Hybrid Neuro-Evolutionary Method for Predicting Wind Turbine Power Output

• URL: http://arxiv.org/abs/2004.12794v1
• Date: Thu, 2 Apr 2020 04:22:22 GMT
• Title: Hybrid Neuro-Evolutionary Method for Predicting Wind Turbine Power Output
• Authors: Mehdi Neshat, Meysam Majidi Nezhad, Ehsan Abbasnejad, Daniele Groppi, Azim Heydari, Lina Bertling Tjernberg, Davide Astiaso Garcia, Bradley Alexander and Markus Wagner
• Abstract summary: We use historical data in the supervisory control and data acquisition (SCADA) systems as input to estimate the power output from an onshore wind farm in Sweden. With the prior knowledge that the underlying wind patterns are highly non-linear and diverse, we combine a self-adaptive differential evolution (SaDE) algorithm. We show that our approach outperforms its counterparts.
• Score: 6.411829871947649
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable wind turbine power prediction is imperative to the planning, scheduling and control of wind energy farms for stable power production. In recent years Machine Learning (ML) methods have been successfully applied in a wide range of domains, including renewable energy. However, due to the challenging nature of power prediction in wind farms, current models are far short of the accuracy required by industry. In this paper, we deploy a composite ML approach--namely a hybrid neuro-evolutionary algorithm--for accurate forecasting of the power output in wind-turbine farms. We use historical data in the supervisory control and data acquisition (SCADA) systems as input to estimate the power output from an onshore wind farm in Sweden. At the beginning stage, the k-means clustering method and an Autoencoder are employed, respectively, to detect and filter noise in the SCADA measurements. Next, with the prior knowledge that the underlying wind patterns are highly non-linear and diverse, we combine a self-adaptive differential evolution (SaDE) algorithm as a hyper-parameter optimizer, and a recurrent neural network (RNN) called Long Short-term memory (LSTM) to model the power curve of a wind turbine in a farm. Two short time forecasting horizons, including ten-minutes ahead and one-hour ahead, are considered in our experiments. We show that our approach outperforms its counterparts.

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)
• 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)
• 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)
• Data-Driven Stochastic AC-OPF using Gaussian Processes [54.94701604030199]
  Integrating a significant amount of renewables into a power grid is probably the most a way to reduce carbon emissions from power grids slow down climate change. This paper presents an alternative data-driven approach based on the AC power flow equations that can incorporate uncertainty inputs. The GP approach learns a simple yet non-constrained data-driven approach to close this gap to the AC power flow equations.
  arXiv  Detail & Related papers  (2022-07-21T23:02:35Z)
• Physics Informed Shallow Machine Learning for Wind Speed Prediction [66.05661813632568]
  We analyze a massive dataset of wind measured from anemometers located at 10 m height in 32 locations in Italy. We train supervised learning algorithms using the past history of wind to predict its value at a future time. We find that the optimal design as well as its performance vary with the location.
  arXiv  Detail & Related papers  (2022-04-01T14:55:10Z)
• 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)
• Wind Power Projection using Weather Forecasts by Novel Deep Neural Networks [0.0]
  Using optimized machine learning algorithms, it is possible to find obscured patterns in the observations and obtain meaningful data. The paper explores the use of both parametric and the non-parametric models for calculating wind power prediction using power curves.
  arXiv  Detail & Related papers  (2021-08-22T17:46:36Z)
• Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids [67.72249211312723]
  Real-time state estimation and forecasting is critical for efficient operation of power grids. PhI-GPR is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate both observed and unobserved states.
  arXiv  Detail & Related papers  (2020-10-09T14:18:31Z)
• An Evolutionary Deep Learning Method for Short-term Wind Speed Prediction: A Case Study of the Lillgrund Offshore Wind Farm [6.939496425463776]
  This study uses a new hybrid evolutionary approach that uses a popular evolutionary search algorithm, CMA-ES, to tune the hyper- parameters of two Long short-term memory(LSTM) ANN models for wind prediction. The proposed hybrid approach is trained on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea.
  arXiv  Detail & Related papers  (2020-02-21T03:28:17Z)

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.