In recent years, wind turbines have shown a maximization trend. However, most of the wind turbine blades operate in areas with a relatively poor natural environment. The stability, safety, and reliability of blade operation are facing many challenges. Therefore, it is of great significance to monitor the structural health of wind turbine blades to avoid the failure of wind turbine outages and reduce maintenance costs. This paper reviews the commonly observed types of damage and damage detection methods of wind turbine blades. First of all, a comprehensive summary of the common embryonic damage, leading edge erosion, micro-cracking, fiber defects, and coating defects damage. Secondly, three fault diagnosis methods of wind turbine blades, including nondestructive testing (NDT), supervisory control and data acquisition (SCADA), and vibration signal-based fault diagnosis, are introduced. The working principles, advantages, disadvantages, and development status of nondestructive testing methods are analyzed and summarized. Finally, the future development trend of wind turbine blade detection and diagnosis technology is discussed. This paper can guide the use of technical means in the actual detection of wind turbine blades. In addition, the research prospect of fault diagnosis technology can be understood.

Accurate short-term photovoltaic (PV) power prediction helps to improve the economic efficiency of power stations and is of great significance to the arrangement of grid scheduling plans. In order to improve the accuracy of PV power prediction further, this paper proposes a data cleaning method combining density clustering and support vector machine. It constructs a short-term PV power prediction model based on particle swarm optimization (PSO) optimized Long Short-Term Memory (LSTM) network. Firstly, the input features are determined using Pearson’s correlation coefficient. The feature information is clustered using density-based spatial clustering of applications with noise (DBSCAN), and then, the data in each cluster is cleaned using support vector machines (SVM). Secondly, the PSO is used to optimize the hyperparameters of the LSTM network to obtain the optimal network structure. Finally, different power prediction models are established, and the PV power generation prediction results are obtained. The results show that the data methods used are effective and that the PSO-LSTM power prediction model based on DBSCAN-SVM data cleaning outperforms existing typical methods, especially under non-sunny days, and that the model effectively improves the accuracy of short-term PV power prediction.

With the increasing proportion of renewable energy in China’s energy structure, among which photovoltaic power generation is also developing rapidly. As the photovoltaic (PV) power output is highly unstable and subject to a variety of factors, it brings great challenges to the stable operation and dispatch of the power grid. Therefore, accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV energy. Currently, the short-term prediction of PV power has received extensive attention and research, but the accuracy and precision of the prediction have to be further improved. Therefore, this paper reviews the PV power prediction methods from five aspects: influencing factors, evaluation indexes, prediction status, difficulties and future trends. Then summarizes the current difficulties in prediction based on an in-depth analysis of the current research status of physical methods based on the classification of model features, statistical methods, artificial intelligence methods, and combined methods of prediction. Finally, the development trend of PV power generation prediction technology and possible future research directions are envisioned.

Onshore wind turbines are primarily installed in high-altitude areas with good wind energy resources. However, in winter, the blades are easy to ice, which will seriously impact their aerodynamic performance, as well as the power and service life of the wind turbine. Therefore, it is of great practical significance to predict wind turbine blade icing in advance and take measures to eliminate the adverse effects of icing. Along these lines, three approaches to supervisory control and data acquisition (SCADA) data feature selection were summarized in this work. The problems of imbalance between positive and negative sample datasets, the underutilization of SCADA data time series information, the scarcity of high-quality labeled data, and weak model generalization capabilities faced by data-driven approaches in wind turbine blade icing detection, were reviewed. Finally, some future trends in data-driven approaches were discussed. Our work provides guidance for the use of technical means in the actual detection of wind turbine blades. In addition, it also gives some insights to the further research of fault diagnosis technology.

Due to the uncertainty of renewable energy power generation and the non-linearity of load demand, it becomes complicated to determine the capacity of each device in hybrid renewable energy power generation systems. This work aims to optimize the capacity of two types of the off-grid hybrid wind-hydrogen energy system. We considered the maximum profit of the system and the minimum loss of power supply probability as optimization goals. Firstly, we established steady-state models of the wind turbine, alkaline electrolyzer, lead-acid battery, and proton exchange membrane fuel cell in matrix laboratory software to optimize the capacity. Secondly, we analyzed the operating mode of the system and determined two system structures (system contains batteries whether or not). Finally, according to the wind speed and load in the sample area, we compared the economics of the two systems and selected the optimal configuration for the area. In the same calculation example data, the non-dominated sorting genetic algorithm-II (NSGA-II) is used to optimize the capacity of each device in the two systems. The results showed that the profit of the without battery-equipped system is 32.38% higher than another system. But the power supply reliability is the opposite. To avoid the contingency of the calculation results, we used the traditional genetic algorithm (GA) and ant colony optimization (ACO) to calculate the same example. The results showed that NSGA-II is significantly better than GA and ACO in terms of iteration steps and calculation results. The required architecture for the System-I composes of 3 numbers of 10 kW wind turbines, 61 sets of 12 V⋅240 Ah lead-acid batteries, 8 kW electrolytic cell, and 6 kW PEMFC. The net profit and LPSP are ¥44,315 and 0.01254 respectively. The required architecture for the System-II composes of 2 numbers of 10 kW wind turbines, 24 kW electrolytic cells, and 18 kW PEMFC. Net profit and LPSP are ¥58,663 and 0.03244, respectively. This paper provided two schemes for the optimal configuration of the hybrid wind-hydrogen energy system in islanding mode, which provided a theoretical basis for practical engineering applications.

With the increasing installed capacity of wind turbines, ensuring the safe operation of wind turbines is of great significance. However, the failure of wind turbines is still a severe problem, especially as blade damage can cause serious harm. To detect blade damage in time and prevent the accumulation of microdamage of blades evolving into severe injury, a damage dataset based on GH Bladed simulation of blade damage is proposed. Then, based on the wavelet packet analysis theory method, the MATLAB software can automatically analyze and extract the energy characteristics of the signal to identify the damage. Finally, the GH Bladed simulation software and MATLAB software are combined for fault diagnosis analysis. The results show that the proposed method based on GH Bladed to simulate blade damage and wavelet packet analysis can extract damage characteristics and identify single-unit damage, multiple-unit damage, and different degrees of damage. This method can quickly and effectively judge the damage to wind turbine blades; it provides a basis for further research on wind turbine blade damage fault diagnosis.