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.

Technical and Economical Investigation of Wind Power Potential in Kuwait

2015 ◽  
Author(s):  
Yousef A. Gharbia ◽  
Haytham Ayoub
Keyword(s):  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Data ◽  
Electricity Production ◽  
Specific Power ◽  
Wind Rose ◽  
Average Wind Speed ◽  
Prevailing Wind Direction ◽  
Power Curves

The State of Kuwait is considering diversifying its energy sector and not entirely depend on oil. This desire is motivated by Kuwait commitment to reducing its share of pollution, as a result of burning fossil fuel, and to extend the life of its oil and gas reserves. The potential for solar energy in Kuwait is quite obvious; however, it is not the case when it comes to wind energy. The aim of this work was to analyze wind data from several sites in Kuwait and assess their suitability for building large-scale wind farms. The analysis of hourly averaged wind data showed that some sites can have an average wind speed as high as 5.3 m/s at 10 m height. The power density using Weibull distribution function was calculated for the most promising sites. The prevailing wind direction for these sites was also determined using wind-rose charts. The power curves of several Gamesa turbines were used in order to identify the best turbine model in terms of specific power production cost. The results showed that the area of Abraq Al-Habari has the highest potential for building a large-scale wind farm. The payback period of investments was found to be around 7 years and the cost of electricity production was around US Cent 4/kWh.

scholarly journals Creating Synthetic Wind Speed Time Series for 15 New Zealand Wind Farms

2011 ◽  
Vol 50 (12) ◽  
pp. 2394-2409 ◽  
Author(s):  
Richard Turner ◽  
Xiaogu Zheng ◽  
Neil Gordon ◽  
Michael Uddstrom ◽  
Greg Pearson ◽  
...  
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Wind Speed ◽  
Wind Farm ◽  
Central Government ◽  
Wind Farms ◽  
Practical Method ◽  
Wind Data ◽  
Regulatory Oversight ◽  
Short Period

AbstractWind data at time scales from 10 min to 1 h are an important input for modeling the performance of wind farms and their impact on many countries’ national electricity systems. Planners need long-term realistic (i.e., meteorologically spatially and temporally consistent) wind-farm data for projects studying how best to integrate wind power into the national electricity grid. In New Zealand, wind data recorded at wind farms are confidential for commercial reasons, however, and publicly available wind data records are for sites that are often not representative of or are distant from wind farms. In general, too, the public sites are at much lower terrain elevations than hilltop wind farms and have anemometers located at 10 m above the ground, which is much lower than turbine hub height. In addition, when available, the mast records from wind-farm sites are only for a short period. In this paper, the authors describe a novel and practical method to create a multiyear 10-min synthetic wind speed time series for 15 wind-farm sites throughout the country for the New Zealand Electricity Commission. The Electricity Commission (known as the Electricity Authority since 1 October 2010) is the agency that has regulatory oversight of the electricity industry and that provides advice to central government. The dataset was constructed in such a way as to preserve meteorological realism both spatially and temporally and also to respect the commercial secrecy of the wind data provided by power-generation companies.

scholarly journals OFFSHORE WIND FARM MACRO AND MICRO SITING PROTOCOL APPLICATION TO RHODE ISLAND

2012 ◽  
Vol 1 (33) ◽  
pp. 73 ◽  
Author(s):  
Annette Renee Grilli ◽  
Malcolm Spaulding ◽  
Christopher O'Reilly ◽  
Gopu Potty
Keyword(s):  
Rhode Island ◽  
Wind Farm ◽  
Cost Benefit ◽  
Principal Component ◽  
Wind Farms ◽  
Management Plan ◽  
Offshore Wind ◽  
Multivariate Statistical ◽  
Offshore Wind Farms ◽  
Special Area

Since 2008, the Rhode Island (RI) Coastal Resources Management Council has been leading the development of an Ocean Special Area Management Plan (Ocean SAMP), in partnership with the University of Rhode Island, resulting in an extensive multidisciplinary analysis of the Rhode Island offshore environment and its suitability to site offshore wind farms. As part of SAMP, a comprehensive macro-siting optimization tool: the Wind Farm Siting Index (WIFSI), integrating technical, societal, and ecological constraints, was developed within the conceptual framework of ecosystem services. WIFSI uses multivariate statistical analyses (principal component and k-means cluster analyses) to define homogeneous regions, which integrate and balance ecological and societal constraints as part of a Cost/Benefit tool. More recently, a Wind Farm micro-Siting Optimization Tool was developed (WIFSO), which uses a genetic algorithm to derive the optimal layout of a wind farm sited within one of the macro-siting selected regions. In this work, we present an overview of the current state of development of the integrated macro- and micro- siting tools.

scholarly journals Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments

2020 ◽  
Vol 5 (1) ◽  
pp. 245-257 ◽  
Author(s):  
Joeri Alexis Frederik ◽  
Robin Weber ◽  
Stefano Cacciola ◽  
Filippo Campagnolo ◽  
Alessandro Croce ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Wind Farm ◽  
Traditional Approach ◽  
Wind Farms ◽  
Simulation Studies ◽  
Proof Of Concept ◽  
Simulation Environment ◽  
Wind Tunnel Experiments ◽  
Alternative Approach ◽  
First Time

Abstract. As wind turbines in a wind farm interact with each other, a control problem arises that has been extensively studied in the literature: how can we optimize the power production of a wind farm as a whole? A traditional approach to this problem is called induction control, in which the power capture of an upstream turbine is lowered for the benefit of downstream machines. In recent simulation studies, an alternative approach, where the induction factor is varied over time, has shown promising results. In this paper, the potential of this dynamic induction control (DIC) approach is further investigated. Only periodic variations, where the input is a sinusoid, are studied. A proof of concept for this periodic DIC approach will be given by the execution of scaled wind tunnel experiments, showing for the first time that this approach can yield power gains in real-world wind farms. Furthermore, the effects on the damage equivalent loads (DEL) of the turbine are evaluated in a simulation environment. These indicate that the increase in DEL on the excited turbine is limited.

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 Higher Wind: Highlighted Expansion Opportunities to Repower Wind Energy

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7716
Author(s):  
Francisco Haces-Fernandez
Keyword(s):  
Wind Energy ◽  
Wind Farm ◽  
Wind Farms ◽  
Energy Generation ◽  
The United States ◽  
Wind Speeds ◽  
Wind Energy Generation ◽  
Novel Method ◽  
Renewable Energy Generation ◽  
Near Future

Decarbonizing the world economy, before the most damaging effects of climate change become irreversible, requires substantially increasing renewable energy generation in the near future. However, this may be challenging in mature wind energy markets, where many advantageous wind locations are already engaged by older wind farms, potentially generating suboptimal wind harvesting. This research developed a novel method to systematically analyze diverse factors to determine the level of maturity of wind markets and evaluate the adequacy of wind farm repowering at regional and individual levels. The approach was applied to wind markets in the United States (U.S.), in which several states were identified as having diverse levels of maturity. Results obtained from case studies in Texas indicated a consequential number of wind farms that have reached their twenty-year end-of-life term and earlier obsolescence levels. The proposed approach aided in determining wind farms that may benefit from total or partial repowering. The method indicated that total repowering of selected installations would significantly increase overall wind energy generation, considering that these older installations have access to some of the best wind speeds, infrastructure and areas to grow. The proposed method can be applied to different world wind markets.

scholarly journals Mean Wind Speed Comparison and Wind Farm Energy Prediction at Chang-Hua Offshore (Taiwan)

2016 ◽  
Author(s):  
Meharkumar Barapati ◽  
Jiun-Jih Miau ◽  
Pei-Chi Chang
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Stable Condition ◽  
Wind Farms ◽  
Green Energy ◽  
Offshore Wind ◽  
Wind Data ◽  
Electricity Production ◽  
Average Roughness ◽  
Mean Wind Speed

Taiwan developing offshore wind power to promote green energy and self-electricity production. In this study, a Light Detection and Ranging (Lidar) was set up at Chang-Hua development zone one on the sea and 10km away from the seashore. At Lidar location, WRF (3.33km & 2km grid lengths) model and WAsP were used to simulate the wind speed at various elevations. Three days mean wind speed of simulated results were compared with Lidar data. From the four wind data sets, developed five different comparisons to find an error% and R-Squared values. Comparison between WAsP and Floating Lidar was shown good consistency. Lukang meteorological station 10 years wind observations at 5m height were used for wind farm energy predictions. The yearly variation of energy predictions of traditional and TGC wind farm layouts are compared under purely neutral and stable condition. The one-year cycle average surface heat flux over the Taiwan Strait is negative (-72.5 (W/m2) and 157.13 STD), which represents stable condition. At stable condition TGC (92.39%) and 600(92.44%), wind farms were shown higher efficiency. The Fuhai met mast wind data was used to estimate roughness length and power law exponent. The average roughness lengths are very small and unstable atmosphere.

Problematika priključenja vetroelektrana na prenosnu mrežu EMS ad

2020 ◽  
Vol 22 (1-2) ◽  
pp. 44-49
Author(s):  
Saša Zdravković ◽  
Milan Blažić ◽  
Branko Šumonja ◽  
Marija Đorđević ◽  
Julijana Vićovac ◽  
...  
Keyword(s):  
Wind Farm ◽  
Electrical Power ◽  
Geographical Area ◽  
Wind Farms ◽  
Real Data ◽  
Electrical Power System ◽  
Farm Production ◽  
Short Period ◽  
Production Capacities ◽  
First Time

in 2018 the first connections of wind farms to the transmission network EMS have been realized. In a short period of time, the total of four (4) wind farms were put into operation and, therefore, with engaging a lot of employees, it was necessary to modify and adapt a number of procedures and regulations in order to conclude all the Wind Energy Exploitation Agreements. Due to the stochasticity of the wind phenomenon, the forecast of wind farm production and its relation to the achieved energy production shall be addressed. As the employees of JSC EMS meet for the first time with this type of production capacities, the paper contains real data on meteorological parameters that influence the production of electricity from wind farms. Since all four (4) wind farms are located in the same geographical area, their impact on power flows, operational planning and balancing of the electrical power system shall be analyzed.

Evaluating Community Engagement and Benefit-Sharing Practices in Australian Wind Farm Development

2017 ◽  
Vol 1 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Nina Lansbury Hall ◽  
Jarra Hicks ◽  
Taryn Lane ◽  
Emily Wood
Keyword(s):  
Community Engagement ◽  
Community Involvement ◽  
Wind Farm ◽  
Social Acceptance ◽  
Wind Farms ◽  
Clean Energy ◽  
Benefit Sharing ◽  
Delphi Panel ◽  
Government Organizations

The wind industry is positioned to contribute significantly to a clean energy future, yet the level of community opposition has at times led to unviable projects. Social acceptance is crucial and can be improved in part through better practice community engagement and benefit-sharing. This case study provides a “snapshot” of current community engagement and benefit-sharing practices for Australian wind farms, with a particular emphasis on practices found to be enhancing positive social outcomes in communities. Five methods were used to gather views on effective engagement and benefit-sharing: a literature review, interviews and a survey of the wind industry, a Delphi panel, and a review of community engagement plans. The overarching finding was that each community engagement and benefit-sharing initiative should be tailored to a community’s context, needs and expectations as informed by community involvement. This requires moving away from a “one size fits all” approach. This case study is relevant to wind developers, energy regulators, local communities and renewable energy-focused non-government organizations. It is applicable beyond Australia to all contexts where wind farm development has encountered conflicted societal acceptance responses.

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