Wind turbine condition monitoring : technical and commercial challenges.

Deployment of larger scale wind turbine systems, particularly offshore, requires more organized operation and maintenance strategies to ensure systems are safe, profitable and cost-effective. Among existing maintenance strategies, reliability centred maintenance is regarded as best for offshore wind turbines, delivering corrective and proactive (i.e. preventive and predictive) maintenance techniques enabling wind turbines to achieve high availability and low cost of energy. Reliability centred maintenance analysis may demonstrate that an accurate and reliable condition monitoring system is one method to increase availability and decrease the cost of energy from wind. In recent years, efforts have been made to develop efficient and cost-effective condition monitoring techniques for wind turbines. A number of commercial wind turbine monitoring systems are available in the market, most based on existing techniques from other rotating machine industries. Other wind turbine condition monitoring reviews have been published but have not addressed the technical and commercial challenges, in particular, reliability and value for money. The purpose of this paper is to fill this gap and present the wind industry with a detailed analysis of the current practical challenges with existing wind turbine condition monitoring technology

Studies in Electrical Machines & Wind Turbines associated with developing Reliable Power Generation

The publications listed in date order in this document are offered for the Degree of Doctor of Science in Durham University and have been selected from the author’s full publication list. The papers in this thesis constitute a continuum of original work in fundamental and applied electrical science, spanning 30 years, deployed on real industrial problems, making a significant contribution to conventional and renewable energy power generation. This is the basis of a claim of high distinction, constituting an original and substantial contribution to engineering science

Automated Wind Turbine Pitch Fault Prognosis using ANFIS

Many current wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. WT Supervisory Control and Data Acquisition (SCADA) systems contain alarms and signals that provide significant important information. A possible WT fault can be detected through a rigorous analysis of the SCADA data. This paper proposes a new method for analysing WT SCADA data by using Adaptive Neuro-Fuzzy Inference System (ANFIS) with the aim to achieve automated detection of significant pitch faults. Two existing statistical analysis approaches were applied to detect common pitch fault symptoms. Based on the findings, an ANFIS Diagnosis Procedure was proposed and trained. The trained system was then applied in a wind farm containing 26 WTs to show its prognosis ability for pitch faults. The result was compared to a SCADA Alarms approach and the comparison has demonstrated that the ANFIS approach gives prognostic warning of pitch faults ahead of pitch alarms. Finally, a Confusion Matrix analysis was made to show the accuracy of the proposed approach

Structural health monitoring for wind turbine foundations

The construction of onshore wind turbines has rapidly been increasing as the UK attempts to meet its renewable energy targets. As the UK’s future energy depends more on wind farms, safety and security are critical to the success of this renewable energy source. Structural integrity of the tower and its components is a critical element of this security of supply. With the stochastic nature of the load regime a bespoke low cost structural health monitoring system is required to monitor integrity of the concrete foundation supporting the tower. This paper presents an assessment of ‘embedded can’ style foundation failure modes in large onshore wind turbines and proposes a novel condition based monitoring solution to aid in early warning of failure. The most common failure modes are discussed and a low-cost remote monitoring system is presented

L'innovation centrée usagers dans la cité par projets : ethnographie de l'appropriation d'une consigne plurivoque dans le secteur numérique : le cas du programme PACA Labs

Based on the longitudinal study of four projects, this thesis examine the impact of user-centered innovation development by analyzing the collective trajectories compelled by the instruction − common but ambiguous − that invites the project teams to involve “real users” in the innovation process. By referring on intersected contributions in sociology of innovation and sociology of the uses of ICTs, I explore more specifically the strategies of enrolment, the protocols for interact with users and the different ways to categorize the users that the actors choose to describe the innovation process and try to control it. Focused on the development of socio-marketable products and services, these projects place on users various purposes and matters of concern that are unevenly distributed in the project teams. Beyond the specific proofs in each trajectory, this thesis aims to challenge the possibility of developing an innovation more “participative” in the short-term world of the Projects-oriented Cité.Structurée autour de l’étude longitudinale de quatre projets, cette thèse questionne la portée de l’innovation centrée usagers en analysant, dans la diversité de leur planification et de leurs épreuves, les trajectoires collectives initiées par la consigne − commune mais labile − invitant des équipes-projet à impliquer des usagers « réels » dans le processus d’innovation. En mobilisant les apports croisés de la sociologie de l’innovation et de la sociologie des usages, elle explore plus spécifiquement les conditions d’élaboration des stratégies d’intéressement convoquées par cette consigne, les dispositifs de confrontation aux situations d’usage échafaudés ainsi que les principes de catégorisation des usagers que les acteurs mobilisent pour décrire le processus d’innovation à l’oeuvre et tenter de l’infléchir. En visant le développement d’innovations commer-so-cialisables, ces projets investissent dans les usagers qu’ils convoquent des préoccupations, des aspirations et des contraintes inégalement distribuées au sein des équipes-projet. Au-delà des épreuves propres à chaque histoire partenariale, cette thèse propose de questionner les conditions de réalisation d’une innovation plus « participative » dans le monde connexionniste et court-termiste de la Cité par projets

Investigation of Damper Valve Dynamics Using Parametric Numerical Methods

The objectives of this study are to identify the dynamics of a Tenneco Automotive hydraulic damper valve and to predict valve performance. Accurate simulations of damper valve performance can be used to improve valve designs without the expense of physical testing. The Tenneco damper valve consists of thin shims and a spring preloaded disc that restricts fluid from exiting the main flow orifices. The deflection of the shims and spring are dependent on the flow-rate through the valve. The pressure distribution acting on the deformable valve components is investigated numerically using a dynamic modelling technique. This technique involves sequential geometry and simulation updating, while varying both the geometry and flow-rate. The valve deflection is calculated by post-processing the pressure distribution. Valve performance can be predicted by coupling the valve deflection with CFD pressure results

Cost-effective condition monitoring for wind turbines

Cost-effective wind turbine (WT) condition monitoring assumes more importance as turbine sizes increase and they are placed in more remote locations, for example, offshore. Conventional condition monitoring techniques, such as vibration, lubrication oil, and generator current signal analysis, require the deployment of a variety of sensors and computationally intensive analysis techniques. This paper describes a WT condition monitoring technique that uses the generator output power and rotational speed to derive a fault detection signal. The detection algorithm uses a continuous-wavelet-transform-based adaptive filter to track the energy in the prescribed time-varying fault-related frequency bands in the power signal. The central frequency of the filter is controlled by the generator speed, and the filter bandwidth is adapted to the speed fluctuation. Using this technique, fault features can be extracted, with low calculation times, from direct- or indirect-drive fixed- or variable-speed WTs. The proposed technique has been validated experimentally on a WT drive train test rig. A synchronous or induction generator was successively installed on the test rig, and both mechanical and electrical fault like perturbations were successfully detected when applied to the test rig

Wind turbine SCADA alarm pattern recognition

Current wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when they are operated offshore. WT Supervisory Control and Data Acquisition (SCADA) systems contain alarm signals providing significant important information. Pattern recognition embodies a set of promising techniques for intelligently processing WT SCADA alarms. This paper presents the feasibility study of SCADA alarm processing and diagnosis method using an artificial neural network (ANN). The back-propagation network (BPN) algorithm was used to supervise a three layers network to identify a WT pitch system fault, known to be of high importance, from pitch system alarm. The trained ANN was then applied on another 4 WTs to find similar pitch system faults. Based on this study, we have found the general mapping capability of the ANN help to identify those most likely WT faults from SCADA alarm signals, but a wide range of representative alarm patterns are necessary for supervisory training

Bayesian Network for Wind Turbine Fault Diagnosis

Wind turbine reliability studies have become more important because good wind turbine reliability with predictable turbine maintenance schedule will reduce the cost of energy and determine the success of a wind farm project. Previous research on wind turbine SCADA system has made progress in this respect. However, SCADA data volume is usually too huge and alarm information is too unclear to indicate failure root causes. In addition, SCADA signals and alarms are not currently interpreted as a whole. This highlights the need for more intelligent methods which can use existing SCADA data to automatically provide accurate WT failure diagnosis. This paper presents a new approach, based on Bayesian Network, to describe the relationship between wind turbine failure root causes and symptoms. The Bayesian Network model was derived from an existing probability-based analysis method – the Venn diagram, and based upon 26 months of historical SCADA data. The Bayesian Network reasoning results have shown that the Bayesian Network is a valuable tool for WT fault diagnosis and has great potential to rationalise failure root causes