Anomaly Detection for Photovoltaic Based on Improved VMD-XGBoost-BiLSTM Combination Model
Photovoltaic is a vital and rapidly developing form of renewable energy generated in China,and anomaly detection is a crucial reference for making decisions on its system operation and maintenance.The abnormal operation status of a photovoltaic system caused by component aging,failure,and other adverse factors affect the power generation efficiency and capacity,which impact the system security and benefits.However,existing solutions have limitations,such as identification of limited types of abnormalities,significantly relying on labeled amount of data,additional costs for model updating,sensitivity to noise and measurement errors,and shortcomings unsuitable for large-scale promotion and deployment.A photovoltaic anomaly detection method based on historical power generation and meteorological monitoring data is proposed to solve this problem.Initially,the preprocessing steps based on spike removal and correlation analysis are used for original data denoising and feature refinement.Next,the Variational Mode Decomposition(VMD)is used to decompose the data into multiple Intrinsic Mode Function(IMF)to extract the periodic and nonperiodic characteristics of photovoltaic power generation.Subsequently,an improved VMD-XGBoost-BiLSTM hybrid model is established to precisely and stably predict normal photovoltaic power generation,benefiting from adaptive weighting,Attention mechanism,and improved Whale Optimization Algorithm(WOA).Finally,the prediction results are compared to actual data,and thus,abnormalities can be detected based on the proposed rules.The experimental results indicate that compared to a single BiLSTM and XGBoost model,this method reduces the average error by more than 20%,in which 15.67%is contributed by the series of improvement.
国家科技期刊平台
NSTL
万方数据
