A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow

• URL: http://arxiv.org/abs/2301.02651v1
• Date: Fri, 6 Jan 2023 18:55:44 GMT
• Title: A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow
• Authors: Pooja Algikar, Yijun Xu, Somayeh Yarahmadi, Lamine Mili
• Abstract summary: The proposed model is trained on recorded time-series data of voltage phasors and power injections to perform a time-series power flow calculation. Our simulation results show that the proposed robust model can handle up to 25% of outliers in the training data set.
• Score: 2.7356119162292654
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we propose a robust data-driven process model whose hyperparameters are robustly estimated using the Schweppe-type generalized maximum likelihood estimator. The proposed model is trained on recorded time-series data of voltage phasors and power injections to perform a time-series stochastic power flow calculation. Power system data are often corrupted with outliers caused by large errors, fault conditions, power outages, and extreme weather, to name a few. The proposed model downweights vertical outliers and bad leverage points in the measurements of the training dataset. The weights used to bound the influence of the outliers are calculated using projection statistics, which are a robust version of Mahalanobis distances of the time series data points. The proposed method is demonstrated on the IEEE 33-Bus power distribution system and a real-world unbalanced 240-bus power distribution system heavily integrated with renewable energy sources. Our simulation results show that the proposed robust model can handle up to 25% of outliers in the training data set.

Related papers

• Transformer based time series prediction of the maximum power point for solar photovoltaic cells [0.24233709516962787]
  This paper proposes an improved deep learning based maximum power point tracking (MPPT) in solar photovoltaic cells. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions.
  arXiv  Detail & Related papers  (2024-09-24T17:26:55Z)
• A Meta-Learning Approach to Predicting Performance and Data Requirements [163.4412093478316]
  We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model performance, leads to large error when using a small dataset. We introduce a novel piecewise power law (PPL) that handles the two data differently.
  arXiv  Detail & Related papers  (2023-03-02T21:48:22Z)
• 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)
• HigeNet: A Highly Efficient Modeling for Long Sequence Time Series Prediction in AIOps [30.963758935255075]
  In this paper, we propose a highly efficient model named HigeNet to predict the long-time sequence time series. We show that training time, resource usage and accuracy of the model are found to be significantly better than five state-of-the-art competing models.
  arXiv  Detail & Related papers  (2022-11-13T13:48:43Z)
• Data-driven Models to Anticipate Critical Voltage Events in Power Systems [0.0]
  This paper explores the effectiveness of data-driven models to predict voltage excursion events in power systems using simple categorical labels. A proof-of-concept case study on a real portion of the Italian 150 kV sub-transmission network, which hosts a significant amount of wind power generation, demonstrates the general validity of the proposal.
  arXiv  Detail & Related papers  (2022-07-24T20:24:27Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Principal Component Density Estimation for Scenario Generation Using Normalizing Flows [62.997667081978825]
  We propose a dimensionality-reducing flow layer based on the linear principal component analysis (PCA) that sets up the normalizing flow in a lower-dimensional space. We train the resulting principal component flow (PCF) on data of PV and wind power generation as well as load demand in Germany in the years 2013 to 2015.
  arXiv  Detail & Related papers  (2021-04-21T08:42:54Z)
• Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff [66.59869239999459]
  In finance, imputation of missing returns may be applied prior to training a portfolio optimization model. There is an inherent trade-off between the look-ahead-bias of using the full data set for imputation and the larger variance in the imputation from using only the training data. We propose a Bayesian posterior consensus distribution which optimally controls the variance and look-ahead-bias trade-off in the imputation.
  arXiv  Detail & Related papers  (2021-02-25T09:05:35Z)
• Robust Data-Driven Error Compensation for a Battery Model [0.0]
  Today's massively collected battery data is not yet used for more accurate and reliable simulations. A data-driven error model is introduced enhancing an existing physically motivated model. A neural network compensates the existing dynamic error and is further limited based on a description of the underlying data.
  arXiv  Detail & Related papers  (2020-12-31T16:11:36Z)
• Nonparametric Estimation in the Dynamic Bradley-Terry Model [69.70604365861121]
  We develop a novel estimator that relies on kernel smoothing to pre-process the pairwise comparisons over time. We derive time-varying oracle bounds for both the estimation error and the excess risk in the model-agnostic setting.
  arXiv  Detail & Related papers  (2020-02-28T21:52:49Z)
• Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows [8.859284959951204]
  Time series forecasting is fundamental to scientific and engineering problems. Deep learning methods are well suited for this problem. We show that it improves over the state-of-the-art for standard metrics on many real-world data sets.
  arXiv  Detail & Related papers  (2020-02-14T16:16:51Z)

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.