An XAI framework for robust and transparent data-driven wind turbine power curve models

• URL: http://arxiv.org/abs/2304.09835v2
• Date: Wed, 6 Sep 2023 09:30:53 GMT
• Title: An XAI framework for robust and transparent data-driven wind turbine power curve models
• Authors: Simon Letzgus and Klaus-Robert M\"uller
• Abstract summary: Wind turbine power curve models translate ambient conditions into turbine power output. In recent years, increasingly complex machine learning methods have become state-of-the-art for this task. We introduce an explainable artificial intelligence framework to investigate and validate strategies learned by data-driven power curve models.
• Score: 0.8547032097715571
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wind turbine power curve models translate ambient conditions into turbine power output. They are essential for energy yield prediction and turbine performance monitoring. In recent years, increasingly complex machine learning methods have become state-of-the-art for this task. Nevertheless, they frequently encounter criticism due to their apparent lack of transparency, which raises concerns regarding their performance in non-stationary environments, such as those faced by wind turbines. We, therefore, introduce an explainable artificial intelligence (XAI) framework to investigate and validate strategies learned by data-driven power curve models from operational wind turbine data. With the help of simple, physics-informed baseline models it enables an automated evaluation of machine learning models beyond standard error metrics. Alongside this novel tool, we present its efficacy for a more informed model selection. We show, for instance, that learned strategies can be meaningful indicators for a model's generalization ability in addition to test set errors, especially when only little data is available. Moreover, the approach facilitates an understanding of how decisions along the machine learning pipeline, such as data selection, pre-processing, or training parameters, affect learned strategies. In a practical example, we demonstrate the framework's utilisation to obtain more physically meaningful models, a prerequisite not only for robustness but also for insights into turbine operation by domain experts. The latter, we demonstrate in the context of wind turbine performance monitoring. Alongside this paper, we publish a Python implementation of the presented framework and hope this can guide researchers and practitioners alike toward training, selecting and utilizing more transparent and robust data-driven wind turbine power curve models.

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)
• Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification [2.126171264016785]
  We use physics-informed neural networks to reproduce historical data coming from 4 turbines in a wind farm. The developed models for regression of the power, torque, and power coefficient showed great accuracy for both real data and physical equations governing the system.
  arXiv  Detail & Related papers  (2023-07-27T07:58:38Z)
• 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)
• End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning [7.850747042819504]
  This work proposes a surrogate model for the representation of wind turbine wakes based on a graph representation learning method termed a graph neural network. The proposed end-to-end deep learning model operates directly on unstructured meshes and has been validated against high-fidelity data. A case study based upon a real world wind farm further demonstrates the capability of the proposed approach to predict farm scale power generation.
  arXiv  Detail & Related papers  (2022-11-24T15:00:06Z)
• XAI for transparent wind turbine power curve models [0.0]
  We use Shapley values and XAI to uncover strategies machine learning models have learned from operational wind turbine data. Our findings reveal that the trend towards ever larger model architectures, driven by a focus on test set performance, can result in physically implausible model strategies. We propose a practical approach to utilize explanations for root cause analysis in the context of wind turbine performance monitoring.
  arXiv  Detail & Related papers  (2022-10-21T16:59:06Z)
• Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One [83.5162421521224]
  We propose a unique method termed E-ARM for training autoregressive generative models. E-ARM takes advantage of a well-designed energy-based learning objective. We show that E-ARM can be trained efficiently and is capable of alleviating the exposure bias problem.
  arXiv  Detail & Related papers  (2022-06-26T10:58:41Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• 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)
• Learning by Distillation: A Self-Supervised Learning Framework for Optical Flow Estimation [71.76008290101214]
  DistillFlow is a knowledge distillation approach to learning optical flow. It achieves state-of-the-art unsupervised learning performance on both KITTI and Sintel datasets. Our models ranked 1st among all monocular methods on the KITTI 2015 benchmark, and outperform all published methods on the Sintel Final benchmark.
  arXiv  Detail & Related papers  (2021-06-08T09:13:34Z)
• 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)

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.