To mitigate source-load bilateral uncertainty and fully tap into low-carbon dispatchable resources on both the source and load sides, a heterogeneous energy collaborative forecasting scheduling method considering source-load low-carbon complementarity and matching is proposed. First, a forecasting model for wind power, photovoltaic power, and power load is developed based on Laguerre polynomials, pseudo-inverse learning, and ensemble learning. This model balances forecast accuracy with stability to reduce uncertainties on both the source and load sides. Second, low-carbon dispatchable resources on the source and load sides are tapped into, and the complementary advantages of low-carbon characteristics between the two sides are analyzed. A low-carbon complementary operation mechanism is proposed. On the source side, an integrated flexible operation mode of a carbon capture power plant is introduced. Wind power, photovoltaic power, and concentrated solar power plants are adopted for coordinated power supply to provide zero-carbon power output. On the load side, logistic active demand response is considered to optimize the power load distribution. A ladder-type carbon trading mechanism is incorporated to control power system carbon emissions. Finally, a source-load matching coefficient is introduced to account for the matching degree between power output and power load. A multi-objective dispatch model is established with the objectives of maximizing the source-load matching degree and minimizing the comprehensive economic cost. Validations on 10-unit and 20-unit power systems demonstrate the favorable robustness and engineering applicability of the proposed method, aiming to provide an effective solution for the low-carbon, economic, and secure dispatch of power systems.

Ice accretion on transmission lines imposes an additional mechanical load on conductors and may affect the safe operation of the power grid. Therefore, reliable estimation of ice thickness is of great importance for online condition monitoring of power transmission systems. A method based on fiber Bragg grating (FBG) sensing is proposed for estimating the ice thickness of transmission lines. Under icing load, an icing load model, a conductor mechanical response model, and an FBG sensing model are established to describe the relationship between conductor strain variation and FBG wavelength shift. On this basis, a quantitative relationship between icing thickness and FBG wavelength shift is obtained. Simulation analysis is conducted to investigate the effects of icing-thickness variation on conductor strain and the corresponding optical response. The results show that the conductor strain increases monotonically with increasing ice thickness, and the FBG wavelength shift exhibits a clear linear response to strain. In addition, the estimated icing thickness shows good agreement with the theoretical value under the simulated conditions. These findings support the feasibility of the proposed method under simulated conditions. The proposed method also provides a quantitative basis for comparing icing-thickness results from different monitoring methods.

The rapid proliferation of electric vehicles (EVs) has introduced significant challenges to the efficient operation of hydrogen-containing integrated energy systems (H-IESs). To cope with these challenges, this paper develops a bi-level optimal scheduling strategy for H-IESs that simultaneously incorporates a ladder-type carbon emission trading mechanism, demand response, and the operational characteristics of EVs. A demand response model is formulated by considering the coupling characteristics of electric and thermal loads. Price-based incentive signals are further designed to coordinate the interactions between the H-IES operator and EV users, enabling flexible resources to actively participate in system scheduling. In the proposed bi-level framework, the upper-level problem aims to minimize the total operating cost of the H-IES, while the lower-level problem seeks to reduce the charging cost of EV users. The resulting bi-level optimization problem is reformulated and solved using the Karush–Kuhn–Tucker (KKT) conditions. Case study results demonstrate that, compared with the single-level benchmark, the proposed bi-level strategy reduces the total operating cost by 34.79% and lowers the EV charging cost by 4.50%.

Integrated Demand Response (IDR) enhances the operational flexibility of Integrated Energy Systems (IES) and promotes renewable energy integration. However, limited interaction between the Integrated Energy Operator (IEO) and users during actual energy transactions can lead to biases in IDR planning, compromising user response effectiveness. To address this, this paper proposes a method for revising IDR stimulus parameters in IES based on gradient descent within a Stackelberg game framework. First, an IDR model based on consumer psychology principles is constructed to establish an IES Stackelberg game, in which the IEO acts as the leader and the load aggregator acts as the follower. Subsequently, during the game, the IEO utilizes users’ energy consumption strategies to adjust the stimulus threshold parameters of the dead zone and saturation zone along the negative gradient direction, thereby updating its decision for the next round. Furthermore, a response adjustment mechanism is designed to refine the IDR plan, enhancing its feasibility. Finally, comparative analyses across diverse scenarios demonstrate that the proposed method reduces deviations in planned IDR, thereby enhancing the low-carbon performance and renewable energy integration capacity of IES.

To address the challenge of simultaneously achieving accuracy at low speeds and stability at high speeds in the lateral control of unmanned vehicles, this paper proposes a feed-forward adaptive weight controller based on model predictive control (FFMPC) aimed at enhancing both accuracy and adaptability during the lateral control of these vehicles. By considering the coupling characteristics of trajectory and heading errors, we derive an error dynamics model that incorporates path curvature and its rate of change into the conventional dynamics model. The main control system is constructed using a model predictive controller with decoupling characteristics, obtained through Taylor series expansion. Additionally, a Sliding Mode Controller (SMC) based on an integral proportional-integral-derivative (PID) sliding mode surface is integrated as a feed-forward component of the control system. To prevent overfitting in the controller, we define an error tolerance threshold. The front steering compensation angle is calculated using the improved preview model, which effectively adjusts the output of the primary controller and enhances system robustness under extreme operating conditions. In this study, trajectory error minimization is employed as a key performance metric for simulation. Comprehensive simulations are conducted in the Carsim and Matlab/Simulink environment under various operating scenarios for comparison and validation. The experimental results indicate that, compared to traditional controllers, the FFMPC demonstrates superior accuracy, robustness and rapid convergence of kinematic states.

In recent years, with the increasing integration of renewable energy, the operation characteristics of generation, grid, and load ("source-grid-load") have become highly heterogeneous and dynamic. Traditional single-dimensional data modeling methods in power systems can no longer support the collaborative operation required by modern power systems. The standardization and collaborative modeling of energy data have become critical factors in improving system intelligence. By establishing a unified data standard model system for the "source-grid-load" architecture, it is possible to achieve deep integration and cross-domain interaction of data in multi-energy complementary scenarios, thereby laying a solid foundation for system operation optimization.This paper elaborates on the core approach to constructing a multi-source collaborative data model by focusing on three key technical aspects: data acquisition, transmission, and platform development, with a strong emphasis on data security. It systematically analyzes the adaptability of existing models such as CIM and IEC 61850 in heterogeneous environments and proposes integration mechanisms for advanced technologies including digital twins, graph neural networks, and federated learning within new data systems. Research findings indicate that the proposed model system significantly enhances data processing efficiency and security in real-world applications, facilitating the intelligent integration of source-grid-load operations. Through case studies and analysis of standardization trends, the study underscores the need to expand modeling capabilities towards hydrogen and integrated energy systems, promote collaborative standard systems, strengthen data governance mechanisms, and enable trustworthy data sharing across stakeholders—thereby supporting the low-carbon, intelligent, and resilient transformation of energy systems.

With the progress of science and technology and the rapid development of aerospace, the attitude control of spacecraft has attracted extensive attention at home and abroad, but the acquisition of nonlinearity and angular velocity has always been a difficult problem for its control and precise maneuvering.Based on the problem of nonlinearity and angular velocity acquisition, this paper uses the quaternion method to accurately describe the attitude of the spacecraft, and verifies the feasibility of the powering integral controller and the supercoil observer. Compared with the traditional PD controller and the Lomborg observer, the simulation results show that the exponentiation integral controller is better than the PD controller in terms of anti-disturbance, speed and stability. The supercoil observer is superior to the Lombberg observer in terms of rapidity and stability of angular velocity observation. Therefore, this study has practical application value.

Bearing fault diagnosis is a critical task for ensuring the safe operation of industrial equipment. However, traditional methods suffer from low fault identification accuracy and computational inefficiency due to the time-varying characteristics of non-stationary vibration signals and the scarcity of fault samples in industrial scenarios. This paper proposes a method named "CNN-LSTM with Complex Wavelet Transform (CWT) and Squeeze-and-Excitation (SE) Attention Mechanism (SEAM)", which can achieve high-precision fault identification under small-sample conditions by extracting and deeply fusing spatiotemporal features of faults. First, this paper elaborates on the theoretical foundations underpinning the key components of the proposed method. Subsequently, experimental validation is performed using the bearing dataset from Case Western Reserve University (CWRU). The results indicate that the proposed fault diagnosis method achieves 99.7% average diagnostic accuracy with limited training samples, while maintaining 99% accuracy even when the sample size is further halved. Finally, two critical conclusions are ultimately drawn.

To overcome issues related to acoustic disturbances, inadequate characteristic identification, and extended temporal correlation difficulties in forecasting bearing remaining operational lifespan (RUL), this research introduces a CEEMDAN-BiGRU framework. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) preprocessing suppresses noise and extracts multi-band features, resolving the issues of mode mixing. The Bidirectional Gated Recurrent Unit (BiGRU) network captures bidirectional degradation patterns through efficient temporal modeling, wavelet denoising and polynomial fitting enhance the prediction interpretability. Tests conducted using the PHM-IEEE-2012 benchmark reveal the suggested approach attains a mean absolute error (MAE) of 0.0438 alongside a relative error (RE) of 1.85%, outperforming models based on EMD/EEMD decomposition and networks such as LSTM, BiLSTM, and GRU, which validates its robustness and prediction accuracy under complex operating conditions.

The integration of hybrid energy storage systems is intended to alleviate the inherent intermittency and volatility inherent in wind power generation, thus improving grid stability and power quality. Firstly, based on grid connection conditions and historical data, an optimized threshold-based target domain determination method is adopted. Through an objective function balancing threshold size and excess penalties with constraints, optimal fluctuation and error thresholds are obtained to establish the joint action domain and derive active reference power. Secondly, CEEMDAN decomposes the domain's active power demand into IMF sub-components. Multi-dimensional features (center frequency, energy ratio, sample entropy) are extracted, and TOPSIS is applied to distinguish high/low-frequency components, obtaining reference power for each storage type. A multi-objective model that takes into account the minimization of life cycle cost and performance optimization is subsequently formulated and solved using a multi-objective particle swarm optimization algorithm. Results show the strategy reduces power gaps, optimizes grid connection, and enhances power grid stability and reliability.

Aiming at the problem of low recognition accuracy due to high feature dimensionality, redundancy and inconspicuous key features in the process of straw crushing tool wear state recognition, the study designed a hybrid feature selection (HFS) with maximum mutual information coefficient (MIC)combined with support vector machine recursive feature elimination(SVM_REF) and improved newton–raphson-based optimizer (INRBO) to optimize the extreme gradient boosting (XGBoost) method for tool wear state recognition. improved newton–raphson-based optimizer (INRBO) to optimize extreme gradient boosting (XGBoost) for tool wear state identification. Initially, the vibration acceleration signal undergoes detrending and noise reduction, followed by the extraction of its time-domain, frequency-domain, and multi-scale arrangement entropy to compile a multi-domain feature set. Subsequently, redundant features are eliminated through a HFS approach, and INRBO-XGBoost is deployed for tool wear state identification. Ultimately, comparative experiments are executed on the proposed methodology. The results of these experiments demonstrate that the proposed method attains a recognition Accuracy of 99.12

In the intelligentization process of open-pit mines, the detection of safety hazard targets is confronted with technical challenges, including blurred features of small targets, extreme multi-scale distribution of targets, and severe background interference under complex working conditions. An improved lightweight safety hazard target detection algorithm for open-pit mines is proposed based on RT-DETR. Firstly, the lightweight PConv-Block module is incorporated into the backbone feature extraction network, which precisely captures the features of every safety hazard while notably lowering the model’s computational complexity. Secondly, by combining multi-scale feature fusion with the Slimneck structure, the Slimneck-SSFF cross-scale feature fusion framework is proposed; this framework not only enhances adaptability to safety hazards of different scales but also reduces the network computational load. Finally, the GIoU loss function is adopted, and detection accuracy is significantly improved through dynamic adjustment of weight allocation for hard-to-detect safety hazard samples. Test findings show that, compared with the original RT-DETR model, the enhanced version sees a 30% drop in parameter quantity, while its mAP50 metric hits 91.7%. These outcomes fully validate the effectiveness of the proposed approach in detecting safety hazards in open-pit mines.

Hydrogen is a crucial part of achieving the dual carbon goals. Various safety concerns regarding hydrogen, including its high leakage risk during storage, transportation and use, have impeded its large-scale deployment. This paper analyzes safety challenges and emerging safety technologies for hydrogen across its lifecycle. The insights provided form a foundation for ensuring its reliable and secure deployment.

This study establishes a multi-objective optimization framework based on equipment safety performance to address operational hazards such as excessive mechanical vibration and critical component fatigue in chain-tooth residual film recovery machines under complex working conditions. Through finite element analysis and vibration testing, a backpropagation neural network was trained using simulation data to develop a safety evaluation model integrating impurity content rate (ICR), film recovery rate (FRR), and machine stability. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed to synergistically optimize operational parameters, achieving an enhanced FRR of 83.67% alongside significantly reduced vibration levels, thereby elevating the equipment's comprehensive safety rating. This methodology effectively balances the conflict between operational efficiency and safety protection, offering a new paradigm for safety-oriented parameter optimization in agricultural machinery.

Addressing the challenges of multi-energy systems in data collection and integration — such as heterogeneity, spatial disparities, and temporal scale mismatches—this paper investigates the characterization and modeling of renewable energy generation data. First, by modeling the principles and output characteristics of wind and photovoltaic power generation, we analyze their nonlinearity, non-smoothness, and multi-scale properties. To address the difficulty of extracting actionable insights directly from raw data, we introduce signal decomposition techniques and propose a refined analytical method for wind power daily profiles based on K-means clustering and variational mode decomposition (VMD). The K-means algorithm clusters wind power data into typical daily profiles, while VMD decomposes the signals, with the modal number K optimized via average sample entropy to precisely extract structural features. Experimental results demonstrate that this method improves data interpretability and enhances prediction accuracy. This study not only advances standardized processing methods for renewable energy data but also contributes to the development of a holistic "source-grid-load" energy data framework.

This paper analyzes patents in wind turbine fault diagnosis technology using data from the Incopat and Innojoy platforms. It explores technological development and innovation trends, noting that while markets in developed regions like Europe, the U.S., and Japan are stabilizing with declining patent activity, China shows significant growth potential. Leading companies are expanding patent layouts and transferring technology to emerging markets, with Chinese enterprises working to bridge gaps in technology and patents. The paper investigates application trends, regional distribution, major applicants, and key technological components, identifying research hotspots and challenges. Methods include patent retrieval, text analysis, and trend forecasting, aiming to highlight technical gaps and research deficiencies. The study offers theoretical support and practical guidance for advancing wind turbine fault diagnosis, contributing to the growth of China’s wind power industry.

The increasing energy demand of data centers highlights the necessity of exploring joint optimization strategies for scheduling and energy management within data centers. This study establishes a data center cluster (DCC) framework composed of a DCC operator (DCCO) and data center prosumers (DCPs). Furthermore, a two-stage energy sharing model is developed, incorporating the integrated demand response (IDR) across multiple loads. The first stage is the day-ahead optimization stage, in which the probability distribution uncertainties of wind power, photovoltaic power, and loads are fully considered, and a shared energy storage (SES) optimization scheduling method based on the worst conditional value-at-risk is constructed. The second stage is the real-time optimization stage; first, a new peer-to-peer (P2P) trading mechanism based on electricity and heat supply/ demand ratios is designed to realize the joint sharing of electricity and heat among DCPs; then, a refined IDR model that considers the temporal-spatial transferability of data loads, household appliance flexibility, thermal retardation and thermal comfort is demonstrated, and some metrics such as efficiency improvement ratio are introduced to evaluate the IDR model; finally, the benefit functions for both DCCO and DCPs are formulated. A Stackelberg game model for DCC is introduced, which incorporates the SES trading price determined by DCCO, along with the IDR and P2P trading strategies employed by DCPs. The results demonstrate that the proposed DCC framework and energy-sharing model achieve a 39.34 % reduction in the total daily operating costs of DCPs, while fostering mutual benefits and a win-win outcome for both DCCO and DCPs.

The manufacturing of high-end equipment cables, characterized by complex production processes and high customization requirements, faces challenges in meeting flexibility demands through traditional production logistics models. Digital transformation has thus become a critical pathway to enhance efficiency and competitiveness. This review summarizes research progress on digital scheduling technologies for logistics operations in high-end equipment cable manufacturing, covering the production workflow, modeling methods for digital scheduling problems in manufacturing logistics workshops, and solution algorithms for digital scheduling challenges. Finally, this paper summarizes existing research challenges and outlines future directions concerning technical pathways, system architecture, and value orientation, aiming to provide reference for related research and applications.

Driven by the goal of " dual-carbon " , wind-photovoltaic- electricity- hydrogen hybrid energy storage microgrids have become an important carrier to enhance the capacity of new energy consumption. Aiming at the problem of renewable energy fluctuation and uncertainty affecting the safety and health of energy storage equipment, a wind-power-hydrogen hybrid energy storage microgrid optimal scheduling model considering the health management of energy storage equipment is proposed. Firstly, a typical scenario of wind-electricity-equivalent uncertainty is established by using the Latin Hypercube algorithm and the K-means clustering algorithm. Secondly, the operating characteristics of battery, electrolyzer and fuel cell are analyzed, and the optimal scheduling model considering the health management of energy storage equipment is established by taking minimizing the operating cost, equipment health loss cost and penalty cost as the objective function, and finally, the Cplex solver is invoked to solve the model based on Matlab. The example results show that the method can effectively balance the economy with equipment safety and health, extend the service life of the energy storage equipment, and improve the operation efficiency of the microgrid.

In this paper, a heuristic function-improved deep reinforcement learning TD3-based robot navigation method is proposed for local navigation in a simulated substation environment. First, a 3D map of the nonstatic simulated substation environment is constructed. Second, 2D OU noise is added to the output action space to improve exploration efficiency. Then, a heuristic function based on state information is designed to adaptively adjust the hyperparameters of the OU noise action space. Finally, migration learning is used to train the robot for obstacle avoidance navigation in a simulated substation environment with dynamic obstacles added. The experimental results obtained by comparing the H-OU, H-Gauss and original Gaussian noise methods reveal that our proposed method achieves the highest average reward function among the three methods, with a 16.46