Verification of Prediction Method Based on Machine Learning under Wake Effect Using Real-Time Digital Simulator

Rae-Jin Park, Jeong-Hwan Kim, Byungchan Yoo, Minhan Yoon () and Seungmin Jung ()
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Rae-Jin Park: Korea Electric Power Corporation Research Institute, Naju-si 58322, Republic of Korea
Jeong-Hwan Kim: Department of Electrical Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
Byungchan Yoo: Department of Electrical Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
Minhan Yoon: Department of Electrical Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
Seungmin Jung: Department of Electrical Engineering, Hanbat National University, Daejeon 34158, Republic of Korea

Energies, 2022, vol. 15, issue 24, 1-15

Abstract: With the increase in the penetration rate of renewable energy sources, a machine-learning-based forecasting system has been introduced to the grid sector to improve the participation rate in the electricity market and reduce energy losses. In these studies, correlation analysis of mechanical and environmental variables, including geographical figures, is considered a crucial point to increase the prediction’s accuracy. Various models have been applied in terms of accuracy, speed calculation, and amount of data based on a mathematical model that can calculate the wake; however, it can be difficult to derive variables such as air density, roughness length, and the effect of turbulence on the structural characteristics of wind turbines. Furthermore, wake accuracy could decrease due to the excessive variables that come from the wake effect parameters. In this paper, we intend to conduct research to improve prediction accuracy by considering the wake effect of wind turbines using supervisory control and data acquisition (SCADA) data from the Dongbok wind farm. The wake divides the wind direction into four parts and then recognizes and predicts the affected wind turbine. The predicted result is the wake wind speed and its conversion to power generation by applying a power curve. We try to show the efficiency of machine learning by comparing the wake wind speed and the power generation in the wake model. This result shows the error rate using evaluation metrics of regression, such as mean squared error (MSE), root mean squared error (RMSE), and weighted absolute percentage error (WAPE), and attempts to verify power system impact and efficiency through a real-time digital simulator (RTDS).

Keywords: wake effect; wind power prediction; machine learning; RTDS; CNN-LSTM; wind farm; power system stability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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