Wind speed trends and the potential of electricity generation at new wind power plants in Northeast Brazil

2021 ◽  
Vol 43 (4) ◽  
Author(s):  
Amanda Ribeiro de Andrade ◽  
Victor Felipe Moura Bezerra Melo ◽  
Daisy Beserra Lucena ◽  
Raphael Abrahão
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Electricity Generation ◽  
Power Plants ◽  
Northeast Brazil ◽  
Wind Power Plants

Wind Speed Forecasting Based on FNN in Wind Farm

2014 ◽  
Vol 651-653 ◽  
pp. 1117-1122
Author(s):  
Zheng Ning Fu ◽  
Hong Wen Xie
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Power Plants ◽  
Fuzzy Neural Network ◽  
Negative Impact ◽  
Wind Speed Forecasting ◽  
Wind Power Plants ◽  
Fuzzy Neural

Wind speed forecasting plays a significant role to the operation of wind power plants and power systems. An accurate forecasting on wind power can effectively relieve or avoid the negative impact of wind power plants on power systems and enhance the competition of wind power plants in electric power market. Based on a fuzzy neural network (FNN), a method of wind speed forecasting is presented in this paper. By mining historical data as the learning stylebook, the fuzzy neural network (FNN) forecasts the wind speed. The simulation results show that this method can improve the accuracy of wind speed forecasting effectively.

Short-Term Power Fluctuations of Large Wind Power Plants*

2002 ◽  
Vol 124 (4) ◽  
pp. 427-431 ◽  
Author(s):  
Yih-huei Wan ◽  
Demy Bucaneg,
Keyword(s):  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Power Plants ◽  
Power Level ◽  
Short Term ◽  
Wind Power Plants ◽  
Weather Events ◽  
Power Fluctuations ◽  
Large Wind

To evaluate short-term wind power fluctuations and their impact on electric power systems, the National Renewable Energy Laboratory, in cooperation with Enron Wind, has started a project to record output power from several large commercial wind power plants at the 1-Hertz rate. This paper presents statistical properties of the data collected so far and discusses the results of data analysis. From the available data, we can already conclude that despite the stochastic nature of wind power fluctuations, the magnitudes and rates of wind power changes caused by wind speed variations are seldom extreme, nor are they totally random. Their values are bounded in narrow ranges. Power output data also show significant spatial variations within a large wind power plant. The data also offer encouraging evidence that accurate wind power forecasting is feasible. To the utility system, large wind power plants are not really random burdens. The narrow range of power level step changes provides a lot of information with which system operators can make short-term predictions of wind power. Large swings of wind power do occur, but those infrequent large changes (caused by wind speed changes) are always related to well-defined weather events, most of which can be accurately predicted in advance.

scholarly journals ANALYSIS OF TECHNICAL AND ECONOMIC EFFICIENCY OF OPERATION OF THE WIND POWER PLANTS IN CRIMEA

2021 ◽  
pp. 31-41
Author(s):  
E. A. Bekirov ◽  
S. N. Voskresenskaya ◽  
V. V. Potenko
Keyword(s):  
Power Plant ◽  
Wind Power ◽  
Wind Turbines ◽  
Electricity Generation ◽  
Power Plants ◽  
Wind Farm ◽  
Net Income ◽  
Wind Power Plants ◽  
Cost Of Energy ◽  
The Cost

The article provides data on the generation and consumption of electricity by a wind farm. To maintain the operability of the wind farm, it is connected to the general grid of the power system, not only for the output of generated electricity, but also for the consumption of the necessary electricity to start the operation of wind turbines. Electricity generation, payback and net profit of a wind power plant of 12 wind turbines were estimated. Subject of study. Wind power plants and their efficiency. Materials and methods. The theoretical and methodological basis is the works of domestic and foreign scientists in the field of wind energy. In the work, analytical research methods were used, including predictive calculation of the annual energy production of wind turbines. Conclusions. The instability of electricity generation using renewable energy generating units is a serious problem that affects the cost of energy produced. According to the calculations, in 14 years, provided the electricity price is equal to 1.8 rubles, the power plant will recoup the investment and begin to generate net income. The correlation coefficient was determined, which was 0.94.

scholarly journals Development of Nonlinear Optimization Models for Wind Power Plants Using Box-Behnken Design of Experiment: A Case Study for Turkey

2020 ◽  
Vol 12 (15) ◽  
pp. 6017
Author(s):  
Yasemin Ayaz Atalan ◽  
Mete Tayanç ◽  
Kamil Erkan ◽  
Abdulkadir Atalan
Keyword(s):  
Wind Speed ◽  
Nonlinear Optimization ◽  
Wind Power ◽  
Design Of Experiment ◽  
Power Plants ◽  
Energy Output ◽  
Optimization Models ◽  
Box Behnken Design ◽  
Wind Power Plants ◽  
The Future

This study aims to develop an optimization model for obtaining the maximum benefit from wind power plants (WPPs) to help with reducing external dependence in terms of energy. In this sense, design of experiment and optimization methods are comprehensively combined in the wind energy field for the first time. Existing data from installed WPPs operating in Turkey for the years of 2017 and 2018 are analyzed. Both the individual and interactive effects of controllable factors, namely turbine power (MW), hub height (m) and rotor diameter (m), and uncontrollable factor as wind speed (m/s) on WPPs are investigated with the help of Box-Behnken design. Nonlinear optimization models are utilized to estimate optimum values for each decision variable in order to maximize the amount of energy to be produced for the future. Based on the developed nonlinear optimization models, the optimum results with high desirability value (0.9587) for the inputs of turbine power, hub height, rotor diameter and wind speed are calculated as 3.0670 MW, 108.8424 m, 106.7597 m, and 6.1684 m/s, respectively. The maximum energy output with these input values is computed as 9.952 million kWh per unit turbine, annually. The results of this study can be used as a guideline in the design of new WPPs to produce the maximum amount of energy contributing to supply escalating energy needs by more sustainable and clean ways for the future.

Improved frequency control from wind power plants considering wind speed variation

2016 ◽  
Author(s):  
Jayachandra N. Sakamuri ◽  
Kaushik Das ◽  
Mufit Altin ◽  
Nicolaos A. Cutululis ◽  
Anca D. Hansen ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Plants ◽  
Frequency Control ◽  
Speed Variation ◽  
Wind Power Plants

scholarly journals Monitoring Turbin Angin Menggunakan Smartphone Android

2020 ◽  
Vol 6 (2) ◽  
pp. 64
Author(s):  
Randy Yonanda Pratama ◽  
Muldi Yuhendri
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Wind Turbines ◽  
Power Plants ◽  
Rotation Speed ◽  
Measuring Instrument ◽  
Horizontal Axis Wind Turbine ◽  
Maximum Capacity ◽  
Wind Power Plants

Wind turbines function as producers of mechanical power to drive generators in wind power plants. One factor that needs to be considered in the operation of wind turbines is the maximum capacity of the generator. Wind turbines must operate below the generator rating so as not to cause damage to the generator. Therefore, the operation of the wind turbine needs to be monitored and controlled to keep it operating within the generator rating limits. In this paper a horizontal axis wind turbine monitoring sistem is proposed using an Android smartphone. Wind turbine monitoring includes wind speed and turbine rotation speed parameters. This parameter data is obtained from sensors that are processed with Arduino Mega 2560. Data from Arduino is sent via the Bluetooth HC-04 module to be displayed on an Android smartphone. The experimental results show that the proposed wind turbine monitoring system has worked well. This can be seen from the wind speed and turbine rotation data that is displayed on android is exactly the same as the data on the measuring instrument

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