研究目的
To develop a fault classification technique for single-phase grid connected PV systems using Wavelet Transform and Neural Network approaches to improve classification accuracy.
研究成果
The proposed fault classification algorithm for single-phase grid connected PV systems, utilizing Wavelet Transform and Neural Network, achieved an overall classification accuracy of 98.4%. This demonstrates the feasibility and effectiveness of the method for fault diagnosis in PV systems.
研究不足
The study is based on simulation data and may require validation with real-world data. The algorithm's performance under varying environmental conditions and different PV system configurations needs further investigation.
1：Experimental Design and Method Selection:
Utilized MATLAB/Simulink platform for simulation of a grid-connected PV system. Wavelet Transform and Neural Network approaches were employed for fault classification.
2：Sample Selection and Data Sources:
Simulation data from a grid-connected PV system under various operating conditions including normal condition, mismatch in frequency component, and short-circuit at grid side.
3：List of Experimental Equipment and Materials:
PV module (Trina solar TSM-250 PA05.08), MATLAB/Simulink software.
4：08), MATLAB/Simulink software.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: Signal analysis through wavelet transform, feature extraction, and classification using a feed-forward neural network.
5：Data Analysis Methods:
Feature extraction based on Entropy, Peak-Peak, Power generated, Total Harmonic Distortion (THD), Signal to Noise Ratio (SNR), Skewness and Kurtosis. Classification accuracy evaluated using confusion matrix and ROC analysis.
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