To address the problem of poor voltage stability of wind-solar-energy storage DC microgrids in distributed renewable energy systems, an optimized nonlinear active disturbance rejection control strategy based on the SAC algorithm （SAC-ADRC） is proposed. Firstly, the wind-solar-energy storage system is modeled with nonlinear ADRC control. Then, the gain parameters of the nonlinear ADRC are reconstructed by using linear/nonlinear ADRC switching to improve its internal parameters which are more difficult to tune and analyze. Finally, the analysis establishes a mechanism for SAC intelligence to learn interactively with the microgrid environment, enabling the adjustment of non-linear ADRC parameters. Comparative analysis using algorithm convergence curves and simulation of various classical working conditions confirms the superiority of the SAC-ADRC control strategy in terms of interference performance. Thus, it is shown that the organic integration of nonlinear ADRC and deep reinforcement learning improves the stability of the microgrid bus voltage.

To address the issue of low-carbon operation in multi-microgrid distribution systems under conflicting interests, this paper proposes a hybrid-game based low-carbon strategy for multi-microgrid distribution system considering carbon emission responsibilities. Firstly, a carbon accounting mechanism based on carbon flow theory is employed to allocate source-side carbon emissions to the load side, thereby clarifying the carbon emission responsibilities of all parties. Simultaneously, considering the game relationships among multiple stakeholders, a hybrid game-based low-carbon operation framework for multi-microgrid distribution systems is developed. This framework incorporates both master-slave and cooperative game models to enable effective multi-stakeholder participation. In this framework, the master-slave game takes the distribution network as the leader and the multi-microgrids as followers. The distribution network aims to minimize its operation costs while accounting for carbon emissions by optimizing electricity pricing. This pricing strategy guides the demand response and operational behaviors of the multi-microgrids, leveraging their collaborative carbon reduction potential under carbon flow theory to enhance the low-carbon operational performance of the system. Moreover, using the power interaction data among microgrids obtained from the master-slave game, a cooperative game model based on Nash negotiations theory is established. This model aims to achieve balanced benefit distribution among the microgrids, promoting fairness and enhancing collaborative efficiency within the system. Finally, the proposed hybrid game model is solved using a combined of the dichotomy and the analytical target cascading approach, ensuring efficient and accurate optimization of the system. The case study analysis demonstrates the effectiveness of the hybrid game model proposed in this paper in addressing the conflicts of interest between the distribution grid and multi-microgrids, and in improving the low-carbon operation level.

To realize the intelligent interconnection and interaction between the intelligent integrated energy system （IES） and the power supply system of rail transit, a synergistic optimization strategy for the hydrogen-containing IES and the power supply system of rail transit is proposed based on the dynamic ladder-type carbon trading mechanism. This strategy can reduce the cost of energy supply and utilization, while enhancing the low-carbon energy supply of the multi-flow coupling system and the low-carbon energy consumption of the rail transit system. Firstly, a dynamic ladder-type carbon trading model is designed based on the level of renewable energy and loads in IES. Secondly, according to the electric and thermal output characteristics of gas turbine （GT） and hydrogen fuel cell （HFC）, the traditional combined heat and power （CHP） unit and hydrogen energy unit are coupled by the Kalina cycle to develop a hydrogen-containing flexible energy supply unit, which can enhance the flexibility of the energy supply. Then, a hydrogen-containing IES collaborative optimization model connecting to the power supply system of rail transit is established to achieve cross-system joint optimization of rail transit energy consumption and multiple heterogeneous energy supply units, fully exploring the low-carbon economic operation potential of the system. Simulation results show that the proposed strategy can effectively balance the low-carbon economy and flexibility of multi-energy system scheduling, which can provide references for the synergistic operation of IES and power supply system of rail transit.

To meet the inertia requirements of different voltage levels and dynamic operating conditions, this paper proposes an inertial fusion control technology of multi-voltage-level DC microgrid. Initially, a model of a multi-voltage-level DC microgrid is established, and the inertia characteristics of various levels of DC systems are analyzed. Subsequently, aiming to maximize inertia in each system level, the paper considers the involvement of battery storage and renewable energy side converters connected to the system bus. These are engaged in inertia regulation through increased output current feed-forward control and grouped adaptive control. Meanwhile, the low-voltage side use inertia control based on observation compensation with converters connected to their load side to suppress fluctuations. This approach allows converters with inertia regulation capabilities to participate in the graded fusion of inertia in the DC microgrid and designs the inertia parameters for each end. Finally, a multi-voltage-level DC microgrid hardware-in-the-loop simulation platform is constructed to validate the effectiveness of this control strategy.

This paper proposes a detection method for protection mismatch risk settings tailored for high-penetration renewable energy integration scenarios. Using a typical topology of high-penetration distributed renewable energy systems, the degradation mechanism of protection performance is analyzed in depth. Furthermore, the constraints on correct protection operation are revealed by investigating the root causes of protection misoperation and failure to trip. Based on this analysis, the mapping relationship between DG output and protection mismatch risk settings is quantitatively examined. The boundary of protection failure is further determined using a dual-loop iteration method based on the backbone particle swarm optimization （PSO） algorithm. Finally, a comprehensive identification procedure for protection mismatch risk settings is developed. The effectiveness of the proposed approach is validated through simulations using the PSCAD/EMTDC and Matlab/Simulink platforms.

It is of great significance to realize transient stability evaluation accurately and quickly for stable and safe operation of power system. Aiming at the problem of low prediction accuracy in the current transient stability evaluation of grid-connected photovoltaic energy storage systems, a transient stability evaluation method based on integrated deep Random forest algorithm （gcForest-AdaBoost） is proposed by combining deep learning and ensemble learning techniques. Firstly, the initial input features are constructed according to the steady-state components of the grid-connected photovoltaic energy storage system during operation. Secondly, in order to reduce the overfit of the model and ensure that the model still has strong learning ability after the number of cascades increases, a transient stability evaluation method based on gcForest-AdaBoost is proposed by combining deep random forest algorithm and AdaBoost algorithm. Thirdly, the gcForest-AdaBoost model is trained with input feature set, and the transient stability evaluation model of grid-connected PV energy storage system is established. Finally, the IEEE 39-node system is connected to the photovoltaic energy storage unit to build a case system for simulation analysis and data acquisition, and the evaluation results are obtained. Numerical examples show that the proposed model can effectively analyze the transient stability of grid-connected photovoltaic energy storage systems, and it is found that the proposed model has good robustness and generalization ability.

Concerning the common-mode current problem and the magnetic saturation problem of energy storage inductor in the single-stage current-source inverter, a multi-input current-source inverter with bypass switches and multi-winding high-frequency transformers is adopted. Concerning the energy management and the loop decoupling problem of the multi-energy system, a one-cycle control and master- slave power energy management strategy based on power ratio is proposed, which aims to realize the stable operation of different power supply modes, smooth transformation of different power supply modes, and decoupling operation of the control-loop. The intensive theoretical analysis of the circuit structure, energy management control strategy, and small-signal model of the system is carried out, the control loop of the system is designed, and finally a 2.5 kVA prototype of single-stage current-source inverter with photovoltaic-wind dual-input is developed to validate the adopted circuit topology and energy management control strategy.

Under the background of global climate change, extreme weather events such as extreme high temperature, cold and freezing temperature, and strong wind and heavy rain occur frequently, which seriously threaten the operational reliability of integrated energy system （IES）. Based on this, we firstly focus on the reliability assessment methods of integrated energy system under extreme weather, and analyze the impacts of three types of extreme weather, namely, extreme high temperature, severe cold and freezing temperature, and strong wind and rainstorm, on the energy production equipment （including power generation, heat supply, and energy storage equipment） and the energy transmission network in various aspects. Secondly, we comprehensively review the integrated energy system reliability assessment methods under these extreme weather conditions, and explore the shortcomings of the existing methods in terms of meteorological data processing, system coupling modeling, multi-method fusion, toughness quantification, and multi-factor considerations. Finally, possible future research directions are proposed for the operational reliability assessment of integrated energy systems under extreme meteorological conditions, aiming to provide certain theoretical and practical references for improving the accuracy and effectiveness of IES reliability assessment under extreme meteorological conditions, so as to guarantee the safety and stability of energy supply.

In order to study the high dimensional unsteady flow characteristics in the flow field of supercritical carbon dioxide centrifugal compressor reversed as turbine, the research object is centrifugal compressor reversed as turbine, the CFX flow field analysis software was utilized for numerical simulation, the dynamic mode decomposition（DMD） was performed on the simulated results, and the first four order modes and their corresponding spatio-temporal information are obtained. The analysis results show that the DMD method can decompose the unsteady flow field into modes with distinct energy levels and frequencies, including the basic mode with the highest energy contribution （0 Hz）, the dynamic and static interference mode with the second-highest energy （at the blade passing frequency）, and the high order harmonic characteristics of the dynamic and static interference modes with lower energy contributions（at multiples of the blade passing frequency）; The main characteristics of the basic mode are caused by the geometric parameters of the impeller, the main characteristics of the dynamic and static interference modes are caused by the dynamic and static interference between the impeller and the guide vane, and the high order harmonic characteristics of the dynamic and static interference modes show the subtle flow characteristics in the flow field; Under low-flow conditions, the first order mode exhibits a relative liquid flow angle smaller than the inlet blade angle, leading to flow separation on the pressure side of the blade inlet; Under high-flow conditions, the relative liquid flow angle of the first-order mode exceeds the inlet blade angle, resulting in flow impingement on the pressure side and flow separation on the suction side of the blade inlet. The DMD method can effectively decouple the unsteady flow field within the impeller and extract transient flow characteristics.

In view of the characteristics of multi-regional correlation of full-network power flow in the new-type power system, an optimization method for coordinated electricity-carbon operation and a new full-network multi-regional cooperative optimization method for dynamically matching coordinated electricity-carbon operation are proposed. Firstly, according to the characteristics of cascade multi-source load-storage units in sub-regions of the new-type power system, the differences in operational regulation efficiency of various load-storage units within the region are analyzed, and a gradient cooperative optimization method for load-storage unit indicators is proposed. Then, aiming at the diversified characteristics of electricity-carbon regulation of load-storage units under different operating modes, a fine-tuning optimization method for load-storage unit control parameters based on complete state space and operational mode feedback is studied, and a 'multi-level multi-core' convolutional neural network model is established. Finally, a simulation model is constructed for validation. Simulation results show that the proposed multi-level indicator cooperative electricity-carbon power flow optimization control strategy can effectively enhance the electricity-carbon coordination capability and electricity-carbon regulation level of the new-type power system.

To achieve accurate load awareness and efficient device control, this study proposes a novel non-intrusive load decomposition （NILD） method by combining multi-modal feature selection techniques with an improved time convolutional network to enhance identification accuracy and efficiency. In this method, the load multi-scale maximum overlapping discrete wavelet transform results are introduced as input features, and the feature is combined with the mRMR algorithm to ensure the effective integration of multi-modal features. At the same time, the adaptive receptive field attention mechanism （RFAM） is added to the TCN method,improving the ability to extract features at different time scales. Finally, the algorithm is validated on datasets such as PLAID. The results show that the proposed method can achieve more accurate and efficient load decomposition with fewer features,which is higher than the regression accuracy of the traditional BiLSTM model of 5.31%,and the detection rate of new energy household equipment such as photovoltaic power has also reached over 96.6%.

The cascaded H-bridge topology exhibits distinct advantages in the field of photovoltaic （PV） power generation with its modular design, smaller filtering inductance, and simplified layout. However, the three-phase cascaded H-bridge inevitably inherits the inherent issue of the single-phase full-bridge inverter, namely, double-line frequency voltage ripple on the DC-link. This issue leads to an output voltage deviation of photovoltaic array from its maximum power point, thereby reducing the power generation efficiency of system. To address this issue, this paper presents an innovative control method based on adaptive third-harmonic injection, which effectively avoids over-modulation risks under varying power factor angles. The method can calculate the optimal third-harmonic injection based on the real-time operating conditions of the system, thereby achieving optimal suppression of DC-link voltage ripple. Finally, a full-scale experimental platform is constructed, and experimental results confirm the effectiveness and feasibility of the proposed method.

To enhance the consumption capacity of distributed photovoltaic （PV） and improve the power supply reliability of rural areas, this paper proposes a hierarchical coordinated planning method of PV and energy storage system for rural microgrids. Firstly, the medium and long-term load forecasting model based on the DenseNet-Autoformer network is proposed, which uses the autocorrelation mechanism to extract the deep time-series features, and comprehensively considers factors such as meteorology and economy on long-term monthly electricity consumption; Secondly, the Wasserstein generative adversarial network-gradient penalty method is used to expand the PV power samples. On this basis, the typical source and load scenarios are obtained via Gaussian mixture clustering; Finally, taking into account the electricity self-sufficiency and the off-grid reliability, the PV and storage configuration plan of the distribution transformer area is optimized, and the power supply reliability under extreme scenarios is quantified based on chance constraints. Meanwhile, considering the overall energy self-sustained ability of the 10 kV feeder, the feeder-level PV and storage configuration plan is optimized. The simulation results show that the proposed method can enhance the energy self-sustained ability and PV consumption capacity of rural microgrids and improve the power supply reliability in extreme scenarios.

A dual-winding induction machine based inverting system is proposed in this paper. The dual winding induction machine with two electrical interfaces is utilized as an energy conversion hub between the renewable energy source and the grid. It enhances the motor characteristics of the power system while completing the renewable energy integration. In order to make it valuable for application, this paper focuses on its control strategy. This paper sets out the analysis of the operating characteristics of the dual-winding induction machine. The analysis is used to design a set of multi-condition control strategies for different operating conditions. This strategy is intended to meet the control requirements of startup, integration into the grid, grid-connected operation and off-grid operation. The experimental results verify the effectiveness of the proposed control strategy.

In this paper, the optimization strategy of reserve capacity and cost sharing mechanism are studied considering the source-load uncertainty. The probability distribution model of wind and PV forecast deviation is improved based on Gauss-Laplace distribution, and an improved Latin hypercube sampling method is proposed based on Box-Muller transformation to promote the efficiency and accuracy. And then a reserve capacity optimization model is constructed to minimize the cost considering the constraints of condition risk. Finally, a reserve cost sharing mechanism is established with the objective of achieving Maxmin fairness in the allocation utility value and the constraint interval of cost allocation as the constraint condition. The IEEE-30 standard test cases show that the improved Latin hypercube sampling method can effectively improve the sampling efficiency and reduce the computation time, and the benefit sharing is more reasonable based on the proposed strategy, which can more effectively motivate the demand side to participate in the standby market.

To mitigate the adverse effects of wind power output fluctuations, low-frequency oscillations, and multi-controller coupling in power systems, this paper proposes a coordinated optimization strategy for multi-controller parameters under diverse operating conditions. Firstly, a Simulink model of the wind-PV-thermal bundled power transmission system and its controllers is established. Typical wind power output scenarios are generated using the Voronoi partition sampling method. Subsequently, the objective function for the optimization problem is formulated based on probabilistic methods, and critical controller parameters for optimization are identified via random forest regression analysis. Finally, an improved hybrid dung beetle optimizer is employed to coordinately optimize these key controller parameters across multiple operating scenarios. Simulation results on the IEEE 4-machine 2-area system and the 16-machine 5-area system demonstrate that optimizing the extracted controller parameters effectively enhances system damping. This significantly suppresses fluctuations in active power, power angles, and other system variables caused by low-frequency oscillations, thereby validating the efficacy of the proposed probabilistic optimization approach.

To explore a new mechanism of frequency response with multi-resource coordination, a day-ahead and intra-day decentralized mutual-aid method of energy and auxiliary services is proposed. Firstly, based on the mutual-aid of energy resources, the framework of multivariate adjusting resources is established including day-ahead and intra-day energy, inertia and primary frequency regulation （PFR）. Secondly, the shared alternating direction method of multiplier（ADMM） is adopted to realize efficient solutions of the model and protect the privacy of mutual-aid subjects, in which the alternating optimization procedure （AOP） is established to address the non-convexity. Finally, a simulation example is given to verify that the proposed method can effectively motivate multiple regulatory resource suppliers to provide inertia and PFR services, which provides a reference for promoting mutual-aid of frequency regulation resources.

An integrated energy system coupled with solar energy and biogas is designed in this study, and a joint optimization method of capacity and operation is proposed. Firstly, a thermodynamic model of biogas production is introduced, and a capacity-operation two-layer optimization model is constructed. The upper layer optimizes the size of the biogas digester and the capacity of energy conversion and energy storage equipment to minimize the annual total cost. The lower layer takes the upper-layer results as constraints to optimize the operation plan of energy conversion and energy storage equipment, minimizing the operation cost. Secondly, by integrating the nonlinear programming method into the genetic algorithm, the model is solved to determine the optimal capacity configuration and operation scheme of the system. Finally, five simulation scenarios are used to verify the effectiveness of the proposed system and the joint optimization method. The results show that the integrated energy system optimized by the proposed method has the best economic performance, and the energy consumption scheme is efficient and reasonable.

To address the low efficiency and high pollution associated with fossil-energy-dominated energy use in rural industrial parks, a park-level integrated energy system （PIES） model is established based on the source-load characteristics of rural parks. To promote renewable energy accommodation and biomass utilization, a power-to-ammonia system （P2A） and a biomass anaerobic fermentation gas production system （AF） are introduced into the PIES and modeled in detail. Considering the influence of anaerobic fermentation temperature on gas production rate, the waste heat from the P2A system is delivered to the AF system to construct a P2A-AF coupled system, thereby improving system energy efficiency and economy. On this basis, detailed carbon emission modeling is carried out for the main energy-use processes of the PIES, and carbon emission costs are incorporated to establish an optimal dispatch model with the minimum total cost as the objective. The simulation results show that the proposed system can improve energy economy by 31.56%, increase the local accommodation rate of renewable energy by 34.92%, and reduce total carbon emissions by 3.58%.

This paper takes the IEA 15 MW wind turbine blade as the research object, establishes the dynamic model of the aeroelastic system for the blades, analyzes the aeroelastic response during flutter at high tip speed ratio, and conducts in-depth research on the multi-degree-of-freedom coupling characteristics therein. The results show that at high tip speed ratio, the long and flexible blade of this wind turbine undergoes a coupled bending-torsion flutter of pitch-torsion. The vibrations in the three degrees of freedom all contain the natural frequencies of pitch and torsion. However, the maintenance of flutter is sustained by the aerodynamic power work in the pitch and torsion degrees of freedom. There is an obvious coupling phenomenon in the development of the instantaneous aerodynamic power of pitch and torsion. In the direction of yaw, there is basically no energy contribution. There is only weak aerodynamic power throughout the process, and it only undergoes forced vibrations due to the excitation of pitch and torsion because of the coupling characteristics of the blade structure.

Insulated-gate bipolar transistor （IGBT） modules, as the core power components of wind turbine power converters, are crucial for achieving high operational efficiency and cost-effectiveness. Optimizing the electrical conductivity and manufacturing cost of these modules is essential. However, the demanding operational environments often cause performance degradation, and traditional design optimization methods incur high computational costs, making rapid iterative optimization difficult. To address these limitations, this study proposes a rapid iterative design optimization method for wind turbine converter IGBTs based on an improved NSGA-Ⅱ algorithm （Non-dominated Sorting Genetic Algorithm Ⅱ）. Firstly, the optimization problem is formulated with electrical conductivity and manufacturing cost as dual objectives. A parametric thermoelectric coupling model is developed to enable accurate and efficient evaluation of conductivity under varying design conditions. To further enhance the optimization process, an improved NSGA-Ⅱ algorithm based on kernel density estimation is proposed, significantly improving computational efficiency and optimization accuracy compared to traditional optimization algorithms. Additionally, a Kriging-based surrogate model is employed to construct high-fidelity mappings between design variables and optimization objectives, thereby reducing computational burdens and enabling rapid iterative optimization. Numerical experiments confirm the effectiveness and robustness of the proposed method, demonstrating reductions in electrical losses of up to 20.00% and decreases in manufacturing costs by as much as 27.63%. This study provides a practical and efficient design framework for IGBT modules, offering valuable insights into multi-objective optimization in the field of power electronics. By integrating advanced optimization algorithms with surrogate modeling, the proposed method addresses key challenges in the design and performance enhancement of wind turbine power systems.

Because of the applicability challenges of power/torque control technology near the rated wind speed for large wind turbines, this paper proposes a fuzzy-based electrical power and load cooperative control strategy for large wind turbines in the full-generation stage. Firstly, a fuzzy algorithm is used to intelligently judge the rotation speed change trend of the generator's speed change during full-generation stage. Secondly, maximum/minimum torque coefficients and power switching coefficients are proposed, and different control strategies are flexibly selected based on the trend of rotation speed change of generat or. That achieve the organic combination of constant-power and constant-torque control method. Finally, the effectiveness of the proposed method is verified through simulation experiments. This method can not only maintain relatively stable of electrical power and ensure the operation safe of wind turbines and the grid, but also effectively reduce the fatigue loads on the major components of wind turbines, which can extend its service life.

In view of the problems that traditional crowbar protection is difficult to simultaneously suppress rotor current and DC bus voltage as well as absorb reactive power during low-voltage ride-through. This paper proposes a control strategy of multi-stage crowbar switching and reactive power compensation. During the fault period, the crowbar resistance is adjusted in real time by grading the crowbar, so as to achieve the purpose of suppressing the rotor overcurrent and DC bus overvoltage. At the same time as the multi-stage crowbar is put into operation, the GSC adopts the reactive power compensation strategy, so that the fan can provide certain reactive power support to the power grid. On this basis, the change of rotor flux linkage after the crowbars at all levels are put into operation is analyzed, analytical expressions for the stator and rotor short-circuit currents upon the activation of each crowbar stage are derived, and the resistance value for each stage is subsequently set. Then, the short-circuit current of GSC is analyzed according to the reactive power compensation strategy, and then the influence of multi-stage crowbar protection access and GSC additional reactive power compensation strategy on DFIG reactive power characteristics is further analyzed. Finally, the effectiveness of the proposed control strategy and the correctness of the short-circuit current expression are verified by simulation.

To address the issues of low efficiency, limited precision, and multiple parameters in existing PI controller identification methods for rotor-side converters （RSC） of doubly-fed induction generators （DFIG）, a hierarchical identification framework combining an improved black hole algorithm （IBHA） with the RT-LAB hardware-in-the-loop （HIL） co-simulation platform is proposed. Firstly, based on the RSC control dynamics, the operational characteristic data of the actual controller of DFIG is collected by the RT-LAB HIL co-simulation platform. Secondly, a trajectory sensitivity analysis is performed to quantify the dynamic response characteristics of each parameter, followed by the selection of optimal observables and a rigorous identifiability assessment based on persistent excitation criteria. Thirdly, a stepwise identification strategy is established based on the RT-LAB measured data and IBHA. Finally, based on the HIL test data, the identification results of several algorithms are compared and analyzed, which verifies the effectiveness of the proposed method.

Based on the catenary theory and the lumped mass method, a comparative analysis of the dynamic response characteristics of submarine cables under diverse laying modes is carried out. Special attention is given to the sensitivity analysis of pivotal parameters, including wave direction, laying depth, touchdown point distance, and laying speed during the dynamic laying process of submarine cables. Thereby, the influence mechanism of each key parameter in submarine cable laying is unveiled. By contrasting the maximum effective tension and minimum bending radius of submarine cables under different laying methods, the dynamic response traits of various laying methods are elaborated.

To address the challenges of insufficient feature extraction, inadequate feature fusion mechanisms, and limited diagnostic capability under complex operating conditions in rotating machinery fault diagnosis, a novel FFT-CNN-Informer fault diagnosis method based on dual-domain feature fusion is proposed. A parallel time-frequency domain feature extraction architecture is constructed. Specifically, Fast Fourier Transform （FFT） is employed to extract frequency-domain features, a multi-scale Convolutional Neural Network （CNN） is designed to capture local temporal features, and a Probabilistic Sparse Self-Attention mechanism is introduced to capture long-range dependencies, enabling multi-dimensional feature extraction of vibration signals. Furthermore, residual learning and an adaptive feature fusion mechanism are incorporated to enhance the ability to extract fault features under varying operating conditions. Experimental results on the Case Western Reserve University bearing dataset demonstrate the superiority of the proposed method. On wind turbine bearing datasets, the proposed method outperforms baseline Transformer and LSTM models by 6.73% and 9.77% in diagnostic accuracy, respectively. Ablation studies further validate the necessity of the dual-domain feature extraction architecture and the adaptive feature fusion mechanism, confirming the effectiveness and robustness of the method in practical wind farm applications.

When the grid dispatch center issues a reactive power command value, the wind farm needs to rationally allocate this command value to each wind turbine generator （WTG） to meet grid stability requirements. To achieve this objective, this paper proposes a loss-minimization-based reactive power optimization control strategy for onshore wind farms. An improved particle swarm optimization （PSO） algorithm is employed to optimize the reactive power reference value for each WTG, aiming to reduce total losses within the wind farm. These losses encompass generator losses, power converter losses, filter losses, transformer losses, and transmission line losses. To validate the effectiveness of the proposed strategy, a wind farm model with a 5×5 layout is established. Simulation studies compare the conventional reactive power control strategy with the proposed loss-minimization-based reactive power optimization control strategy under different scenarios. The simulation results demonstrate that the proposed optimization strategy significantly reduces power losses in the wind farm.

This study investigates the impact of sand-induced surface wear on the aerodynamic performance of the S809 wind turbine airfoil using a numerical approach integrating the Discrete Phase Model （DPM）, dynamic mesh, and Gaussian filtering. Results show that erosion is concentrated at the leading edge, with a maximum depth of 0.6% chord length. As the angle of attack increases （2°~12°）, the eroded region contracts on the suction side but expands on the pressure side. Surface wear causes pressure fluctuations at the leading edge and advances flow separation, shifting the separation point forward by up to 31.43% chord length at 12°. Erosion increases drag while reducing lift and aerodynamic efficiency, with a 44% drag rise, 25% lift loss, and 48% decline in lift-to-drag ratio at 12°. These findings highlight the adverse effects of surface wear on airfoil aerodynamics.

We propose a Mamba-Transformer model integrating physical constraints and multi-scale features for ultra-short-term wind power forecasting. The proposed model employs complete ensemble empirical mode decomposition with adaptive noise to capture nonlinear and nonstationary patterns in wind power data across multiple frequency components. The decomposed modal functions are then fed into the Mamba model alongside meteorological data from the wind farm to uncover local time-varying properties. The self-attention mechanism of the Transformer model is then employed to capture long-range dependencies among multi-scale features. Finally, a one-dimensional Jensen wake model is integrated to establish physical constraints, integrating the prediction results of physical models and data-driven models through an adaptive weighting mechanism. Experimental results demonstrate that compared to the Transformer model, the proposed model reduces root mean square error （RMSE） and mean absolute error （MAE） by 41.29% and 50.26%, respectively. This model enhances wind power forecasting accuracy while exhibiting strong generalization capabilities.

To address the limitations and one-sidedness issues in unidimensional analysis of wind turbine three-row cylindrical roller bearing simulations under single-variable operating conditions, this study establishes a comprehensive rigid multibody contact dynamics simulation framework for spindle bearing systems under operational loading. A comparative analysis is conducted between models incorporating and omitting the transmission path considerations. Systematic investigations are performed on cage-roller kinematic behavior through theoretical velocity computations, simulated rotational dynamics, and acceleration response characterizations. The model is verified through rigorous verification procedures, enabling detailed examination of interfacial force characteristics between rolling elements and raceways in three-row cylindrical roller bearing configurations. Key findings demonstrate that transmission path integration significantly enhances simulation fidelity to actual service environments. Distinct load-bearing and non-load-bearing zones emerge in radial roller arrays, contrasting with the full-row load alternation pattern observed in thrust rollers. The contact force magnitude at the radial roller-inner race interface exceeds distal thrust roller interactions by 5.67 times, while distal thrust roller forces maintain approximately twice the magnitude of proximal counterparts. When the roller is in the non-load-bearing zone, its self-rotation speed gradually decreases, and the degree of decrease is directly proportional to the movement time in the non-load-bearing zone. The rotational speed and contact force of the thrust roller and thrust cage have the same alternating changes as the axial alternating load. The rotational speed of the radial cage is more stable. When the thrust roller is not under load, the corresponding thrust cage will be driven to rotate by the inner ring, and the rotational speed will increase.

A power smoothing strategy without energy storage system by utilizing the large rotational inertia of the wind turbine rotor to achieve power smoothing is proposed to solve the problem of output power fluctuations of modern large-scale wind turbines under rated wind speed. Firstly, the inherent characteristics of the wind turbines are employed to provide an immediate and accurate equivalent wind speed for control participation. Secondly, the upper and lower limits of the rotational speed are set for each wind speed segment based on high aerodynamic efficiency zone and wide speed-ratio design, and the entire inertia control process is given simultaneously to achieve the energy storage and release of the wind turbine rotor. Subsequently, multiple prediction models based on the slope of the wind speed pulsation trend are constructed via big data sources to realize the predictive action of the torque. Finally, the comparative simulation experiments of the power output of the wind turbine are carried out by BLADED simulation software and a certain actual wind farm. The results show that the adopted strategy stabilizes the output power effectively without the additional energy storage devices.

A load reduction method was provided by optimizing the aerodynamic configuration and structural design of the blades in this paper. The GH-Bladed computing platform was used to verify the blade performance and turbine load based on 6.25 MW wind turbine. The study finds that reducing the blade chord length has the most significant effect on reducing the blade root load, while the effect of reducing thickness on load reduction is not prominent. However, reducing blade thickness is a convenient method to improve aerodynamic performance while also weakening load. In addition, the laying thickness of the spar cap structure can change the blade flapwise stiffness to affect flapwise deformation. Expanding the blade flapwise deformation by adjusting the thickness of the blade spar cap to release concentrated stress is a novel method to promote the reduction of blade load generation.

Wind power forecasting is a crucial topic in the field of wind energy generation. With the increasing penetration of renewable energy in power systems, wind energy, as a rapidly developing renewable resource, necessitates accurate short-term forecasting for the energy industry. In this paper, a Gaussian Mixture Model-gated Recurrent Unit （GMM-GRU） based on Gaussian graph convolution is proposed. Firstly, the XGBoost algorithm optimized by Particle Swarm Optimization （PSO） is utilized to construct a feature selection network for identifying important features. Secondly, using the graph auto-encoder and cosine correlation fusion method, a network graph to effectively capture the potential long-distance correlations among sites and accurately describe the spatial correlation of multi-site features are built. Finally, the Gaussian Mixture Model （GMM） is employed to deeply extract the intrinsic relationships within the network graph, and the Gated Recurrent Unit （GRU） integrates its spatio-temporal correlations to address the power forecasting problem. The experiments on real wind farm data are carried out. The comparisons with other models demonstrate that the proposed model significantly enhances wind power forecasting performance.

This study investigates the influence of second-order wave forces on the dynamic response of a semi-submersible floating wind turbines, using the "Guoneng Sharing" as a case study. A comparative analysis of the differences between far-field and near-field methods for calculating the second-order mean wave force transfer functions （QTFs） is presented. A coupled time-domain model of aerodynamic, hydrodynamic, structural, and servo dynamics was established to analyze the dynamic responses of the floating wind turbine under four scenarios： neglecting second-order wave forces, considering second-order mean wave forces, considering second-order difference wave forces, and considering both second-order difference and sum wave forces. The results indicate that the QTFs calculated by the far-field and near-field methods are nearly identical when the angular frequency is less than 1.1 rad/s. However, as the angular frequency increases, the far-field method generally yields larger values than that of the near-field method. The second-order wave forces significantly impact the motion response of the semi-submersible platform, with the primary contributing components being the second-order mean wave forces or second-order difference wave forces, while the second-order sum wave forces exhibit negligible influence. Notably, the second-order difference frequency and sum frequency wave forces significantly affect the acceleration of the semi-submersible platform and the load at the top of the tower, whereas the second-order mean wave forces have no discernible effect on either. The high-frequency response of the tower top load is excited under the influence of second-order difference and sum frequency wave forces and must be taken into account in calculations. Furthermore, second-order wave forces markedly affect the tension and fatigue life of the mooring anchor chains, not only increasing the fatigue damage but also influencing the location of maximum fatigue damage occurrence.

To address the limitations of traditional resource allocation methods, this study analyzes the process of optimizing resource allocation strategies for offshore wind farm construction under meteorological uncertainty. A multi-objective optimization framework is proposed, incorporating four key performance indicators： minimum total working time, resource allocation optimization rate, window period utilization rate, and cost-benefit ratio. A discrete-event digital simulation-based optimization model is developed to support this framework, enabling systematic evaluation of the benefits of resource allocation strategies through statistical analysis of these indicators.The Laotian Monsoon Wind Power Project is selected as a case study for simulation. Results demonstrate that the established model can effectively simulate the entire construction process of offshore wind farms, providing real-time visualization of meteorological conditions, wind turbine unit statuses, and personnel dynamics. It also accurately quantifies critical metrics including minimum working time, resource allocation optimization rate, window period utilization rate, and cost-benefit ratio. The model also to significantly enhances resource allocation during wind farm installation, improves overall construction efficiency, and provides valuable pre-construction planning support. These outcomes collectively contribute to achieving the dual goals of cost reduction and efficiency improvement in offshore wind farm development.

A fault diagnosis method for wind turbine rolling bearings is proposed, which combines the gradient dynamic selective alignment （DSA） method, dual-channel feature fusion technique （DCFF）, and deep learning. Firstly, the acquired one-dimensional time series data are transformed into two different two-dimensional image representations using recursive mapping （RP） and Markov transfer field （MTF）. These transformed images are then input into an encoder for reconstruction into a low-dimensional representation. Next, a dual-channel feature fusion convolutional neural network （DCFFC） is constructed, embedding an improved attention mechanism within FRNet to extract in-depth feature information. A second-level feature fusion is performed to complete the feature classification. Finally, a gradient masking matrix is constructed using the data reconstruction loss of the decoder and the feature extraction network classification loss to achieve gradient dynamic self-adaptation throughout the fault diagnosis process. Experimental validation using the Paderborn dataset demonstrates that the proposed method achieves a diagnostic accuracy of 99%, effectively extracting fault-specific diagnostic information.

Wind turbine gearboxes are subjected to contact loads during operation, resulting in contact fatigue damage in the gears, which significantly affects their service life. To accurately predict the contact fatigue life of gears, this study takes a 6 MW wind turbine gearbox operating at Dabancheng Wind Farm, Xinjiang, as the research object. A gearbox dynamic model considering both internal and external excitations is established to calculate the dynamic meshing forces of each helical gear pair. The analysis incorporates local material properties and residual stresses in the gears. By applying the Dang Van multiaxial fatigue criterion combined with S-N curves, the fatigue damage distribution in the helical gears under contact loading is analyzed. Fatigue hotspots are identified, and critical operating conditions are extracted to develop a surrogate model, which quantifies the contact fatigue damage and estimates the contact fatigue life of each gear over a 20-year design period. The results indicate that the engage-in and recess points of external meshing pairs in helical gears are the primary fatigue hotspots, making them more susceptible to fatigue failure. Among all gears, the sun gears exhibit the most severe contact fatigue damage, with estimated contact fatigue life of 8 years for the high-speed sun gear and 21 years for the low-speed sun gear. Conversely, the planetary gears in both stages show relatively lower fatigue damage, with their contact fatigue life exceeding 30 years. The ring gear experiences the least fatigue damage, resulting in an exceptionally long service life.

To investigate the dynamic response of the multi-bucket jacket foundation of offshore wind turbines under storm surge load, this paper conducts a series of 1 g model tests using the motor servo random dynamic loading equipment. Then, the influence of different depth-to-diameter ratios of the suction bucket on the soil and structure is further investigated through numerical simulations. The results indicate that the extreme horizontal displacement of the structure rises as wind speed increases under storm surge conditions, and the amplification coefficient of horizontal displacement follows a trend of “increasing initially before decreasing”. The peak value of the Fourier amplitude spectrum for the nacelle position acceleration increases with the load, while the peak frequency exhibits a pattern of “decreasing first and then increasing”. As the depth-to-diameter ratio of the suction bucket increases, both the extreme horizontal displacement and the amplification of the structures coefficient corresponding decrease. A depth-to-diameter ratio of L/D= 0.5 is the least favorable, as it cannot withstand storm surge loads. The difference between depth-to-diameter ratios of 1.0 and 1.5 is negligible. Taking into account reliability and economy, a depth-to-diameter ratio of 1.0 is the optimal type. Different depth-to-diameter ratios also have the influence onthe rotation center of the SBJ foundation.

To enhance the accuracy of ultra-short-term wind power forecasting and support efficient power system dispatch, this study proposes an integrated multi-algorithm forecasting model. The variational mode decomposition （VMD） is firstly applied to suppress noise and reconstruct the original power sequence. In the modeling stage, a long short-term memory （LSTM） network is adopted to capture temporal dependencies, followed by a multi-layer convolutional neural network （CNN） to extract local features. A Self Attention mechanism is further incorporated to dynamically focus on critical time steps, resulting in a collaborative multi-module forecasting framework. To evaluate the performance of the proposed model, ablation studies, comparative experiments, and seasonal transfer tests were conducted using data from a wind farm in Shandong Province, China. The results show that, compared to baseline models, the proposed model reduces the mean absolute error （MAE） by 23.5% and the root mean square error （RMSE） by 20%, highlighting the advantages of each module in modeling complex temporal patterns. Additional validations in cross-regional （wind farms in Central and Western China） and cross-energy （photovoltaic plants） scenarios further demonstrate the model's strong generalization capability.

Under low wind speed conditions, conventional maximum power point tracking （MPPT） control strategies fail to effectively account for the dynamic influence of the pitch angle, leading to reduced wind energy utilization efficiency. Under medium and high wind speed conditions, traditional PI-based pitch angle controllers struggle to maintain stable power output. To address these challenges, this paper proposes a collaborative optimization strategy that integrates Physics-Informed Neural Networks （PINNs） with Model Predictive Control （MPC）. The PINN model accurately captures the dynamic characteristics of wind turbines, while an online training mechanism based on a stochastic weight adjustment algorithm enhances adaptability. A unified MPC strategy is developed, considering both torque and pitch angle as control variables. By dynamically adjusting the cost function, the proposed approach achieves maximum power tracking under low wind speed conditions and ensures stable power output under medium and high wind speed conditions. Experimental results validate that the proposed method significantly improves the dynamic response of the wind turbines, enhances real-time control performance, and increases wind energy utilization efficiency.

Adopting the CEL finite element method to systematically simulate the penetration process of rectangular spudcan foundations under typical offshore wind geological conditions. Based on simulation results, the extent of plastically strained soil around the foundation is quantified, soil softening zones are identified, and corresponding strength reduction factors are calculated. Subsequently, small-strain finite element model incorporating these softening parameters are developed to calculate directional stiffness reduction coefficient η. Comparative analyses with non-softening reference models reveal the substantial influence of soil strength degradation on foundation stiffness. A comprehensive parametric study further evaluates the sensitivity of stiffness characteristics to two critical geotechnical parameters： the normalized shear stiffness ratio Gmax/su and the critical shear strain at failure γfp. Results demonstrate that：1） Soil softening effects varies with different directional in-situ stiffness components, but the extent cannot be neglected; 2） Stiffness reduction coefficients exhibits consistent decreasing trend with increasing level of loading;3） Stiffness increases with higher Gmax/su, but decrease with larger γfp.

In order to reduce the wake loss in the wind farm and enhance the whole field power generation, the whole field cooperative control based on yaw control has become a current research hotspot. To address issues such as existing studies not fully considering the dynamic changes in incoming flow and the time-lag effects of wind farm flow, a high-fidelity large eddy simulation method is used to numerically simulate the wind farm flow, and the flow delay time between units is determined by the spatial and temporal correlation between the flow field and the power of the neighboring units. Considering the flow delay time of the flow field, a dynamic yaw control strategy is proposed for the wind turbine under the turbulent inflow conditions. The large eddy simulation results show that the total power generation of the wind turbine array under static yaw and optimal dynamic yaw control is increased by 11.5% and 12.5%, respectively, compared with that under no yaw control. Compared to the static yaw control, the wake development between the wind turbine array is more fully developed and the wake velocity recovery is faster under dynamic yaw control.

The issue of wind turbine icing under the mixed-phase of ice crystals and supercooled water droplets is studied. The mechanism of wind turbine mixed-phase icing is analyzed, and the numerical simulation of airfoil icing on wind turbines is carried out. A particle motion trajectory equation is established using the Lagrangian method. The solution method for the control equation and the relevant definition of the collection coefficient are provided. A new mathematical model for icing under mixed-phase conditions is proposed, and the mass and energy conservation equations for liquid water on the surface of a wind turbine airfoil under mixed-phase conditions are established. The mathematical expressions for each equation are provided, and the physical phenomena of ice crystal adhesion and erosion are analyzed. The solution method for the icing growth model is provided. The accuracy of the icing model method proposed in this paper is demonstrated through comparison with experimental results. The icing characteristics of wind turbine airfoil surfaces under mixed-phase conditions are studied, and the impact of various initial parameters on icing characteristics is investigated. The research results indicate that erosion phenomena can affect surface icing. As the erosion rate increases, the surface icing decreases. The higher the adhesion coefficient and melting ratio, the greater the amount of icing. Different temperatures can lead to the formation of various ice forms on the airfoil. Rime ice forms at higher temperatures, while glaze ice forms at lower temperatures. However, with the increase in ice crystal content and diameter, the range and amount of icing change less. The research work provides a foundation for further research on wind turbine icing under mixed-phase icing conditions and the design of anti-icing and de-icing systems.

To reduce the weight of large wind turbines, structural stiffness is often compromised, leading to local buckling instability and trailing edge structural damage in composite blades under large-scale deformation. To address this issue, this paper introduces an inner rib structure for wind turbine blades to examine its effectiveness in improving resistance to buckling-induced damage. Structural damage response analysis was conducted using the explicit dynamic method under ultimate load conditions at extreme wind speeds and three yaw angles. The results indicate that trailing edge damage in blades is primarily a buckling-driven process, with significant trailing edge buckling occurring only when bending moment loads are applied at specific angles. The support provided by the inner rib in bionic blades localises damage to the transition section, effectively preventing its progression to the blade root section. Under tensile loads, damage concentrates in the trailing edge of the transition section, whereas under compressive loads, damage propagates laterally along the main beam. The buckling resistance of composite layers varies with ply angles, resulting in distinct damage patterns.

To mitigate the risk to secure and adequate operation of power systems under storm conditions, this paper proposes a synergistic control decision-making method aimed at enhancing adequacy and power angle stability. Firstly, the output from wind farms outside storm-affected areas is predicted using the Informer model, providing input for subsequent optimization models. Secondly, based on a “decoupling optimization, aggregation and coordination” strategy, the method decouples system adequacy optimization from power angle stabilization control. For adequacy optimization, operational risk costs are reduced by scheduling adjustable resources. For power angle stabilization, the extended equal-area criterion quantifies the transient stability margin, and preventive-emergency control synergy is applied for each fault scenario to ensure stability. A two-layer optimization model is then constructed and solved iteratively. Finally, case studies on a modified IEEE 39-node system demonstrate the benefits of the proposed model in reducing system load loss and enhancing grid security.

By improving the forecasting accuracy of model products through wind speed deviation correction methods, reliable data support is provided for wind power generation forecasting in wind farms. Taking the Lingchuan County Wind Farm in Shanxi Province as an example, three machine learning methods recurrent neural network （RNN）, nonlinear model（NLinear）, and Transformer were employed to establish wind speed deviation correction models for CMA-WSP2.0 model products. The results show that the results of three methods are better than that of the original model products, and Transformer and NLinear perform better in improving the accuracy of wind speed at heights of 10 m meters and 100 m meters, respectively. Therefore, machine learning methods can effectively improve the quality and reliability of wind speed data, offering a more accurate data foundation for wind energy resource development and utilization, power forecasting and other fields.

In order to address the problem of high slagging rate of corn straw combustion, mixing oil pine with less slagging feature and corn straw to produce the mixture combustion, using thermogravimetric analysis, SEM scanning and energy spectrometry, the effect of oil pine on the combustion characteristics of corn straw and slagging characteristics is studied. The results show that: there is a certain interaction during the combustion process of the oil pine-corn straw mixed fuel. As the temperature rises, it changes from inhibitory effect to synergistic effect, and then from strong synergistic effect to weak synergistic effect. When the mixing ratio of corn straw and oil pine is 4:6, the overall combustion characteristics are the best, the combustion temperature increases to 475.6 ℃, the stable combustion index increases to 8.9379×10^-6 mg/（K²·min） and the combined combustion characteristics index increases to 0.0929×10^-8 mg²/（K³·min²）, and the slagging rate decreases from 56.25% to 9.87% with the blower strength of 0.1 m/s, the slagging rate decreases by 82.45%, and the content of Si and Na, K alkali metal elements in the ash decreases from 46.42% to 29.93%. The addition of oil pine can significantly enhance the combustion characteristics of corn straw and effectively reduce the slagging rate.

The project boundary,baseline emissions and project emissions of fuel ethanol projects were defined. The greenhouse gas （GHG） emission of 3 generations of bioethanol projects,including corn liquid fermentation,sweet sorghum solid fermentation and cellulose hydrolysis fermentation were analyzed. By introducing a secondary emission reduction accounting system for the resource utilization of by-products, the maximum carbon emission reduction potential of each generation of bioethanol processes was systematically revealed. The results show that the sweet sorghum solid-state fermentation has the highest carbon emission reduction potential, with the maximum theoretical 5.14 ton CO₂e GHG abatement per ton of ethanol production. Cellulosic ethanol has greater carbon negative potential under the background of technological progress.

In this paper, MCM-41 mesoporous molecular sieve catalysts modified by Cu, Fe, Co and Ni elements were prepared by impregnation method. XRD and BET characterization analyses were carried out on the modified catalysts to explore the physicochemical structures and other characteristics of catalysts modified by different metals.The effects of co-pyrolysis of cellulose, lignin and polypropylene （PP） on the synergistic reaction and the distribution of pyrolysis products were investigated by TG-FTIR and Py-GC/MS. The results show that MCM-41 can promote the reaction between biomass and PP, and the yields of aromatics and light hydrocarbons are increased. Compared with MCM-41, modified MCM-41 can promote the precipitation of light olefins, C₅~C₁₁ aliphatic hydrocarbons and aromatics. Cu, Fe and Co modification can promote the formation of monocyclic aromatics. Compared with MCM-41, MP-M/Fe has the highest proportion of olefin precipitation, reaching 59.68%, and XP-M/Fe has the highest proportion of light olefin precipitation, reaching 32.84%. XP-M/Co has the highest proportion of alkane precipitation, reaching 34.22%. The proportion of other groups of alkenes and alkanes in the pyrolysis products increases.

Taking the three-dimensional horizontal axis tidal current turbine as the initial model, the influence of the winglet twist angle on the hydrodynamic performance and wake characteristics of the tidal current turbine is analyzed by CFD numerical calculation, and the influence mechanism of the winglet's twist angle on the hydrodynamic performance of the turbine blade is clarified. The results show that the change of the winglet twist angle has a significant impact on the hydrodynamic performance of the tidal current turbine. Compared with the initial model, when winglet twist angle is -6°, the power coefficient of the tidal current turbine increases by 5.17% and the drag coefficient increases by 7.01%. In the range of twist angle from -9° to 6°, the power coefficient with winglet first increases and then decreases with the increase of twist angle, while the drag coefficient decreases monotonously. The changes of the winglet twist angle significantly affect the radial flow state of the fluid immediate behind the blade tip of the turbine, affect the tip loss and the surface pressure distribution, and then change the hydrodynamic performance of the tidal current turbine. By comparing the power spectrum density curve of velocity fluctuation, it can be seen that the winglet's twist angle will affect the axial velocity fluctuation of blade tip vortex, and the influence will gradually decrease with the increase of axial distance, but the influence on radial velocity fluctuation is not obvious.