Fleetwide data-enabled reliability improvement of wind turbines

Verstraeten, Timothy; Nowé, Ann; Keller, Jonathan; Guo, Yifeng; Sheng, Shuangwen; Helsen, Jan

doi:10.1016/j.rser.2019.03.019

Cited by 23 publications

(24 citation statements)

References 53 publications

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“…MATS is a Bayesian method, which means that it can leverage prior knowledge about the data distribution. This property is highly beneficial in many practical applications, e.g., influenza mitigation 18,19 and wind farm control 4 .…”

Section: Discussionmentioning

confidence: 99%

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Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures

Verstraeten

Bargiacchi

Libin

et al. 2020

Sci Rep

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Multi-agent coordination is prevalent in many real-world applications. However, such coordination is challenging due to its combinatorial nature. An important observation in this regard is that agents in the real world often only directly affect a limited set of neighbouring agents. Leveraging such loose couplings among agents is key to making coordination in multi-agent systems feasible. In this work, we focus on learning to coordinate. Specifically, we consider the multi-agent multi-armed bandit framework, in which fully cooperative loosely-coupled agents must learn to coordinate their decisions to optimize a common objective. We propose multi-agent Thompson sampling (MATS), a new Bayesian exploration-exploitation algorithm that leverages loose couplings. We provide a regret bound that is sublinear in time and low-order polynomial in the highest number of actions of a single agent for sparse coordination graphs. Additionally, we empirically show that MATS outperforms the state-of-the-art algorithm, MAUCE, on two synthetic benchmarks, and a novel benchmark with Poisson distributions. An example of a loosely-coupled multi-agent system is a wind farm. Coordination within the wind farm is necessary to maximize power production. As upstream wind turbines only affect nearby downstream turbines, we can use MATS to efficiently learn the optimal control mechanism for the farm. To demonstrate the benefits of our method toward applications we apply MATS to a realistic wind farm control task. In this task, wind turbines must coordinate their alignments with respect to the incoming wind vector in order to optimize power production. Our results show that MATS improves significantly upon state-of-the-art coordination methods in terms of performance, demonstrating the value of using MATS in practical applications with sparse neighbourhood structures.

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Section: Discussionmentioning

confidence: 99%

“…Multi-agent coordination is prevalent in many real-world applications, such as traffic light control 1 , warehouse commissioning 2 and wind farm control 3,4 . Often, such settings can be formulated as coordination problems in which agents have to cooperate in order to optimize a shared team reward 5 .…”

mentioning

confidence: 99%

Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures

Verstraeten

Bargiacchi

Libin

et al. 2020

Sci Rep

Self Cite

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“…Digital technologies have improved O&M (Reininga 2020; Doner 2020); however, there are still a lot of R&D needs to harness their full potential Gao and Sheng 2018;Verstraeten et al 2019;Pfaffel, Faulstich, and Sheng 2019). With the emphasis of an AI-focused R&D initiative within DOE (2021), it would be beneficial to examine the following:…”

Section: Improving Wind Turbine Availability Through Data Analyticsmentioning

confidence: 99%

Wind Turbine Drivetrain Reliability and Wind Plant Operations and Maintenance Research and Development Opportunities

Keller

Sheng

Guo

et al. 2021

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“…Further research also investigated simulating load by means of SCADA data from a single turbine or even the farm [35,36], eventually serving as an input for residual useful lifetime (RUL) estimations. Using the data of a whole farm can also serve to reconstruct an optimized flow through the area of this farm [37].…”

Section: Previous Workmentioning

confidence: 99%

Quantification of the Information Loss Resulting from Temporal Aggregation of Wind Turbine Operating Data

et al. 2021

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SCADA operating data are more and more used across the wind energy domain, both as a basis for power output prediction and turbine health status monitoring. Current industry practice to work with this data is by aggregating the signals at coarse resolution of typically 10-min averages, in order to reduce data transmission and storage costs. However, aggregation, i.e., downsampling, induces an inevitable loss of information and is one of the main causes of skepticism towards the use of SCADA operating data to model complex systems such as wind turbines. This research aims to quantify the amount of information that is lost due to this downsampling of SCADA operating data and characterize it with respect to the external factors that might influence it. The issue of information loss is framed by three key questions addressing effects on the local and global scale as well as the influence of external conditions. Moreover, recommendations both for wind farm operators and researchers are provided with the aim to improve the information content. We present a methodology to determine the ideal signal resolution that minimized storage footprint, while guaranteeing high quality of the signal. Data related to the wind, electrical signals, and temperatures of the gearbox resulted as the critical signals that are largely affected by an information loss upon aggregation and turned out to be best recorded and stored at high resolutions. All analyses were carried out using more than one year of 1 Hz SCADA data of onshore wind farm counting 12 turbines located in the UK.

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Fleetwide data-enabled reliability improvement of wind turbines

Cited by 23 publications

References 53 publications

Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures

Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures

Wind Turbine Drivetrain Reliability and Wind Plant Operations and Maintenance Research and Development Opportunities

Quantification of the Information Loss Resulting from Temporal Aggregation of Wind Turbine Operating Data

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