field substations. Results indicate that the proposed method complements the traditional model-based approach, enhancing situational awareness of control center operators in real time stability monitoring and control

Prognostics and health aware model predictive control of wind turbines

Wind turbines components are subject to considerable stresses and fatigue due to extreme environmental conditions to which they are exposed, especially those located offshore. Also, the most common faults present in wind turbine components have been investigated for years by the research community and that has led to propose a fault diagnosis and fault tolerant control wind turbine benchmark which include a set of faults that affect the sensors and actuators of several wind turbine components. This thesis presents some contributions to the fields of fault diagnosis, fault-tolerant control, prognostics and its integration with wind turbine control which leads to proposing a control approach called health-aware model predictive control (HAMPC). The contributions are summarized below: - Model-based fault diagnosis: to perform fault detection and isolation interval-based observers together with a set of analytical redundant relations (ARRs) are obtained based on a structural analysis and the fault signature matrix that relates the ARRs with the faults. - Fault tolerant control: it is proposed a fault tolerant control scheme that integrates fault detection and an algorithm for fault accommodation. The scheme has the objective to avoid the increment of blades and tower loads when a fault in the rotor azimuth angle sensor occurs using the individual pitch control technique (IPC). - Wind turbine blades fatigue prognostics and degradation: fatigue is assessed using the rainflow counting algorithm which is used to estimate the accumulated damage and for degradation, it is used a stiffness degradation model of blades material which is used to make predictions of remaining useful life (RUL). - Wind turbines health control: the module for the health of the system based on fatigue damage estimation and RUL predictions is integrated with model predictive control (MPC) leading to the proposed control approach (HAMPC). The contributions presented in this thesis have been validated on a wind turbine study case that uses a 5MW wind turbine reference model implemented in a high fidelity wind turbine simulator (FAST).Els components dels aerogeneradors estan sotmesos a considerable estrès i fatiga, degut a les condicions ambientals extremes a les quals estan exposats, especialment els localitzats en alta mar. Per aquest motiu, al comunitat científica durant els últims anys ha investigat les averies més comunes presents en els aerogeneradors, fet que ha portat a proposar un cas d'estudi de diagnosi i control tolerant de fallades que inclou un conjunt de fallades que afecten a diversos components dels aerogeneradors. Aquesta tesi presenta algunes contribucions en els camps de la diagnosi de fallades, el control tolerant de fallades i la prognosi, així com la seva integració amb el control d'aerogeneradors, fet que ha portat a proposar una tècnica de control anomenada control predictiu basada en models conscients de la salut del sistema (HAMPC). Concretament les aportacions es poden resumir en: - Diagnosi de fallades basada en models: per a la detecció s'utilitzen observadors intervalars i l'aïllament de la fallada es fa en base el conjunt d'ARRs obtinguts de l'anàlisi estructural i de la matriu de signatures de fallades que relaciona les ARRs amb les fallades. - Control tolerant de fallades: es proposa un esquema de control tolerant a fallades que integra la detecció de fallades i algoritme d'acomodació de fallades, i té per objectiu evitar l'augment de càrregues en la pala i la torre quan es produeix una fallada en el sensor azimuth quan es fa un control individual de la inclinació de les pales (IPC). - Prognosi de la fatiga i la degradació de les pales: la fatiga s'avalua amb un algorisme denominat "rainflow counting" amb el qual es fa estimació del dany acumulat i per a la degradació es fa servir un model de degradació de la rigidesa del material amb el qual es fan prediccions de la vida útil restant (RUL). - Control de la salut d'aerogeneradors: s'ha integrat la gestió de la salut del sistema basat en danys per fatiga o prediccions de RUL amb control predictiu basat en models (MPC) donant lloc al control que anomenem HAMPC. Les contribucions presentades en aquesta tesi han sigut validades en un cas d'estudi d'aerogeneradors basat en un aerogenerador de referència de 5MW de potència implementat en el simulador d'aerogeneradors d'alta fidelitat conegut amb el nom de FAST.Postprint (published version