研究目的
To develop a fault classification algorithm for accurate and early failure detection in photovoltaic (PV) systems using wavelet transform and radial basis function networks (RBFNs).
研究成果
The developed fault classification algorithm demonstrates high training and testing efficiencies, with the ability to detect faults with low misclassification. The dynamic fusion of kernels enhances the classifier's performance, making it a promising approach for fault detection in PV systems.
研究不足
The study is limited to a simulation model of a 1 kW single-phase standalone PV system. Real-world application may require adjustments for larger systems or different configurations. The dynamic fusion of kernels, while improving performance, adds complexity to the implementation.
1：Experimental Design and Method Selection:
The study involves the development of a fault classification algorithm using wavelet transform for feature extraction and RBFN for classification. The dynamic fusion of kernels is performed to improve classifier performance.
2：Sample Selection and Data Sources:
A simulation model of a 1 kW single-phase standalone PV system is developed using MATLAB/Simulink. Data is collected under various fault conditions.
3：List of Experimental Equipment and Materials:
MATLAB/Simulink for simulation, PV system components including a 1 kW PV array, DC-DC boost converter, and inverter.
4：Experimental Procedures and Operational Workflow:
The system is simulated under standard test conditions and various fault scenarios. Voltage and current measurements are analyzed using wavelet transform for feature extraction, followed by classification using RBFN.
5：Data Analysis Methods:
Features extracted include energy, entropy, peaks, power spectral density, total harmonic distortion, signal to noise ratio, skewness, and kurtosis. Principal component analysis is used to minimize the feature set before classification.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
