A novel fuzzy rule is proposed to adopt a positive pitch strategy when the error between the measured and rated generator speed becomes large and continues to increase, and to adopt a negative pitch strategy when the error is small. The improved approach is introduced into the normal Fuzzy-Proportional-Integral (Fuzzy-PI) control strategy by dividing the fuzzy rules into four areas and analyzing the design method for each area. Furthermore, a low pass filter is used to reduce the ultimate loads of the pitch driver caused by the novel fuzzy rules. The modeling of the wind turbine load under turbulent wind conditions is conducted in GH Bladed, and MATLAB/Simulink is used to interact with the modeling to verify the novel Fuzzy-PI control. The results show that, compared with normal Fuzzy-PI control, the novel Fuzzy-PI control can greatly reduce the ultimate loads and fatigue loads of the pitch driver. The novel Fuzzy-PI control not only reduces the extremum of power deviation, but also decreases some ultimate loads and fatigue loads of the tower base and the blade root. It can reduce these loads by up to 21.53% under the normal turbulent wind condition and by up to 18.14% under the extreme turbulent wind condition.

A simplified free vortex wake (FVW) model called the vortex sheet and ring wake (VSRW) model was developed to rapidly calculate the aerodynamic performance of wind turbines under axial steady conditions. The wake in the simplified FVW model is comprised of the vortex sheets in the near wake and the vortex rings, which are used to replace the helical tip vortex filament in the far wake. The position of the vortex ring is obtained by the motion equation of its control point. Analytical formulas of the velocity induced by the vortex ring were introduced to reduce the computational time of the induced velocity calculation. In order to take into account both accuracy and calculation time of the VSRW model, the length of the near wake was cut off at a 120° wake age angle. The simplified FVW model was used to calculate the aerodynamic load of the blade and the wake flow characteristic. The results were compared with measurement results and the results from the full vortex sheet wake model and the tip vortex wake model. The computational speed of the simplified FVW model is at least an order of magnitude faster than other two conventional models. The error of the low-speed shaft torque obtained from the simplified FVW model is no more than 10% relative to the experiment at most of wind speeds. The normal and tangential force coefficients obtained from the three models agree well with each other and with the measurement results at the low wind speed. The comparison indicates that the simplified FVW model predicts the aerodynamic load accurately and greatly reduces the computational time. The axial induction factor field in the near wake agrees well with the other two FVW models and the radial expansion deformation of the wake can be captured.

Integrated energy services will have multiple values and far-reaching significance in promoting energy transformation and serving “carbon peak and carbon neutralization”. In order to balance the supply and demand of power system in integrated energy, it is necessary to establish a scientific model for power load forecasting. Different algorithms for short-term electric load forecasting considering meteorological factors are presented in this paper. The correlation between electric load and meteorological factors is first analyzed. After the principal component analysis (PCA) of meteorological factors and autocorrelation analysis of the electric load, the daily load forecasting model is established by optimal support vector machine (OPT-SVM), Elman neural network (ENN), as well as their combinations through linear weighted average, geometric weighted average, and harmonic weighted average method, respectively. Based on the actual data of an industrial park of Nantong in China, the prediction performance in the four seasons with the different models is evaluated. The main contribution of this paper is to compare the effectiveness of different models for short-term electric load forecasting and to give a guideline to build the proper methods for load forecasting.

Now that China has outlined its goals of “carbon peak and carbon neutrality”, the development of clean energy will accelerate, the connection between different energy systems will be closer, and the development prospects of the integrated energy service industry will be broader. Integrated energy services are promoting energy transformation and services. “Carbon peaking, carbon neutrality” and other aspects will also have multiple values and far-reaching significance. Before implementing integrated energy services, the top-level design of integrated energy system planning must be carried out, and how to achieve the optimal allocation of capacity in the field of integrated energy systems is an urgent problem to be solved in integrated energy system planning. This paper combines practical engineering experience and the latest theoretical research results to creatively introduce, for the first time, a comprehensive evaluation into the initial planning stage, and proposes, also for the first time, a three-level multi-element comprehensive energy system planning optimization method which combines multi-element requirements to carry out comprehensive energy system planning and optimization. The three-tier planning and optimization solution results in the optimal planning scheme of the integrated energy system, thereby making the scheme more specific and reliable. According to the demand data of an industrial park, this method was applied to complete a case analysis of integrated energy system planning, which verified the feasibility and effectiveness of the method. This method is easy to popularize, and it will guide the planning of integrated energy systems, promote integrated energy services, promote energy transition, and make positive contributions to achieve carbon neutrality as soon as possible.

In order to solve the problem that the fault classification accuracy of the main bearing of the wind turbine is not high due to the unbalanced vibration signal data of the main bearing of the wind turbine under the background of noise, this article proposes a double-layer fault diagnosis model for the main bearing of the wind turbine that combines the auxiliary classifier generation adversarial network (ACGAN) and the deep residual shrinkage network (DRSN). First, the wind turbine main bearing data is sent into the ACGAN to learn the distribution features of fault data, and a particular type of fault data is generated to expand the original dataset to achieve balance conditions, and then the expanded dataset is sent to the DRSN to reduce noise to improve the fault classification accuracy. The simulation results show that, compared with the traditional deep learning model, the model proposed in this article can significantly improve the classification accuracy of the main bearing fault of wind turbines under noise conditions, and also has a strong diagnosis ability in a state of datasets with different loads.