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.

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.

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.

For wind power units, both torque control and pitch control are designed based on generator speed, which leads to the coupling between them near the rated speed, further resulting in rotational speed disturbances. Consequently, conventional torque control will frequently change the control logic according to generator speed, thereby causing the torque and power dips. Besides, two kinds of conventional torque control strategies above the nominal wind, constant-torque and constant-power control, have advantages and disadvantages in torque, power and drive-train loads. In order to eliminate the torque and power dips, full load curve was extended to generator speeds below the rated value. Several state variables, relevant to pitch angle, were selected to reduce the frequency of switching the torque control regions near the rated speed. A new full load curve was designed based on weight programming to comprehensively consider the power and torque performances. The transition curve connecting the optimum Lambda curve and full load curve, was optimized dynamically. Moreover, the proposed control strategy was tested under typical turbulent wind conditions. The simulation results show that, the torque and power dips at above nominal wind are eliminated and the power capture increases at rated wind by using the improved torque control strategy. Furthermore, both torque and power achieves good performance by applying the rational weight.

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.

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.

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.

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.

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.

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.