In recent years, the scale of wind power is growing rapidly, its economic analysis and cost modeling methods are also constantly improving. In order to summarize the existing methods and clarify the follow-up research ideas, the four stages of the whole life cycle of the wind power project are first explained. Then, the cost composition and modeling method of the whole life cycle of wind power projects are introduced, and the differences between onshore and offshore wind farms in this part are compared, and the relationship between life cycle and investment cost is discussed based on the above contents. In order to introduce the benefits of cost investment, the basic economic evaluation indexes and applicability of wind power projects are compared. On this basis, the future development trend is analyzed according to the existing problems, and some suggestions are put forward for the economic evaluation of wind power in different regions and environments. It is hoped that the work of this paper can provide reference for wind power cost modeling and economic evaluation under the new development trend.

The integration of large-scale wind farms will lead to transient stability problems, such as frequency offset, weak feedback and high harmonics, and affect the safe operation of the system. Therefore, research of transient protection technology for large-scale wind farm integration is an urgent requirement. A comprehensive review about grid connection technology and the transient protection technology for large-scale wind farms is carried out. The topology structure of each integration technology is carefully analyzed. Besides, the fault characteristics of three power systems, including permanent magnet synchronous generator, doubly fed induction generator, and traditional synchronous power system are compared and the current research status of transient based protection of onshore and offshore wind farms is illustrated. Finally, perspectives are outlined for future development of relay protection technology in large-scale wind farms, which provides a significant reference for the research of future wind farm integration.

With the increase of the scale of wind farms and the proportion of wind power in energy system, the power grid has higher and higher requirements for the voltage stability of wind farms. The actual operation status of wind farms is focused, and the key issues of the reactive power and voltage control of wind farms are summarized, including how wind turbines adjust the reactive power, how to allocate the reactive power in large scale wind farms, how to maintain the stability of the internal node voltage of wind farms, how to solve the problem of voltage control lag, the transient voltage control strategy under fault conditions, and so on. In view of these issues, the methods and characteristics of reactive power and voltage control technology in wind farms are systematically summarized, and the realization process of reactive power and voltage control technology is expounded from the aspects of the characteristics of wind turbines and reactive power compensation equipment, reactive power and voltage steady-state control, internal node voltage control, model predictive control, and transient control under fault conditions. The research can provide reliable technical means for safe and stable operation of wind farms.

The wake effect of wind farm is the main factor affecting the performance of wind turbines in the downstream wind farm. The wake effect, load characteristics and fatigue damage of wind turbines in front, middle and rear of an offshore wind farm were quantitatively assessed by FAST.Farm, which is the latest opensource multi-physical field coupling simulation software tool of National Renewable Energy Laboratory (NREL). The results show that, the wind speed decreases and the turbulence intensity increases in turn in the wind farm along the flow direction. The fatigue damage of front-row, middle-row and back-row wind turbines increases with the inflow wind speed. Especially, under the condition of high inflow wind speed, the fatigue damage of the middle-row wind turbines at the blade root and the tower base increases exponentially, and the increase range is obviously higher than that of the front and back row wind turbine. It suggests that the structural strength of wind turbines in the central area should be improved to some extent in wind power pre-development and post-operation and maintenance work.

In view of the high failure rate of the insulated gate bipolar transistor (IGBT) of wind turbine converter and the fact that the failure occurs on a short time scale, a health state assessment method of the IGBT based on dynamic regularization and Park vector centrifugal change rate is proposed. The similarity calculation model is established by using the dynamic regularization algorithm to calculate the minimum regularization distance and waveform similarity of three-phase waveforms to judge the condition of the converter. The centrifugal rate and change rate of Park vector ellipse are used to evaluate the IGBT status and set the evaluation index. Moreover, the practicability is verified by simulation data and operation data, respectively. The results reveal that, these two methods have good practicability, the waveform similarity decreases gradually before the fault occurs, and the change trend of Park vector eccentricity continues to increase, which proves that the two algorithm models can clearly distinguish between normal and abnormal waveforms. Using these two methods can timely feed back of the converter health status, thus to effectively avoid the shutdown or damage of the power electronic system due to the IGBT fault and avoid the property loss caused by equipment fault.

In order to effectively monitor the abnormal tower vibration and ensure the unit operation safety, a data-knowledge-driven variable condition tower vibration prediction method based on long-short term memory (LSTM) and empirical mode decomposition (EMD)-eXtreme gradient boosting (XGBoost) algorithm step-by-step modeling is proposed. Firstly, the relationship between environmental and operational variables is stripped out based on the analysis of the unit's operating mechanism and the wind turbine SCADA operating parameters that affect tower vibration are identified. Then, the ultra-short term prediction of unit environmental wind speed and operating power is realized based on LSTM, and the unit data knowledge model is established based on the full working condition historical operating data. Finally, Hilbert-Huang transform (HHT) is used to decompose the vibration signal and extract the low frequency vibration of the tower, and build a tower vibration prediction model based on XGBoost algorithm. Through inputting the predictive variables, the prediction results of the tower low frequency vibration are output, and the prediction interval is determined. The results show that, the tower vibration prediction model can effectively predict the tower vibration, determine the tower operation condition, and ensure the smooth operation of the unit.

Large number of bolted structures exist in wind turbine equipment, once the bolt hole is defective, it may lead to fracture of the entire matrix and cause major accident, but the current bolt hole defect detection method has the situation of missed detection and misjudgment. Aiming at solving this problem, through investigation, theoretical analysis and physical research, two methods which combine special tooling with probe for bolt hole defect detection are developed, namely the direct beam method and the sector scanning deflection method. Taking the bolt hole of pitch bearing in wind turbine as the research object, the CIVA software is used to simulate the two detection methods, and the rectangular simulated crack can be detected. Experiments were carried out on the defects of rectangular grooves in the actual pitch bearing bolt holes, which showed that the direct beam method could realize the defect detection of bolt hole cracks. The research provides a new method for monitoring the service status of bolt holes, which is beneficial to ensure the safe service of bolted structures.

Aiming at solving the problems of large number of wind turbine faults, complex fault knowledge relationship, large difference of knowledge expression and low efficiency of knowledge reasoning, a framework of acquisition, expression and reasoning of wind turbine fault knowledge is proposed. Firstly, through the failure mode and effect analysis method based on the fault tree analysis method, the expert knowledge of wind turbine trouble shooting and maintenance is comprehensively obtained and sorted out. Then, with the help of ontology theory, unstructured expert knowledge is expressed structurally to form a knowledge map and displayed visually. Combined with self-defined rules of ontology and causal reasoning model, the query and reasoning of fault causes are realized, which improves the efficiency of knowledge query and reasoning. Finally, the practicability of this method is illustrated by a specific unit fault case. The results of this study can provide a direction for the intelligent development of wind farm's operation and maintenance.

Existing single-channel networks have poor noise immunity during fault diagnosis of rotating machinery due to the many noises associated with the operation of rotating machinery. To address this problem, a two-channel input LetNet-5 convolutional neural network model incorporating a parallel mechanism was proposed. Case Western Reserve University bearing dataset was used for the model plausibility check process, based on which Gaussian white noise with a signal-to-noise ratio of -10 dB was added to simulate the real noise situation. The short-time Fourier transform was used to process the motor fan-side and drive-side vibration data, and the resulting time-frequency images were passed to a two-channel input LetNet-5 convolutional neural network for training and learning. The results show that, the dual-channel input LetNet-5 convolutional neural network model is able to capture the fault features in a strong noise environment well, it has higher efficiency and accuracy than the multi-scale feature fusion residual model, the multimodal coupled input neural network model, the conventional K-nearest neighbour and decision tree model and the single-channel input LetNet-5 convolutional neural network model.

As a critical component for capturing wind energy, wind turbine blades may subject different degrees of damage due to blade manufacturing and operating load, which directly affects the reliability of wind turbine operation. For preventing quality and safety accidents, a fast and easy non-implantable detection method is needed to identify the damages. According to the physical correlation between blade damage and blade operation noise, a blade damage detection method based on acoustic signal and convolutional neural network (CNN) is proposed. The method converts the time-series acoustic signal into a two-dimensional spectral picture and combines the healthy spectral picture to generate a residual spectral picture. Then, the residual spectrogram is used to train the convolutional neural network and detect the damage. The analysis results show that the algorithm eliminates the influence of the inherent blade sweeping sound generated by the impeller rotation on the damage identification and improves the identification accuracy. The algorithm analysis was carried out with the actual measured data of a local wind turbine, and the results showed that the classification accuracy of the algorithm reached 96.9%, which verified the effectiveness and accuracy of the detection method based on convolutional neural network.