A two-stage wind speed model for multiple wind farms considering autocorrelations and cross-correlations

2016 ◽  
Author(s):  
Kaigui Xie ◽  
Shuwei Miao ◽  
Yun Xia ◽  
Yinghao Ma ◽  
Yanlin Li
Keyword(s):  
Wind Speed ◽  
Wind Farms ◽  
Two Stage ◽  
Cross Correlations

scholarly journals A New Wind Speed Forecasting Modeling Strategy Using Two-Stage Decomposition, Feature Selection and DAWNN

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 334 ◽  
Author(s):  
Sizhou Sun ◽  
Lisheng Wei ◽  
Jie Xu ◽  
Zhenni Jin
Keyword(s):  
Feature Selection ◽  
Wind Speed ◽  
Wind Farms ◽  
Frequency Component ◽  
Ensemble Empirical Mode Decomposition ◽  
Two Stage ◽  
Wind Speed Forecasting ◽  
Wind Speed Data ◽  
Wind Speed Prediction ◽  
Mode Decomposition

Accurate wind speed prediction plays a crucial role on the routine operational management of wind farms. However, the irregular characteristics of wind speed time series makes it hard to predict accurately. This study develops a novel forecasting strategy for multi-step wind speed forecasting (WSF) and illustrates its effectiveness. During the WSF process, a two-stage signal decomposition method combining ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) is exploited to decompose the empirical wind speed data. The EEMD algorithm is firstly employed to disassemble wind speed data into several intrinsic mode function (IMFs) and one residual (Res). The highest frequency component, IMF1, obtained by EEMD is further disassembled into different modes by the VMD algorithm. Then, feature selection is applied to eliminate the illusive components in the input-matrix predetermined by partial autocorrelation function (PACF) and the parameters in the proposed wavelet neural network (WNN) model are optimized for improving the forecasting performance, which are realized by hybrid backtracking search optimization algorithm (HBSA) integrating binary-valued BSA (BBSA) with real-valued BSA (RBSA), simultaneously. Combinations of Morlet function and Mexican hat function by weighted coefficient are constructed as activation functions for WNN, namely DAWNN, to enhance its regression performance. In the end, the final WSF values are obtained by assembling the prediction results of each decomposed components. Two sets of actual wind speed data are applied to evaluate and analyze the proposed forecasting strategy. Forecasting results, comparisons, and analysis illustrate that the proposed EEMD/VMD-HSBA-DAWNN is an effective model when employed in multi-step WSF.

scholarly journals Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2319
Author(s):  
Hyun-Goo Kim ◽  
Jin-Young Kim
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Free Stream ◽  
Turbine Blades ◽  
Wind Farms ◽  
Lidar Measurement ◽  
Wind Turbine Blades ◽  
Wake Effect

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

scholarly journals A Novel Information Theoretical Criterion for Climate Network Construction

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1500
Author(s):  
Sara Cornejo-Bueno ◽  
Mihaela I. Chidean ◽  
Antonio J. Caamaño ◽  
Luis Prieto-Godino ◽  
Sancho Salcedo-Sanz
Keyword(s):  
Wind Speed ◽  
Wind Farms ◽  
Construction Method ◽  
Membership Probability ◽  
Network Construction ◽  
Wind Speed Prediction ◽  
Emergent Behaviour ◽  
Symmetry Properties ◽  
Leibler Divergence ◽  
Speed Prediction

This paper presents a novel methodology for Climate Network (CN) construction based on the Kullback-Leibler divergence (KLD) among Membership Probability (MP) distributions, obtained from the Second Order Data-Coupled Clustering (SODCC) algorithm. The proposed method is able to obtain CNs with emergent behaviour adapted to the variables being analyzed, and with a low number of spurious or missing links. We evaluate the proposed method in a problem of CN construction to assess differences in wind speed prediction at different wind farms in Spain. The considered problem presents strong local and mesoscale relationships, but low synoptic scale relationships, which have a direct influence in the CN obtained. We carry out a comparison of the proposed approach with a classical correlation-based CN construction method. We show that the proposed approach based on the SODCC algorithm and the KLD constructs CNs with an emergent behaviour according to underlying wind speed prediction data physics, unlike the correlation-based method that produces spurious and missing links. Furthermore, it is shown that the climate network construction method facilitates the evaluation of symmetry properties in the resulting complex networks.

scholarly journals Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements

2020 ◽  
Vol 12 (6) ◽  
pp. 2467 ◽  
Author(s):  
Fei Zhao ◽  
Yihan Gao ◽  
Tengyuan Wang ◽  
Jinsha Yuan ◽  
Xiaoxia Gao
Keyword(s):  
Wind Speed ◽  
Wind Shear ◽  
Wind Farm ◽  
Wind Farms ◽  
Field Measurements ◽  
Development Trend ◽  
Asymmetric Distribution ◽  
Lidar Measurements ◽  
Wind Characteristics ◽  
The Development Trend

To study the wake development characteristics of wind farms in complex terrains, two different types of Light Detection and Ranging (LiDAR) were used to conduct the field measurements in a mountain wind farm in Hebei Province, China. Under two different incoming wake conditions, the influence of wind shear, terrain and incoming wind characteristics on the development trend of wake was analyzed. The results showed that the existence of wind shear effect causes asymmetric distribution of wind speed in the wake region. The relief of the terrain behind the turbine indicated a subsidence of the wake centerline, which had a linear relationship with the topography altitudes. The wake recovery rates were calculated, which comprehensively validated the conclusion that the wake recovery rate is determined by both the incoming wind turbulence intensity in the wake and the magnitude of the wind speed.

Selection of Input Variables for the Prediction of Wind Speed in Wind Farms Based on Genetic Algorithm

2011 ◽  
Vol 35 (6) ◽  
pp. 649-660 ◽  
Author(s):  
R. A. Gupta ◽  
Rajesh Kumar ◽  
Ajay Kumar Bansal
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Wind Farms ◽  
Input Variables ◽  
Selection Of

scholarly journals Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4291
Author(s):  
Paxis Marques João Roque ◽  
Shyama Pada Chowdhury ◽  
Zhongjie Huan
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Operating Conditions ◽  
Wake Effect ◽  
Wind Generators ◽  
Wind Potential

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system’s power output as a result of the wake effect. For any wind farm, it is evident that wind turbines’ wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha’s electricity demand was carried out in order to obtain the district’s daily load profile required to size the wind farm’s capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators’ placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm’s capacity, the district’s energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8Do and 10Do, respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km2, and power output estimated at 71.844 GWh per year.

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