Adaptive Online Sequential Extreme Learning Machine with Kernels for Online Ship Power Prediction

• Main Authors: Xiuyan Peng, Bo Wang, Lanyong Zhang, Peng Su
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: Energies
• Subjects: extreme learning machine; online sequential learning; ship power forecasting; adaptive factor; photovoltaic system
• Online Access: https://www.mdpi.com/1996-1073/14/17/5371

With the in-depth penetration of renewable energy in the shipboard power system, the uncertainty of its output power and the variability of sea conditions have brought severe challenges to the control of shipboard integrated power system. In order to provide additional accurate signals to the power control system to eliminate the influence of uncertain factors, this study proposed an adaptive kernel based online sequential extreme learning machine to accurately predict shipboard electric power fluctuation online. Three adaptive factors are introduced, which control the kernel function scale adaptively to ensure the accuracy and speed of the algorithm. The electric power fluctuation data of real-ship under two different sea conditions are used to verify the effectiveness of the algorithm. The simulation results clearly demonstrate that in the case of ship power fluctuation prediction, the proposed method can not only meet the rapidity demand of real-time control system, but also provide accurate prediction results.