Application of neural networks in wind power (generation) prediction

Mishra, Alok Kumar; Ramesh, L.

doi:10.1109/supergen.2009.5348160

Cited by 27 publications

(14 citation statements)

References 4 publications

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“…Simulation results validate the accuracy and anti-noise performance of the proposed method, whose estimated wind speed and direction errors converge to zero when the SNR is over 15 dB.Sensors 2020, 20, 523 2 of 16 turbines, which further maximizes wind utilization efficiency [10]. In addition, wind measurement can be used to estimate the corresponding power generation, thus contributing to the system scheduling and energy dispatching, and, ultimately, the integration of wind power in the grid [11]. In summation, precise wind speed and direction measurements in a 3D space are of vital importance to wind energy utilization and the wind power industry.Light detection and ranging (LIDAR) technology is one of the most popular wind measurements [12].…”

supporting

confidence: 54%

Three-Dimensional Wind Measurement Based on Ultrasonic Sensor Array and Multiple Signal Classification

Teng

Zhu

et al. 2020

Sensors

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The wind power industry continues to experience rapid growth worldwide. However, the fluctuations in wind speed and direction complicate the wind turbine control process and hinder the integration of wind power into the electrical grid. To maximize wind utilization, we propose to precisely measure the wind in a three-dimensional (3D) space, thus facilitating the process of wind turbine control. Natural wind is regarded as a 3D vector, whose direction and magnitude correspond to the wind's direction and speed. A semi-conical ultrasonic sensor array is proposed to simultaneously measure the wind speed and direction in a 3D space. As the ultrasonic signal transmitted between the sensors is influenced by the wind and environment noise, a Multiple Signal Classification algorithm is adopted to estimate the wind information from the received signal. The estimate's accuracy is evaluated in terms of root mean square error and mean absolute error. The robustness of the proposed method is evaluated by the type A evaluation of standard uncertainty under a varying signal-to-noise ratio. Simulation results validate the accuracy and anti-noise performance of the proposed method, whose estimated wind speed and direction errors converge to zero when the SNR is over 15 dB.Sensors 2020, 20, 523 2 of 16 turbines, which further maximizes wind utilization efficiency [10]. In addition, wind measurement can be used to estimate the corresponding power generation, thus contributing to the system scheduling and energy dispatching, and, ultimately, the integration of wind power in the grid [11]. In summation, precise wind speed and direction measurements in a 3D space are of vital importance to wind energy utilization and the wind power industry.Light detection and ranging (LIDAR) technology is one of the most popular wind measurements [12]. However, LIDAR can only measure the wind speed component along the line of sight, a disadvantage known as "the Cyclops dilemma" [13,14]. Therefore, the wind distribution measurement in a 3D space would require the very costly installment and deployment of several LIDARs in the wind farm. In contrast, wind measurement sensors are much cheaper and could be deployed around the wind field in distances of miles to provide information for previewing wind measurement. Several types of wind measurement sensors have been employed by researchers, including cup anemometers [15][16][17], thermal anemometers [18][19][20], and ultrasonic anemometers [21][22][23]. However, measurements from cup anemometers often suffer from errors caused by wear and tear on the internal rotating bearings, and frequent inspection or calibration is required to ensure measurement accuracy [24,25]. The performance of cup anemometers can also be affected by the measuring environment. For example, cup anemometers are prone to jamming in humid environments, since water vapor can penetrate their bearings [26]. Thermal anemometers also have difficulties coping with harsh environments. In addition, their sensitivity to changes in the velocity f...

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confidence: 54%

Three-Dimensional Wind Measurement Based on Ultrasonic Sensor Array and Multiple Signal Classification

Teng

Zhu

et al. 2020

Sensors

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“…Short-term wind power prediction [22][23][24][25][26][27][28][29][30][31][32][33][34][35] Wind speed prediction Short-term wind speed forecasting [76][77][78][79][80][81][82][83][84][85][86][87][88] Wind power estimation [89][90][91][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108] Very-short term wind speed prediction [109][110][111][112] Long-term wind speed predicti...…”

Section: A Forecasting and Predictionmentioning

confidence: 99%

“…Yeh et al Above presented researches [89][90][91][92][93][94][95][96][97][98][99] and some other important researches [100][101][102][103][104][105][106][107][108] done on wind power prediction are summarized in Table 9.…”

Section: Wind Power Estimationmentioning

confidence: 99%

RETRACTED: Artificial neural networks applications in wind energy systems: a review

Ata

2015

Renewable and Sustainable Energy Reviews

209

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“…However, it becomes a major issue for large penetration levels as larger quantities of reserves can be difficult to maintain. As a result, forecasting of wind speeds and associated power outputs has received a lot of attention by researchers [1][2][3][4][5][6][7][8][9].This covers both short term (up to 3 hours) and long term forecasting. Statistical methods are often used in short term forecasting [5,7] while numerical weather prediction methods [8,9] are more effective for long term forecasting.…”

Section: Introductionmentioning

confidence: 99%

Comparison of individual and combined forecasting of wind power output of two wind farms in Western Australia

Banerjee

Jayaweera

Islam

2012

2012 IEEE Power Electronics and Machines in Wind Applications

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Large penetration of wind farms in the electricity grid is expected to be an unknown challenge in the near future. The biggest factor is the intermittency of wind turbine output and the difficulty in forecasting it accurately. A number of studies on wind forecasting have shown that the error of statistical methods increases with forecast horizon. However, combined forecasting of geographically dispersed wind farm output, sees a reduction in error. This paper extends this further by analysis of the confidence interval and uncertainty of forecasts when combined forecasting is used. It is observed that both the forecast error as well as uncertainty is reduced when combined forecasting is used for two wind farms in Western Australia. For a short term forecasting horizon of 6 hours, maximum forecasting error is reduced by 6% while uncertainty is reduced by up to 20%. This is expected to improve if more wind farms are considered.Index Terms-combined wind forecasting, geographical dispersion, Wind power forecasting.

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Application of neural networks in wind power (generation) prediction

Cited by 27 publications

References 4 publications

Three-Dimensional Wind Measurement Based on Ultrasonic Sensor Array and Multiple Signal Classification

Three-Dimensional Wind Measurement Based on Ultrasonic Sensor Array and Multiple Signal Classification

RETRACTED: Artificial neural networks applications in wind energy systems: a review

Comparison of individual and combined forecasting of wind power output of two wind farms in Western Australia

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