Original scientific paper
https://doi.org/10.1080/00051144.2022.2118101
Hybrid power generation forecasting using CNN based BILSTM method for renewable energy systems
T. Anu Shalini
; School of Electrical Engineering, Vellore Institute of Technology, Chennai, India
B. Sri Revathi
orcid.org/0000-0001-7044-2208
; School of Electrical Engineering, Vellore Institute of Technology, Chennai, India
Abstract
This paper presents the design of a grid-connected hybrid system using modified Z source converter, bidirectional converter and battery storage system. The input sources for the proposed system are fed from solar and wind power systems. A modified high gain switched Z source converter is designed for supplying constant DC power to the DC-link of the inverter. A hybrid deep learning (HDL) algorithm (CNN-BiLSTM) is proposed for predicting the output power from the hybrid systems. The HDL method and the PI controller generate pulses to the proposed system. A closed loop control framework is implemented for the proposed grid integrated hybrid system. A 1.5 Kw hybrid system is designed in MATLAB/SIMULINK software and the results are validated. A prototype of the proposed system is developed in the laboratory and experimental results are obtained from it. From the simulation and experimental results, it is observed that the ANN controller with SVPWM (Space vector Pulse width Modulation) gives a THD (Total harmonic distortion) of 2.2% which is within the IEEE 519 standard. Therefore, from the results, it is identified that the ANN-SVPWM method injects less harmonic currents into the grid than the other two controllers.
Keywords
Artificial Neural Network; convolutional neural network; bidirectional long short time memory neural network
Hrčak ID:
287957
URI
Publication date:
7.9.2022.
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