This article presents a novel fault diagnosis algorithm based on the whale optimization algorithm (WOA)-deep belief networks (DBN) for wind turbines (WTs) using the data collected from the supervisory control and data acquisition (SCADA) system. Through the domain knowledge and Pearson correlation, the input parameters of the prediction models are selected. Three different types of prediction models, namely, the wind turbine, the wind power gearbox, and the wind power generator, are used to predict the health condition of the WT equipment. In this article, the prediction accuracy of the models built with these SCADA sample data is discussed. In order to implement fault monitoring and abnormal state determination of the wind power equipment, the exponential weighted moving average (EWMA) threshold is used to monitor the trend of reconstruction errors. The proposed method is used for 2 MW wind turbines with doubly fed induction generators in a real-world wind farm, and experimental results show that the proposed method is effective in the fault diagnosis of wind turbines.

To improve the performance of rural power load forecasting, a new power load forecasting method is proposed. Firstly, basic and statistical features are extracted from historical power load data and numerical weather prediction (NWP) data. Then, t-SNE dimensionality reduction is used to extract main features, and K-means clustering is used to distinguish seasonal features. Secondly, the deep learning network CNN-LSTM (fusion of convolutional neural network and long short-term memory) is designed, and the load prediction models for spring and autumn, summer and winter are trained separately. Finally, use a rural power load in Wuxi, China to predict the power load in the next 24 hours and calculate the error. Experimental results show that the forecasting error of the proposed method is lower than that of LSTM, ARIMA and other prediction models.

In order to suppress the resonance caused by the integration of photovoltaic inverter clusters into the grid, this paper proposes a photovoltaic inverter cluster resonance suppression strategy based on active harmonic conductance method. Firstly, based on the inner loop of the capacitor current under the PI controller and the double closed loop control of the outer loop of the grid current, the resonance suppression effect of the single inverter under the double closed loop control is analyzed. Then, while keeping the control system unchanged, the active filter conductance is added to the photovoltaic grid-connected system for multiple inverter resonance problems, which not only effectively suppresses the inverter low-frequency harmonic current from flowing into the grid-side system, but also improves The inverter is resistant to disturbances, thereby suppressing resonance problems caused by inverter clusters. Finally, the photovoltaic inverter cluster resonance energy is effectively suppressed by Matlab/Simulink software.