Trio-V Wind Analyzer: A Generic Integral System for Wind Farm Suitability Design and Power Prediction Using Big Data Analytics

2017 ◽  
Vol 140 (5) ◽  
Author(s):  
Dina Fawzy ◽  
Sherin Moussa ◽  
Nagwa Badr
Keyword(s):  
Wind Farm ◽  
Big Data Analytics ◽  
Principal Component ◽  
Wind Farms ◽  
Land Suitability ◽  
Land Evaluation ◽  
Power Prediction ◽  
Integral System ◽  
Data Volume ◽  
Wind Energy Development

A fast-growing worldwide interest is directed toward green energies. Due to the huge costs of wind farms establishment, the location for wind farms should be carefully determined to achieve the optimum return of investment. Consequently, researches have been conducted to investigate land suitability prior to wind plants development. The generated data from the sensors detecting a potential land can be very huge, fast in generation, heterogeneous, and incomplete, which become seriously difficult to process using traditional approaches. In this paper, we propose Trio-V Wind Analyzer (WA) that handles data volume, variety, and veracity to identify the most suitable location for wind energy development in any study area using a modified version of multicriteria evaluation (MCE). It utilizes principal component analysis (PCA) and our proposed Double-Reduction Optimum Apriori (DROA) to analyze most of the environmental, physical, and economical criteria. In addition, Trio-V WA recommends the suitable turbines and proposes the adequate turbines’ layout distribution, predicting the expected power generated based on the recommended turbine’s specifications using a regression technique. Thus, Trio-V WA provides an integral system of land evaluation for potential investment in wind farms. Experiments indicate 80% and 95% average accuracy for land suitability degree and power prediction, respectively, with efficient performance.

scholarly journals Wind Farm NWP Data Preprocessing Method Based on t-SNE

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3622 ◽  
Author(s):  
Jiu Gu ◽  
Yining Wang ◽  
Da Xie ◽  
Yu Zhang
Keyword(s):  
Data Visualization ◽  
Wind Farm ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Principal Component ◽  
Wind Farms ◽  
Data Preprocessing ◽  
Power Prediction ◽  
Big Data Visualization

The operation prediction of wind farms will be accompanied by the need for massive data processing, especially the preprocessing of wind farm meteorological data or numerical weather prediction (NWP). Because NWP data are strongly correlated with wind farm operation, proper processing of NWP data could not only reduce data volume but also improve the correlations of wind farm operation predictions. For this purpose, this paper proposes a data preprocessing algorithm based on t-distributed stochastic neighbor embedding (t-SNE). Firstly, the data collected were normalized to eliminate the influence caused by different dimensions. The t-SNE algorithm is then used to reduce the dimensionality of the NWP data related to wind farm operation. Finally, the wind farm data visualization platform is established. In this paper, 22 index variables in NWP data were taken as objects. The t-SNE method was used to preprocess the NWP historical data of a wind farm, and the results were compared with the results of the principal component analysis (PCA) algorithm. It outperformed PCA in error precision; in addition, t-SNE dimension reduction preprocessing also had a visual effect, which could be applied to big data visualization platforms. A long short-term memory network (LSTM) was used to predict the operation of the wind farm by combining the preprocessed NWP data and the operation data. The simulation results proved that the effect of the preprocessed NWP data based on t-SNE on the wind power prediction was significantly improved.

scholarly journals Wealth of Wind and Visitors: Tourist Industry Attitudes towards Wind Energy Development in Iceland

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 693
Author(s):  
Anna Dóra Sæþórsdóttir ◽  
Margrét Wendt ◽  
Edita Tverijonaite
Keyword(s):  
Land Use ◽  
Wind Energy ◽  
Wind Turbines ◽  
Wind Farm ◽  
Service Providers ◽  
Wind Farms ◽  
Energy Development ◽  
Tourism Industry ◽  
Land Use Conflicts ◽  
Wind Energy Development

The interest in harnessing wind energy keeps increasing globally. Iceland is considering building its first wind farms, but its landscape and nature are not only a resource for renewable energy production; they are also the main attraction for tourists. As wind turbines affect how the landscape is perceived and experienced, it is foreseeable that the construction of wind farms in Iceland will create land use conflicts between the energy sector and the tourism industry. This study sheds light on the impacts of wind farms on nature-based tourism as perceived by the tourism industry. Based on 47 semi-structured interviews with tourism service providers, it revealed that the impacts were perceived as mostly negative, since wind farms decrease the quality of the natural landscape. Furthermore, the study identified that the tourism industry considered the following as key factors for selecting suitable wind farm sites: the visibility of wind turbines, the number of tourists and tourist attractions in the area, the area’s degree of naturalness and the local need for energy. The research highlights the importance of analysing the various stakeholders’ opinions with the aim of mitigating land use conflicts and socioeconomic issues related to wind energy development.

scholarly journals MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 28 ◽  
Author(s):  
Arkaitz Rabanal ◽  
Alain Ulazia ◽  
Gabriel Ibarra-Berastegi ◽  
Jon Sáenz ◽  
Unai Elosegui
Keyword(s):  
Wind Farm ◽  
Principal Component ◽  
Wind Farms ◽  
General Purpose ◽  
Wind Data ◽  
Kolmogorov Smirnov ◽  
Clear Identification ◽  
Novel Method ◽  
Verification Techniques ◽  
First Time

A novel multi-criteria methodology for the identification of defective anemometers is shown in this paper with a benchmarking approach: it is called MIDAS: multi-technique identification of defective anemometers. The identification of wrong wind data as provided by malfunctioning devices is very important, because the actual power curve of a wind turbine is conditioned by the quality of its anemometer measurements. Here, we present a novel method applied for the first time to anemometers’ data based on the kernel probability density function and the recent reanalysis ERA5. This estimation improves classical unidimensional methods such as the Kolmogorov–Smirnov test, and the use of the global ERA5’s wind data as the first benchmarking reference establishes a general method that can be used anywhere. Therefore, adopting ERA5 as the reference, this method is applied bi-dimensionally for the zonal and meridional components of wind, thus checking both components at the same time. This technique allows the identification of defective anemometers, as well as clear identification of the group of anemometers that works properly. After that, other verification techniques were used versus the faultless anemometers (Taylor diagrams, running correlation and R M S E , and principal component analysis), and coherent results were obtained for all statistical techniques with respect to the multidimensional method. The developed methodology combines the use of this set of techniques and was able to identify the defective anemometers in a wind farm with 10 anemometers located in Northern Europe in a terrain with forests and woodlands. Nevertheless, this methodology is general-purpose and not site-dependent, and in the future, its performance will be studied in other types of terrain and wind farms.

Forecast of Short-Term Wind Power Based on GA Optimized Elman Neural Network

2014 ◽  
Vol 536-537 ◽  
pp. 470-475
Author(s):  
Ye Chen
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Prediction ◽  
Elman Neural Network ◽  
Wind Power Prediction ◽  
Forecasting Method ◽  
Safety And Stability

Due to the features of being fluctuant, intermittent, and stochastic of wind power, interconnection of large capacity wind farms with the power grid will bring about impact on the safety and stability of power systems. Based on the real-time wind power data, wind power prediction model using Elman neural network is proposed. At the same time in order to overcome the disadvantages of the Elman neural network for easily fall into local minimum and slow convergence speed, this paper put forward using the GA algorithm to optimize the weight and threshold of Elman neural network. Through the analysis of the measured data of one wind farm, shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

scholarly journals Wind Energy Development in India and a Methodology for Evaluating Performance of Wind Farm Clusters

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Sanjeev H. Kulkarni ◽  
Tumkur Ramakrishnarao Anil ◽  
Rajakumar Dyamenally Gowdar
Keyword(s):  
Wind Energy ◽  
Power Plants ◽  
Wind Farm ◽  
Pairwise Comparison ◽  
Wind Farms ◽  
Analytic Hierarchy ◽  
Healthy Environment ◽  
Indian State ◽  
Wind Energy Development ◽  
Hierarchy Process

With maturity of advanced technologies and urgent requirement for maintaining a healthy environment with reasonable price, India is moving towards a trend of generating electricity from renewable resources. Wind energy production, with its relatively safer and positive environmental characteristics, has evolved from a marginal activity into a multibillion dollar industry today. Wind energy power plants, also known as wind farms, comprise multiple wind turbines. Though there are several wind-mill clusters producing energy in different geographical locations across the world, evaluating their performance is a complex task and is an important focus for stakeholders. In this work an attempt is made to estimate the performance of wind clusters employing a multicriteria approach. Multiple factors that affect wind farm operations are analyzed by taking experts opinions, and a performance ranking of the wind farms is generated. The weights of the selection criteria are determined by pairwise comparison matrices of the Analytic Hierarchy Process (AHP). The proposed methodology evaluates wind farm performance based on technical, economic, environmental, and sociological indicators. Both qualitative and quantitative parameters were considered. Empirical data were collected through questionnaire from the selected wind farms of Belagavi district in the Indian State of Karnataka. This proposed methodology is a useful tool for cluster analysis.

scholarly journals M2GSNet: Multi-Modal Multi-Task Graph Spatiotemporal Network for Ultra-Short-Term Wind Farm Cluster Power Prediction

2020 ◽  
Vol 10 (21) ◽  
pp. 7915
Author(s):  
Hang Fan ◽  
Xuemin Zhang ◽  
Shengwei Mei ◽  
Kunjin Chen ◽  
Xinyang Chen
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Farms ◽  
Task Graph ◽  
Probabilistic Prediction ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction

Ultra-short-term wind power prediction is of great importance for the integration of renewable energy. It is the foundation of probabilistic prediction and even a slight increase in the prediction accuracy can exert significant improvement for the safe and economic operation of power systems. However, due to the complex spatiotemporal relationship and the intrinsic characteristic of nonlinear, randomness and intermittence, the prediction of regional wind farm clusters and each wind farm’s power is still a challenge. In this paper, a framework based on graph neural network and numerical weather prediction (NWP) is proposed for the ultra-short-term wind power prediction. First, the adjacent matrix of wind farms, which are regarded as the vertexes of a graph, is defined based on geographical distance. Second, two graph neural networks are designed to extract the spatiotemporal feature of historical wind power and NWP information separately. Then, these features are fused based on multi-modal learning. Third, to enhance the efficiency of prediction method, a multi-task learning method is adopted to extract the common feature of the regional wind farm cluster and it can output the prediction of each wind farm at the same time. The cases of a wind farm cluster located in Northeast China verified that the accuracy of a regional wind farm cluster power prediction is improved, and the time consumption increases slowly when the number of wind farms grows. The results indicate that this method has great potential to be used in large-scale wind farm clusters.

Design and Implementation of Wind Farm’s Power Prediction Web System

2013 ◽  
Vol 385-386 ◽  
pp. 1066-1069
Author(s):  
Yan Bo Jia
Keyword(s):  
Wind Farm ◽  
Web Design ◽  
Wind Farms ◽  
Web Security ◽  
Power Prediction ◽  
Development Platform ◽  
Web System ◽  
Wind Power Prediction ◽  
Data Persistence ◽  
The Web

In this paper, a web system for Wind farm power prediction is presented. By using annotation-based Spring 3 platform and technologies including FreeMarker and JFreechart, process of web design is simplified and quickly finished. In addition, the web system can be easily maintained and expanded. Finally, the establishment of wind power prediction web system is convenient for bidding of power market and maintenance of wind farms.Usually, wind farms are located in remote area and wind turbines belonging to the same wind farm are not in the same area. It is difficult for the dispatching and the management departments which are far away from wind farm to obtain data, such as power generation and the changing wind speed. So, it is necessary to build a distributional, open and real-time web system to predict power of wind farms.As the development of Internet and web technology, it is easier to apply web according to different and complex enterprises demands. And the focus of web design was to make application more convenient, more powerful and more flexible. In this paper, in order to design the forecasting web system, the lightweight container Spring3 was taken as a development platform, Spring MVC as web framework, Spring JPA as data persistence technology, Spring Security3 as Web security management. Moreover, FreeMarker was adopted as view template and JFreeChart as figure display tool. The web system provides the dispatching and the management departments with an information sharing platform which is safe, efficient, stable, extendible and easy to be maintained.

Research on the Allocated Capacity of Energy Storage Based on Adjustment of SOC

2014 ◽  
Vol 687-691 ◽  
pp. 3502-3507
Author(s):  
Wu Chang Dai ◽  
Yu Dong ◽  
Xin Fei Zhao ◽  
Xian Jun Shang
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Wind Farm ◽  
Traditional Approach ◽  
Wind Farms ◽  
Electrical Energy ◽  
Power Prediction ◽  
New Approach ◽  
Grid Connection ◽  
High Level

In order to remedy errors in wind power prediction, a common method adopted in wind farms is the configuration of a certain capacity of energy storage at the grid interface. The traditional approach for configuring capacity ignores the problems of excessive allocation and the adjustment of SOC, and for this reason, the paper proposes a model of wind power coupling by adding a resistance load at the grid connection point. In addition, this paper proposes a control strategy to improve the operation of energy storage systems through the adjustment of SOC in order to maintain a high level of SOC, and proposes a new approach to deploying storage capacity based on this. According to the analysis of a wind farm in Northeast China, a new approach to the configuration of the energy storage can use local utility of a little electrical energy to reduce the allocated capacity, and furthermore, this method also has better control characteristics.

scholarly journals Design and analysis of a spatially heterogeneous wake

2020 ◽  
Author(s):  
Alayna Farrell ◽  
Jennifer King ◽  
Caroline Draxl ◽  
Rafael Mudafort ◽  
Nicholas Hamilton ◽  
...  
Keyword(s):  
Flow Field ◽  
Wind Farm ◽  
Wind Farms ◽  
Weather Conditions ◽  
Atmospheric Conditions ◽  
Power Prediction ◽  
Atmospheric Measurements ◽  
Flow Field Characteristics ◽  
Spatially Variant ◽  
Wake Modeling

Abstract. Methods of turbine wake modeling are being developed to more accurately account for spatially variant atmospheric conditions within wind farms. Most current wake modeling utilities are designed to apply a uniform flow field to the entire domain of a wind farm. When this method is used, the accuracy of power prediction and wind farm controls can be compromised depending on the flow-field characteristics of a particular area. In an effort to improve strategies of wind farm wake modeling and power prediction, FLOw Redirection and Induction in Steady State (FLORIS) was developed to implement sophisticated methods of atmospheric characterization and power output calculation. In this paper, we describe an adapted FLORIS model that features spatial heterogeneity in flow-field characterization. This model approximates an observed flow field by interpolating from a set of atmospheric measurements that represent local weather conditions. The adaptations were validated by comparing the simulated power predictions generated from FLORIS to the actual recorded wind farm output from the Supervisory Control And Data Acquisition (SCADA) recordings. This work quantifies the accuracy of wind plant power predictions under heterogeneous flow conditions and establishes best practices for atmospheric surveying for wake modeling.

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