TY - GEN
T1 - Improving voltage harmonics forecasting at a wind farm using deep learning techniques
AU - Kuyunani, E. M.
AU - Hasan, Ali N.
AU - Shongwe, T.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/20
Y1 - 2021/6/20
N2 - The South African renewable energy mix includes wind, solar, hydro and ocean. This energy mix contributes to the nation energy requirements while reducing dependency on fossil fuel and in the process mitigating the emission of green-house gases. Wind power generation is always associated with the generation of voltage harmonics. Precise predictions of the presence of voltage harmonics is of vital importance in order to ensure clean voltage is coupled to the national grid. A total of 8103 voltage harmonics, measured at Jeffreys Bay Wind Farm in the Eastern Cape Province have been used in our experiments. The proposed model would take two steps to extract important features present in the voltage harmonics signals. The mean voltage amplitude is extracted using moving window segmentation. Long short-term memory (LSTM), a deep learning method, is used in the prediction of voltage harmonics generation based on the voltage features extracted. LSTM is a special kind of recurrent neural network (RNN) capable of learning long-term dependencies. For simplicity the model uses one LSTM layer with 128 hidden neurons. 8103 calculated mean values were used as the expected data to train the model in Matlab. The LSTM model could predict the next 3800 sample mean values with low root mean square error (RMSE).
AB - The South African renewable energy mix includes wind, solar, hydro and ocean. This energy mix contributes to the nation energy requirements while reducing dependency on fossil fuel and in the process mitigating the emission of green-house gases. Wind power generation is always associated with the generation of voltage harmonics. Precise predictions of the presence of voltage harmonics is of vital importance in order to ensure clean voltage is coupled to the national grid. A total of 8103 voltage harmonics, measured at Jeffreys Bay Wind Farm in the Eastern Cape Province have been used in our experiments. The proposed model would take two steps to extract important features present in the voltage harmonics signals. The mean voltage amplitude is extracted using moving window segmentation. Long short-term memory (LSTM), a deep learning method, is used in the prediction of voltage harmonics generation based on the voltage features extracted. LSTM is a special kind of recurrent neural network (RNN) capable of learning long-term dependencies. For simplicity the model uses one LSTM layer with 128 hidden neurons. 8103 calculated mean values were used as the expected data to train the model in Matlab. The LSTM model could predict the next 3800 sample mean values with low root mean square error (RMSE).
KW - artificial intelligence
KW - deep learning
KW - long-short term memory
KW - recurrent neural network
KW - wind power
UR - http://www.scopus.com/inward/record.url?scp=85118780779&partnerID=8YFLogxK
U2 - 10.1109/ISIE45552.2021.9576357
DO - 10.1109/ISIE45552.2021.9576357
M3 - Conference contribution
AN - SCOPUS:85118780779
T3 - IEEE International Symposium on Industrial Electronics
BT - Proceedings of 2021 IEEE 30th International Symposium on Industrial Electronics, ISIE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE International Symposium on Industrial Electronics, ISIE 2021
Y2 - 20 June 2021 through 23 June 2021
ER -