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Output Power Prediction of Wave Energy Generation System Based on Modified Ensemble Empirical Mode Decomposition-Autoregressive Integrated Moving Average Model
Author:
Affiliation:

1.College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China;2.Nanjing Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China

Abstract:

As a kind of abundant clean energy, wave energy is one of the ideal energy in the future, but it has strong stochastic fluctuation characteristics. Reliable prediction of output power for wave energy generation systems will bring great convenience to the scheduling of complex power grids. A combination prediction model of wave energy based on modified ensemble empirical mode decomposition (MEEMD)-autoregressive integrated moving average (ARIMA) model is proposed. Firstly, based on the wave calculation principle, the hourly average height and period of the mixed wave are calculated. Secondly, the MEEMD is used to decompose the hourly average wave height and period to obtain a series of intrinsic mode functions (IMFs) with different characteristics and margins. The results of the average wave height decompositions are compared with the decomposition results of discrete wavelet transformation. Then, the obtained components are used to establish ARIMA prediction models, and the predicted values of the hourly average wave height and period are obtained by superposition. Finally,the conversion model between wave height and power of direct-drive wave energy generation system is established, and the test results show the effectiveness of the combined model prediction.

Keywords:

Foundation:

This work is supported by National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid (No. U1766203).

Get Citation
[1]WU Feng, WANG Fei, GU Kanghui, et al. Output Power Prediction of Wave Energy Generation System Based on Modified Ensemble Empirical Mode Decomposition-Autoregressive Integrated Moving Average Model[J]. Automation of Electric Power Systems,2021,45(1):65-70. DOI:10.7500/AEPS20191124004
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History
  • Received:November 24,2019
  • Revised:April 28,2020
  • Adopted:
  • Online: January 05,2021
  • Published: