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When training models for visual anomaly detection, typically, a dataset is collected and then annotated offline. Even if collecting raw data is relatively cheap, annotations are expensive, especially if they require human expertise. We therefore propose a novel interactive learning framework that combines active learning with natural language interaction to minimise the amount of annotated training data and allow for refined human expert feedback that may be leveraged in the learning pro- cess. In our initial experiments on wind turbine drone images, we demonstrate the effectiveness of active learning for anomaly detection when using ground truth la- bels, and assess the impact on learning when collecting labels from ‘experts’ versus ‘non-experts’ using our dialogue system. In addition to anomaly labels with confi- dence scores, we collect and analyse natural language explanations, which may be used to improve both anomaly detection performance and explainability.
2024 The 14th International Workshop on Spoken Dialogue Systems Technology, Sapporo, Japan
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Traditional predictive simulations and remote sensing techniques for forecasting floods are based on
fixed and spatially restricted physics-based models.
These models are computationally expensive and
can take many hours to run, resulting in predictions made based on outdated data. They are also
spatially fixed, and unable to scale to unknown
areas. By modelling the task as an image segmentation problem, an alternative approach using artificial intelligence to approximate the parameters
of a physics-based model in 2D is demonstrated,
enabling rapid predictions to be made in real-time.
2024 Northern Lights Deep Learning Conference, Tromso, Norway
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Wind power is a key pillar in efforts to decarbonise energy production. However, variability in wind speed and resultant wind turbine power generation poses a challenge for power grid integration. Digital Twin (DT) technology provides intelligent service systems, combining real-time monitoring, predictive capabilities and communication technologies. Current DT research for wind turbine power generation has focused on providing wind speed and power generation predictions reliant on Supervisory Control and Data Acquisition (SCADA) sensors, with predictions often limited to the timeframe of datasets. This research looks to expand on this, utilising a novel framework for an intelligent DT system powered by k-Nearest Neighbour (kNN) regression models to upscale live wind speed forecasts to higher wind turbine hub-height and then forecast power generation. As there is no live link to a wind turbine, the framework is referred to as a “Simulated Digital Twin” (SimTwin). 2019-2020 SCADA and wind speed data are used to evaluate this, demonstrating that the method provides suitable predictions. Furthermore, full deployment of the SimTwin framework is demonstrated using live wind speed forecasts. This may prove useful for operators by reducing reliance on SCADA systems and provides a research and development tool where live data is limited.
2023 The 6th International Conference on Renewable Energy and Environment Engineering (REEE 2023)
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Traditional approaches to flood modelling mostly rely on hydrodynamic physical simulations. While these simulations can be accurate, they are computationally expensive and prohibitively so when thinking about real-time prediction based on dynamic environmental conditions.
Alternatively, social media platforms such as Twitter are often used by people to communicate during a flooding event, but discovering which tweets hold useful information is the key challenge in extracting information from posts in real time.
In this article, we present a novel model for flood forecasting and monitoring that makes use of a transformer network that assesses the severity of a flooding situation based on sentiment analysis of the multimodal inputs (text and images). We also present an experimental comparison of a range of state-of-the-art deep learning methods for image processing and natural language processing. Finally, we demonstrate that information induced from tweets can be used effectively to visualise fine-grained geographical flood-related information dynamically and in real-time.
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Wind energy’s ability to liberate the world from conventional sources of energy relies on lowering the significant costs associated with the maintenance of wind turbines. Since icing events on turbine rotor blades are a leading cause of operational failures, identifying icing in advance is critical. Some recent studies have utilized deep learning (DL) techniques to predict icing events with high accuracy by leveraging rotor blade images, but these studies only focus on specific wind parks and fail to generalize to unseen scenarios (e.g., new rotor blade designs). In this paper, we aim to facilitate ice prediction on the face of lack of ice images in new wind parks. We propose the utilization of synthetic data augmentation via a generative artificial intelligence technique—the neural style transfer algorithm to improve the generalization of existing ice prediction models. We also compare the proposed technique with the CycleGAN as a baseline. We show that training standalone DL models with augmented data that captures domain-invariant icing characteristics can help improve predictive performance across multiple wind parks. Through efficient identification of icing, this study can support preventive maintenance of wind energy sources by making them more reliable toward tackling climate change.
2023 Environmental Data Science, Cambridge University Press
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Wind energy’s ability to liberate the world of conventional sources of energy relies on lowering the significant costs associated with the maintenance of wind turbines. Since icing events on turbine rotor blades are a leading cause of operational failures, identifying icing in advance is critical. Some recent studies focus on specific wind parks and fail to generalize to unseen scenarios (e.g. new rotor blade designs). We propose the utilisation of synthetic data augmentation via neural style transfer to improve the generalization of existing ice prediction models. We show that training models with augmented data that captures domain-invariant icing characteristics can help improve predictive performance across multiple wind parks. Through efficient identification of icing, this study can support preventive maintenance of wind energy sources by making them more reliable towards tackling climate change.
2022 Climate Change AI Workshop, NeurIPS, New Orleans, USA
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With an increasing emphasis on driving down the costs of Operations and Maintenance (O&M) in the Offshore Wind (OSW) sector, comes the requirement to explore new methodology and applications of Deep Learning (DL) to the domain. Condition-based monitoring (CBM) has been at the forefront of recent research developing alarm-based systems and data-driven decision making. This paper provides a brief insight into the research being conducted in this area, with a specific focus on alarm sequence modelling and the associated challenges faced in its implementation. The paper proposes a novel idea to predict a set of relevant repair actions from an input sequence of alarm sequences, comparing Long Short-term Memory (LSTM) and Bidirectional LSTM (biLSTM) models. Achieving training accuracy results of up to 80.23%, and test accuracy results of up to 76.01% with biLSTM gives a strong indication to the potential benefits of the proposed approach that can be furthered in future research. The paper introduces a framework that integrates the proposed approach into O&M procedures and discusses the potential benefits which include the reduction of a confusing plethora of alarms, as well as unnecessary vessel transfers to the turbines for fault diagnosis and correction.
2022 8th International Symposium on Model-Based Safety Assessment, Munich, Germany
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Intelligent question-answering (QA) systems have witnessed increased interest in recent years, particularly in their ability to facilitate information access, data interpretation or decision support. The wind energy sector is one of the most promising sources of renewable energy, yet turbines regularly suffer from failures and operational inconsistencies, leading to downtimes and significant maintenance costs. Addressing these issues requires rapid interpretation of complex and dynamic data patterns under time-critical conditions. In this article, we present a novel approach that leverages interactive, natural language-based decision support for operations & maintenance (O&M) of wind turbines. The proposed interactive QA system allows engineers to pose domain-specific questions in natural language, and provides answers (in natural language) based on the automated retrieval of information on turbine sub-components, their properties and interactions, from a bespoke domain-specific knowledge graph. As data for specific faults is often sparse, we propose the use of paraphrase generation as a way to augment the existing dataset. Our QA system leverages encoder-decoder models to generate Cypher queries to obtain domain-specific facts from the KG database in response to user-posed natural language questions. Experiments with an attention-based sequence-to-sequence (Seq2Seq) model and a transformer show that the transformer accurately predicts up to 89.75% of responses to input questions, outperforming the Seq2Seq model marginally by 0.76%, though being 9.46 times more computationally efficient. The proposed QA system can help support engineers and technicians during O&M to reduce turbine downtime and operational costs, thus improving the reliability of wind energy as a source of renewable energy.
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A rise in ecological anomalous events will be observed due to climate change. One such event is the harmful algal bloom which occurs due to an increase in nutrients from anthropogenic activities and has economic and ecological effects. Algae thrive in warmer temperatures which will lead to an increase in the frequency of harmful algal blooms. To overcome this increasing frequency, early detection tools are essential. Deep learning and frequent monitoring have been used to detect this phenomenon with a focus on unimodal approaches. In this work, we propose using multiple sources of satellite and in-situ data for detecting algal blooms with a joint multimodal learning approach, focusing on the North Sea and the Irish Sea. This work will aid domain experts to monitor potential changes to the ecosystem done by human interference and to take action when necessary.
2022 ECML/PKDD Workshop on Machine Learning for Earth Observation
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Artificial intelligence (AI) can help facilitate wider adoption of renewable energy globally. We organized a social event for the AI and renewables community to discuss these aspects at the International Conference on Learning Representations (ICLR), a leading AI conference. This opinion reflects on the key messages and provides a call for action on leveraging AI for transition toward net zero.
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Possible sensory failures on monitoring systems re- sult in partially filled data which may lead to erroneous statistical conclusions which may affect critical systems such as pollutant detectors and anomaly activity detectors. Therefore imputation becomes necessary to decrease error. This work addresses the missing data problem by experimenting with various methods in the context of a water quality dataset with high miss rates. Compared models chosen make different assumptions about the data which are Generative Adversarial Networks, Multiple Im- putation by Chained Equations, Variational Auto-Encoders, and Recurrent Neural Networks. A novel recurrent neural network architecture with self-attention is proposed in which imputation is done in a single pass. The proposed model performs with a lower root mean square error, ranging between 0.012-0.28, in three of the four locations. The self-attention components increase the interpretability of the imputation process at each stage of the network, providing information to domain experts.
2022 IEEE International Joint Conference on Neural Networks (IJCNN). Padua, Italy.
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Offshore wind turbine monopiles require structural health monitoring throughout their lifespan, yet direct structural measurements are limited. This paper combines numerical modeling and machine learning to present an approach to obtain rapid estimations of monopile fatigue using hourly metocean conditions. Aero-hydro-servo-elastic numerical simulations for a reference turbine provide the meta-model training dataset that encompasses wind-wave conditions applicable to the North Sea. Analysis reveals conditions whereby higher-order fully non-linear wave kinematics produce larger damage values compared to linear waves. This increase in damage is absent when implementing a simple probabilistic data lumping method. The prototype meta-model is developed based on convolutional neural networks to determine the monopile damage from measured wind-wave conditions at high temporal frequency. The proof-of-concept meta-model provides a step-change that demonstrates a promising approach to estimate monopile fatigue accumulation at high temporal resolution with scope for development to specific real-world offshore wind farms where validation data is available.
2022 European Workshop on Structural Health Monitoring (EWSHM), Palermo, Italy.
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Accurate and efficient assessment of offshore wind turbine monopile fatigue is required to inform maintenance and decommissioning decision making. Although, direct field-based measurements are limited and current industry standard modelling approaches are often devoid of fully non-linear waves, thus omitting critically important resonance effects. Here, numerical modelling is combined with machine learning to develop a meta-model capable of rapidly estimating monopile damage and fatigue. Fully non-linear wave kinematics were numerically modelled using higher-order boundary element methods to represent conditions recorded in the North Sea. These environmental simulations were implemented within numerical areo-hydro-servo-elastic engineering modelling of a reference turbine (NREL 5MW) with monopile foundations, for both operational and parked turbine configurations across a range of incoming wind conditions. The modelled fore-aft tower base bending moments are used to estimate of the corresponding structural damage using rainflow-counting methods, enabling identification of conditions associated with the largest damage loads. These data are applied within the development a meta-model based on convolutional neural networks to provide rapid assessment of monopile damage associated with any given environmental and operational condition.
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Climate change will affect how water sources are managed and monitored. Continuous monitoring of water quality is crucial to detect pollution, to ensure that various natural cycles are not disrupted by anthropogenic activities and to assess the effec- tiveness of beneficial management measures taken under defined protocols. One such disruption is algal blooms in which population of phytoplank- ton increase rapidly affecting biodiversity in marine environments. The frequency of algal blooms will in- crease with climate change as it presents favourable conditions for reproduction of phytoplankton. Ma- chine learning has been used for early detection of algal blooms previously, with the focus mostly on single closed bodies of water in Far East Asia with short time ranges. In this work, we study four locations around the North Sea and the Irish Sea with different characteristics predicting activity with longer time-spans and explaining the importance of the input for the decision making process with regards to the prediction model. This work aids domain experts to monitor potential changes to the ecosystem done by human interference over longer time ranges and to take action when necessary.
2022 Northern Lights Deep Learning Conference (NLDL).
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Classical measurements and modelling that underpin present flood warning and alert systems are based on fixed and spa- tially restricted static sensor networks. Computationally ex- pensive physics-based simulations are often used that can’t react in real-time to changes in environmental conditions. We want to explore contemporary artificial intelligence (AI) for predicting flood risk in real time by using a diverse range of data sources. By combining heterogeneous data sources, we aim to nowcast rapidly changing flood conditions and gain a greater understanding of urgent humanitarian needs.
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Wind energy has emerged as a highly promising source of renewable energy in recent times. However, wind turbines regularly suffer from operational inconsistencies, leading to significant costs and challenges in operations and maintenance (O&M). Condition-based monitoring (CBM) and performance assessment/analysis of turbines are vital aspects for ensuring efficient O&M planning and cost minimisation. Data-driven decision making techniques have witnessed rapid evolution in the wind industry for such O&M tasks during the last decade, from applying signal processing methods in early 2010 to artificial intelligence (AI) techniques, especially deep learning in 2020. In this article, we utilise statistical computing to present a scientometric review of the conceptual and thematic evolution of AI in the wind energy sector, providing evidence-based insights into present strengths and limitations of data-driven decision making in the wind industry. We provide a perspective into the future and on current key challenges in data availability and quality, lack of transparency in black box-natured AI models, and prevailing issues in deploying models for real-time decision support, along with possible strategies to overcome these problems. We hope that a systematic analysis of the past, present and future of CBM and performance assessment can encourage more organisations to adopt data-driven decision making techniques in O&M towards making wind energy sources more reliable, contributing to the global efforts of tackling climate change.
2021. Renewable and Sustainable Energy Reviews 144.
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Offshore wind farm operators need to make short-term decisions on planning vessel transfers to turbines for preventive or corrective maintenance. These decisions can play a pivotal role in ensuring maintenance actions are carried out in a timely and cost-effective manner. The present optimization of offshore vessel transfer uses mathematical models rather than learning decisions from historical data. In this paper, we design a simulated environment for an offshore wind farm based on Supervisory Control & Acquisition (SCADA) data and alarm logs of historical faults in an operational turbine. Firstly, we utilise a state-of-art decision tree model to predict fault types using SCADA features, and provide explainable decisions. Next, we apply deep reinforcement learning to automatically learn maintenance priorities corresponding to different fault types for ensuring prioritized vessel transfers for critical conditions, and deciding on optimal vessel fleet size. This can lead to significant savings in maintenance costs for the offshore wind industry.
Developments in Renewable Energies Offshore: Proceedings of the 4th International Conference on Renewable Energies Offshore (RENEW 2020, 12-15 October 2020, Lisbon, Portugal).
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With growing natural disasters, rise in carbon emissions and faltering ecosystems, the need for furthering research in climate change has become integral. Recent studies have shown that data science can play a vital role in better understanding natural phenomena and discovering novel insights. Although no silver bullet, machine learning (ML) has been successfully utilised in an array of applications, ranging from prediction and assessment of droughts and floods, energy control in grids, water quality modelling, operations & maintenance (O&M) of renewable energy sources such as wind and solar energy etc. However, the existing studies suffer from 2 prime challenges: (1) Lack of data availability - domain specific information e.g. from wind turbines, is often commercially sensitive, making it difficult to procure large amounts of useable data - especially new kinds of data which can possibly generate significant new insights. Transfer learning techniques can help learn from little or no labelled data, ensuring accuracy and helping algorithms to generalise better. (2) The black-box nature of (deep) ML models makes them suffer from the problem of transparency, wherein, although predictions can often be made with high accuracy, confidence and trust in the model decisions is difficult. A human intelligible diagnosis of when, why, what and how a model performs (or not) is essential. Hybrid ML techniques can bridge the gap between transparency and accuracy, and causal inference can help discover hidden insights from data. Natural language generation can further help in generating informative reports and descriptions of natural disasters and O&M strategies for renewable energy sources. We propose a perspective to tackle some of these challenges in ensuring reliable decision making and envisage that making data-driven decision support systems intelligent and transparent would have a significant impact in tackling climate change.
2020. Workshop on Data Science in Climate and Climate Impact Research, ETH Zurich, Switzerland.
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As global efforts in transitioning to sustainable energy sources rise, wind energy has become a leading renewable energy resource. However, turbines are complex engineering systems and rely on effective operations & maintenance (O&M) to prevent catastrophic failures in sub-components (gearbox, generator, etc.). Wind turbines have multiple sensors embedded within their sub-components which regularly measure key internal and external parameters (generator bearing temperature, rotor speed, wind speed etc.) in the form of Supervisory Control & Data Acquisition (SCADA) data. While existing studies have focused on applying ML techniques towards anomaly prediction in turbines based on SCADA data, they have not been supported with transparent decisions, owing to the inherent black box nature of ML models. In this project, we aim to explore transparent and intelligent decision support in O&M of turbines, by predicting faults and providing human-intelligible maintenance strategies to avert and fix the underlying causes. We envisage that in contributing to explainable AI for the wind industry, our method would help make turbines more reliable, encouraging more organisations to switch to renewable energy sources for combating climate change.
2020. Proceedings of the European Conference on Artificial Intelligence (ECAI)’s Doctoral Consortium, Santiago, Spain, August.
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Machine learning techniques have been widely used for condition-based monitoring
of wind turbines using Supervisory Control & Acquisition (SCADA) data. However, many
machine learning models, including neural networks, operate as black boxes: despite performing
suitably well as predictive models, they are not able to identify causal associations within
the data. For data-driven system to approach human-level intelligence in generating effective
maintenance strategies, it is integral to discover hidden knowledge in the operational data. In
this paper, we apply deep learning to discover causal relationships between multiple features
(confounders) in SCADA data for faults in various sub-components from an operational turbine
using convolutional neural networks (CNNs) with attention. Our technique overcomes the black
box nature of conventional deep learners and identifies hidden confounders in the data through
the use of temporal causal graphs. We demonstrate the effects of SCADA features on a wind
turbine’s operational status, and show that our technique contributes to explainable AI for wind
energy applications by providing transparent and interpretable decision support.
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Condition-based monitoring (CBM) has been widely utilised in the wind industry for monitoring operational inconsistencies and failures in turbines,
with techniques ranging from signal processing and vibration analysis to
artificial intelligence (AI) models using Supervisory Control & Acquisition
(SCADA) data. However, existing studies do not present a concrete basis to facilitate explainable decision support in operations and maintenance
(O&M), particularly for automated decision support through recommendation of appropriate maintenance action reports corresponding to failures predicted by CBM techniques. Knowledge graph databases (KGs) model a
collection of domain-specific information and have played an intrinsic role
for real-world decision support in domains such as healthcare and finance,
but have seen very limited attention in the wind industry. We propose
XAI4Wind, a multimodal knowledge graph for explainable decision support
in real-world operational turbines and demonstrate through experiments several use-cases of the proposed KG towards O&M planning through interactive
query and reasoning and providing novel insights using graph data science
algorithms. The proposed KG combines multimodal knowledge like SCADA
parameters and alarms with natural language maintenance actions, images
etc. By integrating our KG with an Explainable AI model for anomaly prediction, we show that it can provide effective human-intelligible O&M strategies
for predicted operational inconsistencies in various turbine sub-components.
This can help instil better trust and confidence in conventionally black-box
AI models. We make our KG publicly available and envisage that it can
serve as the building ground for providing autonomous decision support in
the wind industry.
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The last decade has witnessed an increased interest in applying machine learning techniques to predict faults and anomalies in the operation of wind turbines. These e�orts have lately been dominated by deep learning techniques which, as in other �elds, tend to outperform traditional machine learning algorithms given su�cient amounts of training data. An important shortcoming of deep learning models is their lack of transparency – they operate as black boxes and typically do not provide rationales for their predictions, which can lead to a lack of trust in predicted out- puts. In this article, a novel hybrid model for anomaly prediction in wind farms is proposed, that combines a recurrent neural network approach for accurate classi�cation with an XGBoost deci- sion tree classi�er for transparent outputs. Experiments with an o�shore wind turbine show that our model achieves a classi�cation accuracy of up to 97%. The model is further able to generate detailed feature importance analyses for any detected anomalies, identifying exactly those com- ponents in a wind turbine that contribute to an anomaly. Finally, the feasibility of transfer learning is demonstrated for the wind domain by porting our “o�shore" model to an unseen dataset from an onshore wind farm. The latter model achieves an accuracy of 65% and is able to detect 85% of anomalies in the unseen domain. These results are encouraging for application to wind farms for which no training data is available, e.g. because they have not been in operation for long.
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Wind energy is one of the fastest-growing sustainable energy sources in the world but relies crucially on efficient and effective operations and maintenance to generate sufficient amounts of energy and reduce downtime of wind turbines and associated costs. Machine learning has been applied to fault prediction in wind turbines, but these predictions have not been supported with suggestions on how to avert and fix faults. We present a data-to-text generation system utilising transformers for generating corrective maintenance strategies for faults using SCADA data capturing the operational status of turbines. We achieve this in two stages: a first stage identifies faults based on SCADA input features and their relevance. A second stage performs content selection for the language generation task and creates maintenance strategies based on phrase-based natural language templates. Experiments show that our dual transformer model achieves an accuracy of up to 96.75% for alarm prediction and up to 75.35% for its choice of maintenance strategies during content-selection. A qualitative analysis shows that our generated maintenance strategies are promising. We make our human- authored maintenance templates publicly available, and include a brief video explaining our approach.
2020 International Joint Conference on Neural Networks (IJCNN).
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The global pursuit towards sustainable development is leading to increased adaptation of renewable energy sources. Wind turbines are
promising sources of clean energy, but regularly suffer from failures
and down-times, primarily due to the complex environments and
unpredictable conditions wherein they are deployed. While various
studies have earlier utilised machine learning techniques for fault
prediction in turbines, their black-box nature hampers explainability and trust in decision making. We propose the application of
causal reasoning in operations & maintenance of wind turbines using Supervisory Control & Acquisition (SCADA) data, and harness
attention-based convolutional neural networks (CNNs) to identify
hidden associations between different parameters contributing to
failures in the form of temporal causal graphs. By interpreting these
non-obvious relationships (many of which may have potentially
been disregarded as noise), engineers can plan ahead for unforeseen
failures, helping make wind power sources more reliable.
Fragile Earth Workshop, KDD, August 2020, San Diego, CA
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Wind turbines suffer from operational inconsistencies due to a variety of factors, ranging from environmental changes, to intrinsic anomalies in specific components, such as gearbox, generator, pitch system etc. Condition monitoring of wind turbines has been a critical research area in the last decade, wherein the Supervisory Control & Data Acquisition (SCADA) data is used to analyse the operational behaviour of the turbine and predict any incipient faults to prevent catastrophic losses caused by unexpected failures. Machine learning models have formed a large part of the data-analytics based methods used for learning from historical failures through supervised learning, but they suffer from the lack of ability to provide additional capabilities for learning with little labelled data, or for that matter, no labelled faults in a different domain. Deep learning has shown immense success in areas where time-series data is to be modelled. In this paper, we propose a hybrid deep learning model combining a Long short-term memory network (LSTM) with XGBoost, a decision tree-based classifier for providing the benefits of accuracy through deep learning, and transparency through traditional decision trees. Our study shows that Transfer learning allows us to make predictions with increasing accuracy on unseen data; which is useful for simulations of new operations, new wind farms or other cases of non-available training data. This can help reduce downtime of turbines through predictive maintenance, by predicting incipient faults, or provide corrective maintenance, by assisting the engineers and technicians to analyse the root causes behind the failure, thus contributing to the reliability and uptake of wind energy as a sustainable and promising domain.
2019. In WindEurope Offshore, Copenhagen, Denmark.
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Wind energy is one of the fastest-growing sustainable energy sources in the world
but relies crucially on efficient and effective operations and maintenance to generate
sufficient amounts of energy and reduce downtime of wind turbines and associated
costs. Machine learning has been applied to fault prediction in wind turbines,
but these predictions have not been supported with suggestions on how to avert
and fix faults. We present a data-to-text generation system using transformers to
produce event descriptions from SCADA data capturing the operational status of
turbines and proposing maintenance strategies. Experiments show that our model
learns feature representations that correspond to expert judgements. In making a
contribution to the reliability of wind energy, we hope to encourage organisations
to switch to sustainable energy sources and help combat climate change.
2019. In NeurIPS 2019 Workshop on Tackling Climate Change with Machine Learning. Vancouver, Canada.
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With the rising costs of conventional sources of en- ergy, the world is moving towards sustainable energy sources including wind energy. Wind turbines consist of several electrical and mechanical components and experience an enormous amount of irregular loads, making their operational behaviour at times inconsis- tent. Operations and Maintenance (O&M) is a key factor in monitoring such inconsistent behaviour of the turbines in order to predict and prevent any in- cipient faults which may occur in the near future.
2019. Extended Abstract in Northern Lights Deep Learning Workshop (NLDL), Tromso, Norway.
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Wind energy is one of the fastest-growing sustainable energy sources in the world
but relies crucially on efficient and effective operations and maintenance to generate
sufficient amounts of energy and reduce downtime of wind turbines and associated
costs. Machine learning has been applied to fault prediction in wind turbines,
but these predictions have not been supported with suggestions on how to avert
and fix faults. We present a data-to-text generation system using transformers to
produce event descriptions from SCADA data capturing the operational status of
turbines and proposing maintenance strategies. Experiments show that our model
learns feature representations that correspond to expert judgements. In making a
contribution to the reliability of wind energy, we hope to encourage organisations
to switch to sustainable energy sources and help combat climate change.
2019. In NeurIPS 2019 Workshop on Tackling Climate Change with Machine Learning. Vancouver, Canada.
Hide/Show Full Abstract
With the rising costs of conventional sources of en- ergy, the world is moving towards sustainable energy sources including wind energy. Wind turbines consist of several electrical and mechanical components and experience an enormous amount of irregular loads, making their operational behaviour at times inconsis- tent. Operations and Maintenance (O&M) is a key factor in monitoring such inconsistent behaviour of the turbines in order to predict and prevent any in- cipient faults which may occur in the near future.
2019. Extended Abstract in Northern Lights Deep Learning Workshop (NLDL), Tromso, Norway.
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This study investigated the effects of a fully
protected marine reserve on commercially valuable scallops
and benthic habitats in Lamlash Bay, Isle of Arran,
United Kingdom. Dive surveys found the abundance of
juvenile scallops to be greater within the marine reserve
than outside. A novel multivariate approach, based upon
neural networks and generalised linear models, revealed
the greater abundance of juveniles to be related to the
greater presence of macroalgae and maerl within the
reserve boundaries. This complex habitat appeared to have
positively encouraged spat settlement. In contrast, the
density of adult scallops did not differ between the two
treatments, possibly due to the short duration of protection.
However, the age, size and biomass of adult scallops were
significantly greater within the reserve. Overall, this study
suggests that the newly created marine reserve is already
providing benefits which are flowing back to species targeted
by fisheries, emphasising the importance of marine
reserves in ecosystem-based management of fisheries.