Abstract
Grid management is important for energy distribution, system security and market economics, and one of the key
issues is accurate and stable prediction of wind speed for optimal operation and management of wind power connected to the
grid. In this study, a novel two-layer hybrid method termed SSP-BO-LSTM is proposed for ultra-short-term wind speed
prediction, such as four-hour ahead. The first layer is based on the smoothing spline preprocessing (SSP) method to remove nonGaussian and non-stationary volatilities from the high-resolution wind speed series. Then, the processed wind speed data are
predicted four-hour ahead by the long short-term memory (LSTM) model, and a bayesian optimization (BO) algorithm is
presented to optimize the hyperparameters of the LSTM model. To evaluate the performance of the proposed SSP-BO-LSTM
model, a case study of ultra-short-term wind speed prediction is conducted, including three high-resolution wind speed series
from wind turbine measurements. Moreover, six other prediction models are introduced for in-depth comparison, and a
comprehensive analysis is performed. The results show that the proposed model can improve the accuracy of four-hour ahead
prediction by about 8%-35%, proving to be more effective and stable in providing acceptable results compared to the other six
models mentioned in this study.
Key Words
Bayesian optimization; long short-term memory; smoothing spline preprocessing; ultra-short-term prediction;
wind speed
Address
Weicheng Hu:1)Zhejiang Jiangnan Project Management Co., Ltd., Hangzhou, 310007, China
2)State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure, School of Transportation Engineering,
East China Jiaotong University, Nanchang, 330013, China
3)Chongqing Key Laboratory of Wind Engineering and Wind Energy Utilization, School of Civil Engineering,
Chongqing University, Chongqing, 400044, China
Baolong Cheng:Zhejiang Jiangnan Project Management Co., Ltd., Hangzhou, 310007, China
Qingshan Yang:Chongqing Key Laboratory of Wind Engineering and Wind Energy Utilization, School of Civil Engineering,
Chongqing University, Chongqing, 400044, China
Zhenqing Liu:School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
Ziting Yuan:School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang, 330013, China
Ke Li:Chongqing Key Laboratory of Wind Engineering and Wind Energy Utilization, School of Civil Engineering,
Chongqing University, Chongqing, 400044, China
Mingjin Zhang:Department of Bridge Engineering, Southwest Jiaotong University, Chengdu, 610031, China
Abstract
The flashover is one of the common incidents in transmission line systems. The wind-induced swing angle of the
suspension insulator string is the key critical index for flashover incident limit state function. Based on the equivalent static wind
load obtained from Gust Loading Envelope method, the wind-induced swing angle of suspension insulator string could be
explicitly expressed by 10-min mean wind speed and line parameters, with consideration of wind speed correlation along
conductor span. Therefore, the short-term forecast of the 10-min mean wind speed trend will be of great significance to the early
warning of flashover incidents. This study proposes an improved hybrid prediction model based on the secondary data
decomposition technology and neural network optimized by Bat algorithm for short-term multi-step-ahead wind speed
prediction. In the improved hybrid prediction model, the high-frequency components of original wind speed data will be
secondary decomposed because of greater prediction error. Then, the Bat algorithm is used further to optimize the initial weight
and threshold parameters of the neural network to improve prediction accuracy. The accuracy and superiority of the proposed
prediction model are verified by the application example. The obtained prediction results of wind speed will be substituted into
the limit state function of flashover incidents to assess the flashover risk. The results show that the improved hybrid model has
better performance in the multi-step-ahead forecast and can be used for flashover incident early warning.
Key Words
Bat algorithm; BP neural network; flashover incident; limit state function; multi-step-ahead wind speed forecast;
secondary data decomposition
Address
Wenjuan Lou, Weizheng Zhou, Dengguo Wu and Siran Chen:Institute of Structural Engineering, Zhejiang University, Hangzhou, China
Abstract
Aeroelastic instability (i.e., flutter) is a critical issue that threatens the safety of flexible bridges with increasing span
length. As a promising technique for flutter prevention, active aerodynamic control using auxiliary surfaces attached to the
bridge deck (e.g., winglets and flaps) can be utilized to extract the stabilizing forces from the surrounding wind flow.
Conventional controllers for the active aerodynamic control are usually designed using linear model-based schemes [e.g., linear
quadratic regulator (LQR) and H-infinity control]. In addition to suffering from model inaccuracies, the obtained linear
controller may not work well considering the high complexity of the inherently nonlinear wind-bridge-control system. To this
end, this study proposes a nonlinear model-free controller based on deep reinforcement learning for active flutter control of longspan bridges. Specifically, a deep neural network (DNN), with the powerful ability to approximate nonlinear functions, is
introduced to map from the system state (e.g., the motion of bridge deck) to the control command (e.g., reference position of the
actively controlled surface). The DNN weights are obtained by interacting with the wind-bridge-control environment in a trialand-error fashion (hence the explicit model of system dynamics is not required) using reinforcement learning algorithms of deep
deterministic policy gradient (DDPG) due to its ability to tackle continuous actions with high training efficiency. As a proof of
concept, numerical examples on active flutter control of a flat plate and a bridge deck are conducted to demonstrate the good
performance of the proposed scheme.
Key Words
active control; deep neural networks; flutter; long-span bridges; reinforcement learning
Address
Teng Wu:University at Buffalo, Buffalo, NY 14260, USA
Jiachen He:China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan, Hubei 430063, China
Shaopeng Li:University of Florida, Gainesville, FL 32611, USA
Abstract
Improving the accuracy of wind speed predictions is crucial to the scheduling plan and operating stability of the
power grid system. However, few studies utilize the generative adversarial network (GAN) to implement wind speed predictions
considering the influence of other meteorological factors. Additionally, the accuracy of wind speed predictions needs to be
further improved, especially for multi-step wind speed predictions. Subsequently, a novel hybrid wind speed prediction model is
proposed, including four modules: (1) data collection of the weather research and forecasting (WRF) simulation, (2) data
generation of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and GAN with the
generator of bidirectional long short-term memory (BLSTM), (3) an error correction strategy of the CEEMDAN and GANBLSTM, and (4) hyperparameters optimization of the grid search (GS) and particle swarm optimization (PSO). Three datasets
are utilized to validate the forecasting accuracy of the proposed model. The verification results demonstrate that the forecasting
performance of the proposed model outperforms other baseline models. Taking the mean absolute percentage error (MAPE) of
the ten-step prediction for the three datasets as an example, the MAPE values are respectively 0.51%, 0.46%, and 0.55% with
correction, leading to 9.16%, 9.77%, 9.59% lower than those without correction. Above all, the proposed model possesses
excellent wind speed prediction accuracy, especially in multi-step wind speed predictions, due to its lower values of MAPE with
similar coefficients of determination (R2
) values.
Key Words
bidirectional long short-term memory; error correction; generative adversarial network; wind speed prediction;
WRF simulation
Address
Lian Shen:School of Civil Engineering, Changsha University, Changsha 411022, China
Lihua Mi:Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering,
Changsha University of Science & Technology, Changsha,410076, China
Yan Han:1)School of Civil Engineering, Changsha University, Changsha 411022, China
2)Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering,
Changsha University of Science & Technology, Changsha,410076, China
Chunsheng Cai:Department of Bridge Engineering, School of Transportation, Southeast University, Nanjing, 211189, China
Kai Li:Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering,
Changsha University of Science & Technology, Changsha,410076, China
Lidong Wang:Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering,
Changsha University of Science & Technology, Changsha,410076, China
Abstract
Wind speed measurement is one of the most fundamental tasks for multidiscipline applications and plays an
important role in the design and maintenance of modern infrastructures. Wind speed is usually measured using conventional
gauges which require additional connections to sensors or collection boxes, and their complex operating principles make these
devices largely serve only professionals. This study proposed a novel framework associated with a machine learning architecture
to estimate wind speed directly from acoustic signal collected using smartphones. The one-dimensional convolutional network is
employed to characterize the underlying relationship between the frequency domain features of the acoustic signal and wind
speed. An experimental dataset is collected in wind tunnel laboratory in which the wind speed is measured using cobra probe
and the acoustic signal is recorded using smartphone. The influence of encountering direction angle on the 1D-CNN wind speed
measurement model is also discussed, as well as the ability of the model to resist noise. The favorable robustness and
generalization performance of the 1D-CNN model are verified from multiple perspectives, illustrating the feasibility and
practical value of using smartphones to measure wind speed.
Key Words
1D-CNN; acoustic signal; deep learning; smartphone; wind speed prediction
Address
Yang Ling, Zilong Ti, Hengrui You and Yongle Li:National Key Laboratory of Bridge Intelligent and Green Construction, Southwest Jiaotong University, Chengdu 611756, Sichuan, China
