Wind turbine condition monitoring based on intra- and inter-farm federated learning

• URL: http://arxiv.org/abs/2409.03672v1
• Date: Thu, 5 Sep 2024 16:25:30 GMT
• Title: Wind turbine condition monitoring based on intra- and inter-farm federated learning
• Authors: Albin Grataloup, Stefan Jonas, Angela Meyer,
• Abstract summary: Many AI applications in wind energy may benefit from using operational data not only from individual wind turbines but from multiple turbines and multiple wind farms. Federated learning has emerged as a privacy-preserving distributed machine learning approach in this context. We investigate various federated learning strategies, including collaboration across different wind farms and turbine models, as well as collaboration restricted to the same wind farm and turbine model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As wind energy adoption is growing, ensuring the efficient operation and maintenance of wind turbines becomes essential for maximizing energy production and minimizing costs and downtime. Many AI applications in wind energy, such as in condition monitoring and power forecasting, may benefit from using operational data not only from individual wind turbines but from multiple turbines and multiple wind farms. Collaborative distributed AI which preserves data privacy holds a strong potential for these applications. Federated learning has emerged as a privacy-preserving distributed machine learning approach in this context. We explore federated learning in wind turbine condition monitoring, specifically for fault detection using normal behaviour models. We investigate various federated learning strategies, including collaboration across different wind farms and turbine models, as well as collaboration restricted to the same wind farm and turbine model. Our case study results indicate that federated learning across multiple wind turbines consistently outperforms models trained on a single turbine, especially when training data is scarce. Moreover, the amount of historical data necessary to train an effective model can be significantly reduced by employing a collaborative federated learning strategy. Finally, our findings show that extending the collaboration to multiple wind farms may result in inferior performance compared to restricting learning within a farm, specifically when faced with statistical heterogeneity and imbalanced datasets.

Related papers

• Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance [1.533848041901807]
  We leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods. A key innovation lies in the ensemble of FNN and LSTM models, capitalizing on their collective learning. Machine learning techniques are applied to detect wind turbine performance deterioration, enabling proactive maintenance strategies.
  arXiv  Detail & Related papers  (2024-03-01T20:54:31Z)
• Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer [112.12271800369741]
  Wind power is attracting increasing attention around the world due to its renewable, pollution-free, and other advantages. Accurate wind power forecasting (WPF) can effectively reduce power fluctuations in power system operations. Existing methods are mainly designed for short-term predictions and lack effective spatial-temporal feature augmentation.
  arXiv  Detail & Related papers  (2023-05-30T04:03:15Z)
• Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning [0.0]
  We present a distributed machine learning approach that preserves the data privacy by leaving the data on the wind turbines while still enabling fleet-wide learning on those local data. We show that through federated fleet-wide learning, turbines with little or no representative training data can benefit from more accurate normal behavior models.
  arXiv  Detail & Related papers  (2022-12-07T09:22:33Z)
• Modeling Wind Turbine Performance and Wake Interactions with Machine Learning [0.0]
  Different machine learning (ML) models are trained on SCADA and meteorological data collected at an onshore wind farm. ML methods for data quality control and pre-processing are applied to the data set under investigation. A hybrid model is found to achieve high accuracy for modeling wind turbine power capture.
  arXiv  Detail & Related papers  (2022-12-02T23:07:05Z)
• Low Emission Building Control with Zero-Shot Reinforcement Learning [70.70479436076238]
  Control via Reinforcement Learning (RL) has been shown to significantly improve building energy efficiency. We show it is possible to obtain emission-reducing policies without a priori--a paradigm we call zero-shot building control.
  arXiv  Detail & Related papers  (2022-08-12T17:13:25Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)
• Measuring Wind Turbine Health Using Drifting Concepts [55.87342698167776]
  We propose two new approaches for the analysis of wind turbine health. The first method aims at evaluating the decrease or increase in relatively high and low power production. The second method evaluates the overall drift of the extracted concepts.
  arXiv  Detail & Related papers  (2021-12-09T14:04:55Z)
• Scalable Optimization for Wind Farm Control using Coordination Graphs [5.56699571220921]
  A wind farm controller is required to match the farm's power production with a power demand imposed by the grid operator. This is a non-trivial optimization problem, as complex dependencies exist between the wind turbines. We propose a new learning method for wind farm control that leverages the sparse wind farm structure to factorize the optimization problem.
  arXiv  Detail & Related papers  (2021-01-19T20:12:30Z)
• Multi-target normal behaviour models for wind farm condition monitoring [0.0]
  This research explores multi-target models as a new approach to capturing a wind turbine's normal behaviour. We find that multi-target models are advantageous in comparison to single-target modelling in that they can reduce the cost and effort of practical condition monitoring.
  arXiv  Detail & Related papers  (2020-12-05T16:46:35Z)
• Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration [130.89746032163106]
  We propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data. We show that the energy function and sampler can be trained efficiently via a new variational form of power iteration. We present an energy model guided fuzzer for software testing that achieves comparable performance to well engineered fuzzing engines like libfuzzer.
  arXiv  Detail & Related papers  (2020-11-10T19:31:29Z)
• Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms [98.78553146823829]
  Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks. In this paper, a novel framework is proposed to implement distributed learning (FL) algorithms within a UAV swarm.
  arXiv  Detail & Related papers  (2020-02-19T14:04:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.