277 research outputs found
Deep neural network configuration sensitivity analysis in wind power forecasting
The trend toward increasing integration of wind farms into the power system is a challenge for transmission and distribution system operators and electricity market operators. The variability of electricity generation from wind farms increases the requirements for flexibility needed for the reliable and stable operation of the power system. Operating a power system with a high share of renewables requires advanced generation and consumpti-on forecasting methods to ensure the reliable and economical operation of the system. Installed wind power capacities require advanced techniques to monitor and control such data-rich power systems. The rapid development of advanced artificial neural networks and data processing capabilities offers numerous potential applications. The effectiveness of advanced deep recurrent neural networks with long-term memory is constantly being demonstrated for learning complex temporal sequence-to-sequence dependencies. This paper presents the application of deep learning methods to wind power production forecasting. The models are trained using historical wind farm generation measurements and NWP weather forecasts for the areas of Croatian wind farms. Furthermore, a comparison of the accuracy of the proposed models with currently used forecasting tools is presented
A deep learning framework based on Koopman operator for data-driven modeling of vehicle dynamics
Autonomous vehicles and driving technologies have received notable attention
in the past decades. In autonomous driving systems, \textcolor{black}{the}
information of vehicle dynamics is required in most cases for designing of
motion planning and control algorithms. However, it is nontrivial for
identifying a global model of vehicle dynamics due to the existence of strong
non-linearity and uncertainty. Many efforts have resorted to machine learning
techniques for building data-driven models, but it may suffer from
interpretability and result in a complex nonlinear representation. In this
paper, we propose a deep learning framework relying on an interpretable Koopman
operator to build a data-driven predictor of the vehicle dynamics. The main
idea is to use the Koopman operator for representing the nonlinear dynamics in
a linear lifted feature space. The approach results in a global model that
integrates the dynamics in both longitudinal and lateral directions. As the
core contribution, we propose a deep learning-based extended dynamic mode
decomposition (Deep EDMD) algorithm to learn a finite approximation of the
Koopman operator. Different from other machine learning-based approaches, deep
neural networks play the role of learning feature representations for EDMD in
the framework of the Koopman operator. Simulation results in a high-fidelity
CarSim environment are reported, which show the capability of the Deep EDMD
approach in multi-step prediction of vehicle dynamics at a wide operating
range. Also, the proposed approach outperforms the EDMD method, the multi-layer
perception (MLP) method, and the Extreme Learning Machines-based EDMD
(ELM-EDMD) method in terms of modeling performance. Finally, we design a linear
MPC with Deep EDMD (DE-MPC) for realizing reference tracking and test the
controller in the CarSim environment.Comment: 12 pages, 10 figures, 1 table, and 2 algorithm
Numerical weather prediction wind correction methods and its impact on computational fluid dynamics based wind power forecasting
Numerical weather prediction (NWP) of wind speed (WS) is an important input to wind power forecasting (WPF), which its accuracy will limit the WPF performance. This paper proposes three NWP correcting methods based on the multiple linear regression, a radial basis function neural network, and an Elman neural network. The proposed correction methods exhibit small sample learning and efficient computational ability. So, they are in favour of forecasting the performance of planned large-scale wind farms. To this end, a physical WPF model based on computational fluid dynamics is used to demonstrate the impact of improving the NWP WS data based forecasting. A certain wind farm located in China is selected as the case study, and the measured and NWP WS forecasts before and after correction are taken as inputs to the WPF model. Results show that all three correction methods improve the precision of the NWP WS forecasts, with the nonlinear correction models performing a little better than the linear one. Compared with the original NWP, the three corrected NWP WS have higher annual, single point, and short-term prediction accuracy. As expected, the accuracy of wind power forecasting will increase with the accuracy of the input NWP WS forecast. Moreover, the WS correction enhances the consistency of error variation trends between input WS and output wind power. The proposed WS correction methods greatly improve the accuracy of both original NWP WS and the WPF derived from them
Clustering methods of wind turbines and its application in short-term wind power forecasts
Commonly used wind power forecasts methods choose only one representative wind turbine to forecast the output power of the entire wind farm; however, this approach may reduce the forecasting accuracy. If each wind turbine in a wind farm is forecasted individually, this considerably increases the computational cost, especially for a large wind farm. In this work, a compromise approach is developed where the turbines in the wind farm are clustered and a forecast made for each cluster. Three clustering methods are evaluated: K-means; a self-organizing map (SOM); and spectral clustering (SC). At first, wind turbines in a wind farm are clustered into several groups by identifying similar characteristics of wind speed and output power. Sihouette coefficient and Hopkins statistics indices are adopted to determine the optimal cluster number which is an important parameter in cluster analysis. Next, forecasting models of the selected representative wind turbines for each cluster based on correlation analysis are established separately. A comparative study of the forecast effect is carried to determine the most effective clustering method. Results show that the short-term wind power forecasting on the basis of SOM and SC clustering are effective to forecast the output power of the entire wind farm with better accuracy, respectively, 1.67% and 1.43% than the forecasts using a single wind speed or power to represent the wind farm. Both Hopkins statistics and Sihouette coefficient are effective in choosing the optimal number of clusters. In addition, SOM with its higher forecast accuracy and SC with more efficient calculation when applied into wind power forecasts can provide guidance for the operating and dispatching of wind power. The emphasis of the paper is on the clustering methods and its effect applied in wind power forecasts but not the forecasting algorithms
Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model
Wind power plays a leading role in the development of renewable energy. However, the random nature of wind turbine power and its associated uncertainty create challenges in dispatching this power effectively in the power system, which can result in unnecessary curtailment of the wind turbine power. Improving the accuracy of wind turbine power forecasting is an effective measure for resolving such problems. This study uses a deep learning network to forecast the wind turbine power based on a long short-term memory network (LSTM) algorithm and uses the Gaussian mixture model (GMM) to analyze the error distribution characteristics of short-term wind turbine power forecasting. The LSTM algorithm is used to forecast the power and uncertainties for three wind turbines within a wind farm. According to numerical weather prediction (NWP) data and historical power data for three turbines, the forecasting accuracy of the turbine with the largest number of training samples is the best of the three. For one of the turbines, the LSTM, radial basis function (RBF), wavelet, deep belief network (DBN), back propagation neural networks (BPNN), and Elman neural network (ELMAN) have been used to forecast the wind turbine power. This study compares the results and demonstrates that LSTM can greatly improve the forecasting accuracy. Moreover, this study obtains different confidence intervals for the three units according to the GMM, mixture density neural network (MDN), and relevance vector machine (RVM) model results. The LSTM method is shown to have higher accuracy and faster convergence than the other methods. However, the GMM method has better performance and evaluation than other methods and thus has practical application value for wind turbine power dispatching
Impact of wind farm wake steering control on blade root load
Yaw misalignment is known to affect blade root loads on wind turbines. Most of previous studies concentrate on yaw misalignment in the context of wake steering control, aiming at increasing the total output power of the wind farm. There, wake steering is compared with greedy control, in which yaw misalignment is considered to be 0. In reality, yaw misalignment also occurs in greedy control due to changes in wind direction arising from varying inflow conditions (e.g. turbulence). This paper aims at comparing these two sources of yaw misalignment-naturally changing wind direction versus active yaw in wake steering-in terms of blade root loads. To this end, SCADA data from a real wind farm is used to get yaw misalignment statistics in actual greedy control conditions. FAST.Farm is used to simulate three wind turbines arranged in series, to study maximum and damage-equivalent loads corresponding to in-plane and out-of-plane bending moments on the blades. The results show that compared with actual greedy control, wake steering control reduces the maximum load from the upstream wind turbine, but increases it from other wind turbines. Concerning the damage-equivalent loads from all wind turbines, the blade's in-plane moment is reduced, but the blade's out-of-plane moment is increased.Impact of wind farm wake steering control on blade root loadacceptedVersio
Magnetic and Electrical Properties of Heusler Alloy Co 2 MnSi Thin Films Grown on Ge(001) Substrates via an Al 2 O 3 Tunnel Barrier
Heusler alloy Co 2 MnSi/Al 2 O 3 heterostructures on single-crystal Ge(001) substrates were prepared through magnetron sputtering for both Co 2 MnSi and Al 2 O 3 thin films as a promising candidate for future-generation semiconductor-based spintronic devices. Sufficiently high saturation magnetization 781 emu/cm 3 was obtained for the Co 2 MnSi thin film. Furthermore, the current versus voltage (I-V) characteristics showed that the tunneling conduction was dominant in Co 2 MnSi/Al 2 O 3 (2 nm)/Ge(001) heterostructure and the I-V characteristics were slightly dependent on temperature. The conductance versus voltage (dI/dV-V) characteristics indicated that the potential barrier height at the Co 2 MnSi/Al 2 O 3 interface was almost equal to that at the n-Ge/Al 2 O 3 interface for the prepared Co 2 MnSi/Al 2 O 3 /Ge(001) heterostructure
Magnetic and Electrical Properties of Heusler Alloy Co 2
Heusler alloy Co2MnSi/Al2O3 heterostructures on single-crystal Ge(001) substrates were prepared through magnetron sputtering for both Co2MnSi and Al2O3 thin films as a promising candidate for future-generation semiconductor-based spintronic devices. Sufficiently high saturation magnetization 781 emu/cm3 was obtained for the Co2MnSi thin film. Furthermore, the current versus voltage (I-V) characteristics showed that the tunneling conduction was dominant in Co2MnSi/Al2O3 (2 nm)/Ge(001) heterostructure and the I-V characteristics were slightly dependent on temperature. The conductance versus voltage (dI/dV-V) characteristics indicated that the potential barrier height at the Co2MnSi/Al2O3 interface was almost equal to that at the n-Ge/Al2O3 interface for the prepared Co2MnSi/Al2O3/Ge(001) heterostructure
Novel cost model for balancing wind power forecasting uncertainty
The intermittency of wind generation creates nonlinear uncertainties in wind power forecasting (WPF). Thus, additional operating costs can be incurred for balancing these forecasting deviations. Normally, large wind power penetration requires accurate quantification of the uncertainty-induced costs. This paper defines this type of costs as wind power uncertainty incremental cost (WPUIC) and wind power uncertainty dispatch cost (WPUDC), and it then formulates a general methodology for deriving them based on probabilistic forecasting of wind power. WPUIC quantifies the incremental cost induced from balancing the uncertainties of wind power generation. WPUDC is a balancing cost function with a quadratic form considering diverse external conditions. Besides, the risk probability (RP) of not meeting the scheduled obligation is also modelled. Above models are established based on a newly developed probabilistic forecasting model, varying variance relevance vector machine (VVRVM). Demonstration results show that the VVRVM and RP provide accurate representation of WPF uncertainties and corresponding risk, and thus they can better support and validate the modelling of WPUDC and WPUIC. The proposed cost models have the potential to easily extend traditional dispatches to a new low-carbon system with a high penetration of renewables.</p
Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders
Deep learning methodology contributes a lot to the development of
hyperspectral image (HSI) analysis community. However, it also makes HSI
analysis systems vulnerable to adversarial attacks. To this end, we propose a
masked spatial-spectral autoencoder (MSSA) in this paper under self-supervised
learning theory, for enhancing the robustness of HSI analysis systems. First, a
masked sequence attention learning module is conducted to promote the inherent
robustness of HSI analysis systems along spectral channel. Then, we develop a
graph convolutional network with learnable graph structure to establish global
pixel-wise combinations.In this way, the attack effect would be dispersed by
all the related pixels among each combination, and a better defense performance
is achievable in spatial aspect.Finally, to improve the defense transferability
and address the problem of limited labelled samples, MSSA employs spectra
reconstruction as a pretext task and fits the datasets in a self-supervised
manner.Comprehensive experiments over three benchmarks verify the effectiveness
of MSSA in comparison with the state-of-the-art hyperspectral classification
methods and representative adversarial defense strategies.Comment: 14 pages, 9 figure
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