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

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

Review of magnetic gear technologies and their applications in marine energy

The marine energy industry is in its early stages but has a large potential for growth. One of the most significant challenges is the reduction of operation and maintenance costs. Magnetic gears (MGs) offer the potential for long periods between maintenance intervals due to their frictionless torque transmission which could reduce these costs. This study presents a summary of the state of the art in MG technology and then investigates its potential for marine energy applications. A brief overview is given of the state of the marine energy industry and the environment in which marine energy converters (MECs) operate. A short history of MG development over the past century is then presented followed by a discussion of the leading MG technologies and their relative advantages. In order to demonstrate the potential of MGs in marine applications, the current technologies, i.e. mechanically geared and direct drive machines, are examined in terms of sizing, reliability and economic value using previous studies on a similar technology, namely wind. MGs are applied to four types of MECs to demonstrate how the technology can be incorporated. The potential to deploy at scale and potential obstacles to this are then discussed

Cross magnetisation effects in electrical machines

Examples of cross-magnetisation effects in electrical machines are collated and the physical characteristics of the phenomena are reviewed qualitatively. New experimental results to clarify the phenomena are introduced. This subject has also been called a cross-axis, cross-magnetisation, cross-saturation, intersaturation or cross-reactance effect. Cross-magnetisation encompasses the magnetic behaviour of saturable machine materials when the vector of the main magnetomotive force (MMF) is applied in a direction that is not geometrically or analytically favoured in the magnetic structure of the machine. The phenomenon is important because it controls the magnetic behaviour of components that are closest to the highest field strengths in operating machines. A number of cases in electrical machines where this can occur are considered and conclusions drawn. These can be used to allow the cross-magnetisation effects from field calculations of electrical machines to be quantitatively represented and qualitatively explained, particularly for machines of unusual topology, in which key components are not subjected to a magnetic field in the preferred direction

Review of condition monitoring of rotating electrical machines

Condition monitoring of rotating electrical machinery has received intense research interest for more than 30 years. However, electrical machinery has been considered reliable and the application of fast-acting digital electrical protection has rather reduced the attention operators pay to the equipment. The area based upon current literature and the author's experience is reviewed. There are three restrictions: only on-line techniques for rotating machines are dealt with; specific problems of variable speed drives are not dealt with, except in passing; conventional rather than emerging brushless, reluctance and permanent magnet machines of unusual topology are concentrated upon. The art of condition monitoring is minimalist, to take minimum measurements from a machine necessary to extract a diagnosis, so that a condition can be rapidly inferred, giving a clear indication of incipient failure modes. The current state of the art is reviewed in the following ways: survey developments in condition monitoring of machines, mechanically and electrically, over the last 30 years; put that work in context alongside the known failure mechanisms; review those developments which have proved successful and identify areas of research which require attention in the future to advance the subject