Residual-based Attention Physics-informed Neural Networks for Efficient Spatio-Temporal Lifetime Assessment of Transformers Operated in Renewable Power Plants
- URL: http://arxiv.org/abs/2405.06443v1
- Date: Fri, 10 May 2024 12:48:57 GMT
- Title: Residual-based Attention Physics-informed Neural Networks for Efficient Spatio-Temporal Lifetime Assessment of Transformers Operated in Renewable Power Plants
- Authors: Ibai Ramirez, Joel Pino, David Pardo, Mikel Sanz, Luis del Rio, Alvaro Ortiz, Kateryna Morozovska, Jose I. Aizpurua,
- Abstract summary: This article introduces an efficienttemporal model for transformer winding temperature and ageing estimation.
It uses physics-based partial differential equations with data-driven Neural Networks.
It is validated with a distribution transformer operated on a floating photovoltaic power plant.
- Score: 0.6223528900192875
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Transformers are vital assets for the reliable and efficient operation of power and energy systems. They support the integration of renewables to the grid through improved grid stability and operation efficiency. Monitoring the health of transformers is essential to ensure grid reliability and efficiency. Thermal insulation ageing is a key transformer failure mode, which is generally tracked by monitoring the hotspot temperature (HST). However, HST measurement is complex and expensive and often estimated from indirect measurements. Existing computationally-efficient HST models focus on space-agnostic thermal models, providing worst-case HST estimates. This article introduces an efficient spatio-temporal model for transformer winding temperature and ageing estimation, which leverages physics-based partial differential equations (PDEs) with data-driven Neural Networks (NN) in a Physics Informed Neural Networks (PINNs) configuration to improve prediction accuracy and acquire spatio-temporal resolution. The computational efficiency of the PINN model is improved through the implementation of the Residual-Based Attention scheme that accelerates the PINN model convergence. PINN based oil temperature predictions are used to estimate spatio-temporal transformer winding temperature values, which are validated through PDE resolution models and fiber optic sensor measurements, respectively. Furthermore, the spatio-temporal transformer ageing model is inferred, aiding transformer health management decision-making and providing insights into localized thermal ageing phenomena in the transformer insulation. Results are validated with a distribution transformer operated on a floating photovoltaic power plant.
Related papers
- SAfEPaTh: A System-Level Approach for Efficient Power and Thermal Estimation of Convolutional Neural Network Accelerator [4.1221717424687165]
This paper introduces SAfEPaTh, a novel system-level approach for accurately estimating power and temperature in tile-based CNN accelerators.
By addressing both steady-state and transient-state scenarios, SAfEPaTh effectively captures the dynamic effects of pipeline bubbles in interlayer pipelines.
arXiv Detail & Related papers (2024-07-24T20:29:52Z) - Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves [69.9104427437916]
Multi-generator Wave Energy Converters (WEC) must handle multiple simultaneous waves coming from different directions called spread waves.
These complex devices need controllers with multiple objectives of energy capture efficiency, reduction of structural stress to limit maintenance, and proactive protection against high waves.
In this paper, we explore different function approximations for the policy and critic networks in modeling the sequential nature of the system dynamics.
arXiv Detail & Related papers (2024-04-17T02:04:10Z) - 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 Long-Term Time-Series Forecasting: Feature, Pattern, and
Distribution [57.71199089609161]
Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning.
Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism.
We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
arXiv Detail & Related papers (2023-01-05T13:59:29Z) - Real-time Health Monitoring of Heat Exchangers using Hypernetworks and
PINNs [12.23889788846524]
A hypernetwork based approach is used to enable the domain-decomposed PINN learn the thermal behavior of the heat exchanger.
We achieve orders of magnitude reduction in inference time in comparison to existing PINNs, while maintaining the accuracy on par with the physics-based simulations.
arXiv Detail & Related papers (2022-12-20T07:07:44Z) - Efficient Localness Transformer for Smart Sensor-Based Energy
Disaggregation [8.828396559882954]
We propose an efficient localness transformer for non-intrusive load monitoring (NILM)
Specifically, we leverage normalization functions and switch the order of matrix multiplication to approximate self-attention.
We demonstrate the efficiency and effectiveness of the the proposed ELTransformer with considerable improvements compared to state-of-the-art baselines.
arXiv Detail & Related papers (2022-03-29T22:58:39Z) - Enhanced physics-constrained deep neural networks for modeling vanadium
redox flow battery [62.997667081978825]
We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions.
The ePCDNN can accurately capture the voltage response throughout the charge--discharge cycle, including the tail region of the voltage discharge curve.
arXiv Detail & Related papers (2022-03-03T19:56:24Z) - Fast Transient Stability Prediction Using Grid-informed Temporal and
Topological Embedding Deep Neural Network [4.116150060665464]
This paper proposes the temporal and topological embedding deep neural network (TTEDNN) model to forecast transient stability with the early transient dynamics.
The TTEDNN model can accurately and efficiently predict the transient stability by extracting the temporal and topological features from the time-series data.
The results show that the TTEDNN model has the best and most robust prediction performance.
arXiv Detail & Related papers (2022-01-23T12:22:44Z) - Learning Generative Vision Transformer with Energy-Based Latent Space
for Saliency Prediction [51.80191416661064]
We propose a novel vision transformer with latent variables following an informative energy-based prior for salient object detection.
Both the vision transformer network and the energy-based prior model are jointly trained via Markov chain Monte Carlo-based maximum likelihood estimation.
With the generative vision transformer, we can easily obtain a pixel-wise uncertainty map from an image, which indicates the model confidence in predicting saliency from the image.
arXiv Detail & Related papers (2021-12-27T06:04:33Z) - Efficient pre-training objectives for Transformers [84.64393460397471]
We study several efficient pre-training objectives for Transformers-based models.
We prove that eliminating the MASK token and considering the whole output during the loss are essential choices to improve performance.
arXiv Detail & Related papers (2021-04-20T00:09:37Z) - Thermal Neural Networks: Lumped-Parameter Thermal Modeling With
State-Space Machine Learning [0.0]
Thermal models for electric power systems are required to be both, real-time capable and of high estimation accuracy.
In this work, the thermal neural network (TNN) is introduced, which unifies both, consolidated knowledge in the form of heat-transfer-based lumped- parameter models.
A TNN has physically interpretable states through its state-space representation, is end-to-end trainable, and requires no material, geometry, nor expert knowledge for its design.
arXiv Detail & Related papers (2021-03-30T13:15:48Z)
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.