LSTM RNN-based excitation force prediction for the real-time control of wave energy converters

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Zhang, Ming and Yuan, Zhi-Ming and Dai, Sai-Shuai and Chen, Ming-Lu and Incecik, Atilla (2024) LSTM RNN-based excitation force prediction for the real-time control of wave energy converters. Ocean Engineering, 306. 118023. ISSN 0029-8018 (https://doi.org/10.1016/j.oceaneng.2024.118023)

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Abstract

Wave energy is a type of abundant and dense renewable energy. Wave force prediction is a critical technology that influences power absorption efficiency in the real-time control of wave energy converter (WEC). Could wave elevation be used to predict wave excitation force directly by training artificial neural network? This method results in rapid and suitable prediction for real-time control. A long short-term memory recurrent neural network (LSTM RNN) algorithm is introduced to identify characteristics of wave excitation forces based on wave elevations. In this method, the wave elevations in front of the structure are measured to obtain sufficient time to actuate the control manipulation. A total of 180 regular wave and 12 irregular wave tests are conducted, and the LSTM RNN model is trained based on the experimental results. The performance of the LSTM algorithm is verified. According to the regular cases in the study, the LSTM prediction can identify high-order harmonic loads, and the anti-noise capability of the LSTM algorithm can filter random noises from the measure signals. In the irregular cases, the LSTM RNN algorithm performs effectively to predict the wave force excited on the structure using wave elevations measured by wave probes. The best combinations of the test setting parameters are determined to guide experimental tests and WEC prototypes.

ORCID iDs

Zhang, Ming, Yuan, Zhi-Ming ORCID logoORCID: https://orcid.org/0000-0001-9908-1813, Dai, Sai-Shuai ORCID logoORCID: https://orcid.org/0000-0002-9666-6346, Chen, Ming-Lu and Incecik, Atilla;
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