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.

This paper studies how to mitigate the blade root flap-wise moment of wind turbine under the influence of wind shear and tower shadow effect. An individual pitch control strategy based on simplex method is proposed to mitigate the blade root flap-wise moment and its 1p component load on the basis of ensuring the power control of the wind turbine. This method and the individual pitch control strategy of conventional PI control are applied to a 4.5 MW wind turbine model, and simulation is carried out under turbulent wind conditions to compare and analyze the blade root flap-wise moment, its power spectral density and the output power. The analysis of the simulation operation data of the 4.5 MW wind turbine model shows that, the individual pitch control strategy based on the simplex method can effectively mitigate the blade root flap-wise moment and its 1p component, and stabilize the output power.

Dual-rotor wind turbine with high-soft tower can break the limits of wind energy utilization of conventional single-wheel wind turbines and improve the efficiency of wind energy utilization in low wind speed areas. The natural frequency of the flexible tower is within the operating speed range of the wind turbine, so there is a speed exclusion zone. Based on the normal operation control of wind turbine, a resonance crossing control algorithm is proposed to prevent the resonance between the wind turbine impeller and the tower during operation. The algorithm finds the resonance interval through Campbell diagram, and adds speed control on the basis of optimal torque control to achieve fast resonance crossing. A large number of simulation tests were carried out on a simple wind turbine model developed on Simulink for steady-state wind and in three scenarios with different turbulence intensities. The simulation results show that the algorithm can achieve fast and effective resonance traversal under all the above conditions.

In order to study the influence mechanism of rotor-side converter and its control system on damping characteristics of doubly-fed wind turbine, a dynamic model of the wind turbine under small disturbance state is constructed considering mechanical torque, electromagnetic torque, transient potential, rotor-side converter control, voltage control and angle offset. Then, the damping torque and synchronous torque expressions of the doubly-fed wind turbine are derived based on the complex torque coefficient method. The damping torque is related to the oscillation frequency, wind speed, mechanical parameters, electrical parameters and control system parameters of the wind turbine, and the control parameters of the inner and outer loops of the rotor-side converter are coupled with each other to affect the damping of the wind turbine. Finally, the mathematical model is verified by time domain simulation and frequency domain simulation. The results show that the model has applicability at different oscillation frequencies.

With the rapid development of wind power industry, the number of wasted wind turbine blades increased significantly year by year, which brings great environment pressure. Against this problem, the influence of atmosphere on the generation characteristics of gas, liquid and solid products of wasted wind turbine blades during thermal treatment at different temperatures was studied, to provide reference for thermal recovery strategies. The results showed that, in N₂ and CO₂ atmospheres, the production of combustible CH₄ reached the highest at 800 ℃, that of CO increased with temperature. Tar products in each atmosphere mainly consisted of p-isopropenyl phenol, p-isopropyl phenol and bisphenol A. Moreover, it was found in the experiment that, at high temperature, CO₂ in the atmosphere effectively prevented the formation of polycyclic aromatic hydrocarbons (PAHs) in tar, which is helpful to subsequent treatment of tar. It is also found that, the coke yield in air and CO₂ atmosphere was higher than that in N₂ atmosphere, however, at higher temperatures (600 ℃ and above), the results were opposite. This may be due to different carbonization levels in different atmospheres at low temperatures.

In order to reduce the adverse impact of wind power fluctuation and anti-peak shaving on power grid operation, a coordinated optimal dispatching strategy of "wind-grid-EV charging and swapping station" considering wind power consumption is proposed. Firstly, thermal power is used as an adjustable power supply to assist wind power grid, and through the carbon trading mechanism, the thermal power system is encouraged to actively reduce output and reduce carbon during periods of low grid load and high wind abandonment rate, so as to effectively improve the wind power grid space. Then, on the basis of meeting the power demand of the power grid, the load of the charging and changing power station is connected to further restrain the fluctuation of wind power, and at the same time, the wind power consumption is increased. The objective function is to minimize the peak valley difference of power grid load and optimize the comprehensive operation cost of the system. The low-carbon economic operation model of the joint system is constructed. Finally, the NSGA-Ⅱ algorithm is used to solve and analyze different scenarios. The results show that, this strategy can effectively reduce the system operation cost and the peak valley difference of grid load, and improve the wind power consumption rate of the grid.

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.

China's large-scale clean energy base has formed a regional power-heat combined system with multiple randomness, such as wind power, photovoltaic power generation and power/heat load, which highly depends on flexible and adjustable resources. Its low-carbon and economic operation is also challenging. In order to fully integrate the resources from source and load sides and maximize the consumption of new energy power, a day-ahead optimal dispatching method of the regional integrated electric-heating operation system considering demand response of electric, heating loads and prediction error scenario is proposed. Firstly, sliding time window multivariate Gaussian mixture distribution and Monte Carlo are used to generate and correct the random source-charge day-ahead prediction error to further optimize the forward scheduling results. Secondly, the demand response models of electric and heating loads are analogically defined. A regional electric-thermal system model considering the source-charge interaction is established. Then, considering multiple costs, a low-carbon and economic pre-ahead scheduling expectation model of the joint system is established in all typical scenarios. Finally, by taking the winter energy supply in a region in northern China as an example, the multi-scenario optimization operation results of electricity with and without electricity and heat load demand response are gradually compared. The results show that, fully introducing the electric and heating load demand responses and molten-salt thermal energy storage boiler can promote the consumption of large-scale wind and photovoltaic power, and significantly enhance the low-carbon and economic operation capability of the regional integrated electric-heating system.

Aiming at the characteristics of interconnection and multi-source in modern power systems, a heuristic intelligent optimization algorithm is proposed to assist multi-area interconnected power systems with wind, solar, water, thermal storage to optimize load frequency control. This method takes the area control error of each region as the objective function, and uses the advantages of whale intelligent optimization algorithm, such as strong robustness, high solution accuracy and fast convergence speed to jointly optimize the parameters of the PID load frequency controller in each region, so that the system can maintain frequency stability and long-term safe operation under various random disturbances. Finally, a three-area interconnected power system model with wind, solar, water and thermal storage is established to compare the frequency and tie line power deviation of the interconnected power system in different optimization tuning methods, and test the stability of the system in different regions under different disturbances and the effectiveness of the proposed method. The experimental results show that the coordinated optimization tuning method of the multi-area interconnected load frequency controller adopted in this paper effectively improves the stability of the system, and has good robustness and practicality.

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.