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Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System

Author

Listed:
  • Y. D. Song
  • Qian Cao
  • Xiaoqiang Du
  • Hamid Reza Karimi

Abstract

This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC) is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.

Suggested Citation

  • Y. D. Song & Qian Cao & Xiaoqiang Du & Hamid Reza Karimi, 2013. "Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-8, November.
    • Handle: RePEc:hin:jnljam:375840
    DOI: 10.1155/2013/375840
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    Cited by:

    1. Lamsal, Dipesh & Sreeram, Victor & Mishra, Yateendra & Kumar, Deepak, 2019. "Output power smoothing control approaches for wind and photovoltaic generation systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    2. Jessica C. A. Sousa & Thiago M. Soares & Jonathan M. Tabora & Hugo G. Lott, 2025. "Design of a Controller for Supercapacitor’s Bidirectional High-Gain Interleaved Converter," Energies, MDPI, vol. 18(10), pages 1-26, May.
    3. Emilio Jiménez-Macías & Angel Sánchez-Roca & Hipólito Carvajal-Fals & Julio Blanco-Fernández & Eduardo Martínez-Cámara, 2014. "Wavelets Application in Prediction of Friction Stir Welding Parameters of Alloy Joints from Vibroacoustic ANN‐Based Model," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    4. Yuanchang Zhong & Xu Huang & Pu Meng & Fachuan Li, 2014. "PSO‐RBF Neural Network PID Control Algorithm of Electric Gas Pressure Regulator," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    5. Zhilin Liu & Lutao Liu & Jun Zhang, 2014. "Signal Feature Extraction and Quantitative Evaluation of Metal Magnetic Memory Testing for Oil Well Casing Based on Data Preprocessing Technique," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    6. Muhammad Khalid, 2019. "A Review on the Selected Applications of Battery-Supercapacitor Hybrid Energy Storage Systems for Microgrids," Energies, MDPI, vol. 12(23), pages 1-34, November.
    7. Hong Zhang & Lixing Chen & Yong Qu & Guo Zhao & Zhenwei Guo, 2014. "Support Vector Regression Based on Grid‐Search Method for Short‐Term Wind Power Forecasting," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    8. João Faria & José Pombo & Maria do Rosário Calado & Sílvio Mariano, 2019. "Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System," Energies, MDPI, vol. 12(5), pages 1-24, March.

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