Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian 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

A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance

Offshore wind farms are a rapidly developing source of clean, low-carbon energy and as they continue to grow in scale and capacity, so does the requirement for their efficient and optimised operation and maintenance. Historically, approaches to maintenance have been purely reactive. However, there is a movement in offshore wind, and wider industry in general, towards more proactive, condition-based maintenance approaches which rely on operational data-driven decision making. This paper reviews the current efforts in proactive maintenance strategies, both predictive and prescriptive, of which the latter is an evolution of the former. Both use operational data to determine whether a turbine component will fail in order to provide sufficient warning to carry out necessary maintenance. Prescriptive strategies also provide optimised maintenance actions, incorporating predictions into a wider maintenance plan to address predicted failure modes. Beginning with a summary of common techniques used across both strategies, this review moves on to discuss their respective applications in offshore wind operation and maintenance. This review concludes with suggested areas for future work, underlining the need for models which can be simply incorporated by site operators and integrate live data whilst handling uncertainties. A need for further focus on medium-term planning strategies is also highlighted along with consideration of the question of how to quantify the impact of a proactive maintenance strategy

Early fault detection in the main bearing of wind turbines based on Gated Recurrent Unit (GRU) neural networks and SCADA data

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksFailures in the main bearings of wind turbines are critical in terms of downtime and replacement cost. Early diagnosis of their faults would lower the levelized cost of wind energy. Thus, this work discusses a gated recurrent unit (GRU) neural network, which detects faults in the main bearing some months ahead (when the event that initiates/develops the failure releases heat) the actual fatal fault materializes. GRUs feature internal gates that govern information flow and are utilized in this study for their capacity to understand whether data in a time series is crucial enough to preserve or forget. It is noteworthy that the proposed methodology only requires healthy supervisory control and data acquisition (SCADA) data. Thus, it can be deployed to old wind parks (nearing the end of their lifespan) where specific high-frequency condition monitoring sensors are not installed and to new wind parks where faulty historical data do not exist yet. The strategy is trained, validated, and finally tested using SCADA data from an in-production wind park composed of nine wind turbines.Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energiaObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.3 - Per a 2030, duplicar la taxa mundial de millora de l’eficiència energèticaPostprint (author's final draft

Predictive maintenance of wind turbine’s main bearing using wind farm SCADA data and LSTM neural networks

Field failures of wind turbine main bearings cause unwanted downtime and significant maintenance costs. Currently, this industry seeks to increase its reliability, for which condition monitoring and predictive maintenance systems have been adopted. In most industrial wind farms, the integrated Supervisory Control and Data Acquisition (SCADA) system provides data that is stored averaged every 10 minutes that can be used to quantify the health of a wind turbine (WT). This research presents a framework for the analysis of data collected from the SCADA system of an operating wind farm, aiming to early detect the main bearing failure using a Long-Short-Term Memory (LSTM) neural network. For prediction, SCADA variables of the temperature of turbine components near the main bearing, rotor speed, ambient temperature, and generated power are taken into account. The results show that the proposed methodology can detect the target failure up to 4 months in advance of the fatal breakdown. The results obtained confirm the applicability of the proposed model in real scenarios that can help the operator with enough time to make more informed maintenance decisions.Peer ReviewedPostprint (published version

Using SCADA data for wind turbine condition monitoring - a review

The ever increasing size of wind turbines and the move to build them offshore have accelerated the need for optimised maintenance strategies in order to reduce operating costs. Predictive maintenance requires detailed information on the condition of turbines. Due to the high costs of dedicated condition monitoring systems based on mainly vibration measurements, the use of data from the turbine Supervisory Control And Data Acquisition (SCADA) system is appealing. This review discusses recent research using SCADA data for failure detection and condition monitoring, focussing on approaches which have already proved their ability to detect anomalies in data from real turbines. Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems. Potential for future research on the use of SCADA data for advanced turbine condition monitoring is discussed

A hybrid prognostic methodology for tidal turbine gearboxes

Tidal energy is one of promising solutions for reducing greenhouse gas emissions and it is estimated that 100 TWh of electricity could be produced every year from suitable sites around the world. Although premature gearbox failures have plagued the wind turbine industry, and considerable research efforts continue to address this challenge, tidal turbine gearboxes are expected to experience higher mechanical failure rates given they will experience higher torque and thrust forces. In order to minimize the maintenance cost and prevent unexpected failures there exists a fundamental need for prognostic tools that can reliably estimate the current health and predict the future condition of the gearbox.This paper presents a life assessment methodology for tidal turbine gearboxes which was developed with synthetic data generated using a blade element momentum theory (BEMT) model. The latter has been used extensively for performance and load modelling of tidal turbines. The prognostic model developed was validated using experimental data

Online learning of windmill time series using Long Short-term Cognitive Networks

Forecasting windmill time series is often the basis of other processes such as anomaly detection, health monitoring, or maintenance scheduling. The amount of data generated on windmill farms makes online learning the most viable strategy to follow. Such settings require retraining the model each time a new batch of data is available. However, update the model with the new information is often very expensive to perform using traditional Recurrent Neural Networks (RNNs). In this paper, we use Long Short-term Cognitive Networks (LSTCNs) to forecast windmill time series in online settings. These recently introduced neural systems consist of chained Short-term Cognitive Network blocks, each processing a temporal data chunk. The learning algorithm of these blocks is based on a very fast, deterministic learning rule that makes LSTCNs suitable for online learning tasks. The numerical simulations using a case study with four windmills showed that our approach reported the lowest forecasting errors with respect to a simple RNN, a Long Short-term Memory, a Gated Recurrent Unit, and a Hidden Markov Model. What is perhaps more important is that the LSTCN approach is significantly faster than these state-of-the-art models