The collection of seabed ore particles is a core technology of exploiting deep sea mineral resources, with wall-attached jet collection technology based on Coandă-effect being considered as a nodule collection method with engineering application potential. Based on the experimentally verified CFD-DEM numerical simulation, the optimization of geometric parameters of the collection device is conducted to improve pick-up efficiency. The influences of three geometric parameters, i.e., the ratio of the curvature radius of the convex curved wall to the diameter of the nodule particle R/d, the tangential radian of the jet θ, and the ratio of the thickness of the jet to the diameter of the nodule b/d on the critical unconditional jet flow rate q₀, are investigated and compared. The nodule collection characteristics are revealed through an analysis of the flow field characteristics. The results show that b/d has the greatest influence on the pick-up efficiency, followed by R/d, while θ has the least. The performance of nodule collection is optimal when R/d=9, θ=1.05 rad, and b/d=0.26 in contrast conditions. This research provides technical support for designing and developing the Coandă-effect-based collection devices.

To enhance the accuracy of finite element model simulation, a model updating method based on Bayesian theory is proposed, and the updating efficiency is improved by integrating improved Markov chain Monte Carlo (MCMC) algorithm and surrogate model. A radial basis function (RBF) surrogate model is constructed using the parameters to be updated as inputs and the finite element model modal responses as outputs. Whale optimization algorithm (WOA) is introduced into the MCMC algorithm and the uncertain parameters are updated. Finally, a numerical study on a simply supported beam and an experimental study on a three-story steel frame are conducted to verify the accuracy of the proposed method. The results show that WOA can significantly improve the stability and convergence speed of the MCMC algorithm, the updating efficiency can be improved by 13.9% at most, and the maximum frequency errors of the simply supported beam model and the three-story steel frame model updated by the WO-MH algorithm are 0.009% and 2.41%, respectively. The proposed model updating method can effectively enhance the simulation accuracy of the finite element model under both two-dimensional and eight-dimensional inputs, which provides technical reference for lean simulation and optimal design of building structures.

Although the adaptive bistable wave energy generation device solves the problem that the bistable system may be difficult to cross the barrier when the amplitude of the incident wave is small, its efficiency can still be improved. Previous studies have proved that the change of the parameters of the device will have a great impact on its performance, and the optimal device parameters are closely related to the spectral peak frequency at a given time. Therefore, in the control study of the device, a control scheme is designed and the device parameters are adjusted accordingly in order to improve efficiency assuming that the peak frequency within a period of time is predictable. In this study, three control parameters are selected, and the optimal device parameter library with different spectral peak frequencies is determined by simulation calculation. The control module is then added to the simulation program to control the parameters by interpolation. The results show that the device with variable parameter control improves energy capture efficiency.

The electric power industry plays a pivotal role in carbon emission control. Accurate and real-time accounting of carbon emissions in the power industry is essential for supporting the carbon reduction of the power industry. At present, the measurement of carbon emissions in the power industry relies mainly on direct measurement or the accounting methods, which often struggles to balance low measurement costs with real-time accuracy. Therefore, in this paper, the robust power data infrastructure in the power industry is leveraged and the correlation between electricity consumption and carbon emissions is explored to propose a short-term electricity-to-carbon method using machine learning methods based on historical data of electricity. This method utilizes convolutional neural networks (CNNs) for feature extraction, and light gradient boosting machine (LightGBM) for carbon emission estimation based on extracted features. Moreover, K-fold cross-validation is used in model training, with parameter optimization using grid search to enhance the generalization capability and robustness of the model. To validate the proposed method, it is compared with other machine learning models under the same data segmentation condition for daily and hourly data sets. The results indicate that the proposed model outperforms other models in both performance evaluation and the consistency between estimated and target values.

This paper investigates vortex-induced vibration (VIV) characteristics of double free-hanging water transmission pipes, which are crucial for temperature difference energy harvesting platforms. Compared to a single pipe, double pipes could offer higher transport efficiency and cost-effectiveness. In this paper, model experiments were conducted to analyze VIV characteristics of the double free-hanging pipes and a method for identifying vortex-induced loads for large displacements and small deformations was proposed. A comparative analysis of the VIV characteristics of double free-hanging pipe and the single pipe was performed. The findings show that VIV displacement amplitudes of double free-hanging pipe are similar at low flow velocities but differ with those of single pipe at high velocities. The double free-hanging pipe is more prone to instability in VIV, including traveling waves and multi-frequency responses. The VIV frequencies of double free-hanging pipe can be predicted by the same Strouhal number as that of the single pipe. Additionally, a significant difference in the added mass coefficient affects natural wet frequency adjustment for VIV resonance.

To address the computational challenge posed by the “curse of dimensionality” inherent in traditional branch and bound algorithms for large-scale power grid unit commitment problems, an optimization method for iteration path search of unit commitment state is proposed. To prevent the loss of the optimal solution due to the simplification of the problem and the reduction of the feasible region, the determination of the unit state scheme is divided into a two-stage process of depth traverse and breadth iteration. Based on an initial solution, the unit dynamic priority list is used as the search direction for the unit state iteration path. In deep traverse stage, the optimal shutdown redundant units and their corresponding shutdown time are determined. Breadth iteration is then used to expand the feasible region of the problem to improve the optimality of the solution. The results of a comparative case study conducted on the IEEE 118 system and ACTIVSg10k system indicate that the proposed method effectively reduces the scale of the problem, minimizes the number of unit state attempts, and achieves efficient search and iteration of unit states, exhibiting fast computational speed, high efficiency, which has practical applicability for solving problems of large-scale unit commitment.

The study of the dynamic response of a horizontal axis twin wind turbine in tandem arrangement is crucial for ensuring the structural safety of the wind turbine. Based on the computational fluid dynamics (CFD) method, the characteristics of the wake flow field of the downstream turbine, located in the near wake region of the upstream turbine, are analyzed. The time course curves of the aerodynamic loads on the twin turbines are obtained. Structural dynamics and finite element numerical methods are then used to analyze the wind-driven dynamic effects of the upstream and downstream turbine structures. It is found that the wake velocity deficit in the near wake region is significant, causing a reduction in thrust and torque of the downstream turbine by 54.94% and 91.89% respectively. Additionly, the wake turbulence increases cyclic fluctuation of aerodynamic load on the downstream turbine. While the aerodynamic load volatility has a small effect on the dynamic response of the downstream wind turbine, the overall dynamic response is weaker, and the displacement of the downstream wind turbine tower top in the thrust direction is reduced by 50.79%. The results provide technical references for the analysis of aerodynamic response of wind turbine cluster structures in offshore wind farms.

The internal energy optimization within a single entity of industrial users, base stations, and charging stations is constrained by local power supply and demand limitations, resulting in low utilization of flexible resources such as energy storage and insufficient energy utilization efficiency. To address these issues, an energy sharing and interactive optimization method is proposed for industrial users, base stations, and charging stations based on the quantification of their complementarity and a game-based pricing incentive mechanism. First, a complementary quantification model is developed based on the analysis of the characteristics of industrial users, base stations, and charging stations, using the standard deviation of net load as a complementary indicator. Then, considering the adjustable capabilities of air conditioning and electric vehicles, as well as the proactive decision-making abilities of industrial users, charging stations, and base stations, a master-slave game-based pricing model is established to incentivize the sharing of energy storage and energy interaction among these entities. Next, incorporating 0-1 integer variables, a solution method utilizes the adaptive differential evolution algorithm combined with the mixed-integer optimization theory. Finally, case studies validate that optimizing the energy storage and energy dispatch of industrial users, base stations, and charging stations in different time periods can effectively leverage their complementarity, enhance the economic benefits of each entity, improve the utilization of idle flexible resources, and enhance the overall energy self-consistency of the system.

With the increasing penetration of new energy sources and the development of new power systems, grid-following converter (GFL) plays a crucial role in maintaining the stability of power systems. However, existing transient stability analyses of GFLs assume that the direct current (DC) side behaves as a constant-voltage source, neglecting the effects of DC-bus voltage control. This paper aims to investigate the transient instability mechanism of GFL considering DC-bus voltage control. First, a transient synchronous stability model considering DC voltage control is established, followed by an analysis of the transient synchronous stability of GFL under DC-bus voltage control. The findings indicate that DC voltage control increases the active current reference value and decreases the equivalent damping of the GFL, which in turn reduces its transient synchronous stability of GFL. By increasing the proportional coefficient or reducing the integral coefficient of DC-bus voltage control, transient synchronous stability can be appropriately improved. Finally, the theoretical analysis is validated through MATLAB/Simulink simulations.

During the block assembly, large curved thin plates (such as outer plates) undergo deformation due to the force of gravity when they are placed on the jigs, which affects the accuracy and quality of the block assembly in shipbuilding. In order to predict the deformation of these large curved thin plates within a given jig layout, this paper introduces a Transformer-based surrogate model with two-stage Latin hypercube sampling (TSM-TLHS). Primarily, compared to traditional approaches, the two-stage Latin hypercube sampling (TLHS) method enables direct sampling of irregularly shaped thin plates. Simultaneously, this paper uses a Transformer-based surrogate model (TSM) incorporating multi-head attention modules and positional encoding to comprehensively consider the impact of jig positions and corresponding node displacements on thin plate deformation. Real case results demonstrate that the prediction error of this TSM-TLHS method is only 61 μm, meeting the on-site assembly precision requirements for predicting plate deformation. This facilitates timely anti-deformation compensation by block in shipyards, ensuring assembly quality.

The distributed photovoltaic power generation system is widely dispersed and lacks a scientific and standardized operation and maintenance management system. Due to the limited availability of data, it is difficult to accurately detect abnormal conditions in photovoltaic devices caused by fluctuations in weather. In this paper, according to the operation and maintenance status and data characteristics of distributed photovoltaic, a quantile regression neural network (QRNN)-based method is proposed for detecting photovoltaic power outliers. First, the solar irradiance characteristics of sunny days are analyzed, and the influence of rainy weather is excluded by using a sunny day screening method. Then, the power output correlation of different power stations is analyzed to identify the photovoltaic stations with high power output correlation, which is used as a horizontal reference. Subsequently, the curves of the power output of the stations tested on different sunny days are compared vertically to eliminate the interfering factors such as weather and environmental conditions. The measured active power data is fed into the QRNN model to establish the normal active power range for the photovoltaic system, whose threshold is used to detect photovoltaic power outliers. The simulation results of actual photovoltaic system data show that the method proposed can eliminate the meteorological influence, accurately identify the faulty photovoltaic system, and promote the fine operation and maintenance of distributed photovoltaic.

Identifying electricity price anomalies and exploring the underlying reasons in such a complex market environment, especially with incomplete data, is a key issue for promoting the orderly operation of power market and ensuring the reasonable interests of power customers. Therefore, a method is established for feature extraction and anomaly identification of electricity prices for power customers. First, an electricity price feature vector is constructed, and its dimensionality is reduced using a spectral clustering algorithm. Then, typical electricity price characteristics are extracted as the basic standard for determining price anomalies. Next, the similarity between each power customer and typical electricity price characteristics is calculated. Finally, electricity price anomalies are identified in two stages. The causes of anomalies are initially and rapidly identified based on electricity consumption and trading behavior, and then further identified in-depth. Case analysis shows that this method can quickly and effectively extract typical electricity price features and identify anomalies. The reasons behind these anomalies are further analyzed from both electricity consumption and trading behaviors, and corresponding improvement measures are proposed.

Parallel gas-electric hybrid systems have broad application prospects in low-carbon ships due to their few emissions and dynamic performance. However, uncertain delays in multiple actuators during mode transition can cause violent fluctuations in the shaft speed along the power drive. In this paper, a cascaded internal mode control (IMC) consisting of filters with explicit nominal delay is proposed to improve speed tracking performance and eliminate the effect of delay. A dynamic model of the marine driveline is developed, and the cascade IMC is designed based on the driveline mechanism with the clutch serving as the separating component. The cascade IMC consists of an anti-saturation compensator, a two-stage tracking controller, and a two-stage anti-interference controller. Finally, the small-gain theorem is derived to ensure robust stability conditions, taking the upper bound of the uncertain delays into consideration. The results of simulation and dynamometer test show that the cascaded IMC has excellent robustness in handling uncertain delays, significantly reduces shaft jerk, and ensures smooth mode transition.

As a key component in the subsea production system for oil and gas exploitation, a marine umbilical consists of optical cables, electrical cables, steel tubes, and filler bodies. The difference of materials and dimensions between the components leads to a great difference in their mechanical properties, and the different layouts cause a large gap in the performance of an umbilical. Considering the compactness, balance, and heat source dispersion of the cross-section, a multi-objective optimization model is established in this paper. Based on the quasi-physical algorithm, the layout design of cross-section of an umbilical containing equal-diameter components is conducted. Due to the mutual constraints between functional components, the optimized cross-section will have large gap. In order to meet the requirement of dense cross-sectional layout in the umbilical cable design specification, a strategy for automatically filling filler bodies based on image recognition is introduced, in combination with the layout optimization process. Finally, taking an umbilical as an example, the filling strategy is utilized to complete the design of cross-sectional filler bodies after obtaining the optimal layout through the quasi-physical algorithm. The algorithm is validated by comparison with the initial cross-sectional layout, demonstrating its effectiveness as a reference for the design of cross-sectional filler bodies of umbilicals.

Newly built microgrids lack historical operation data, making it challenging to predict renewable power output accurately using conventional data-driven methods, which in turn affects the accuracy of scheduling plans. To address this problem, an optimal scheduling method for newly built microgrids in scenarios with limited sample data is proposed. First, an improved network structure integrating a domain adversarial neural network with a long-short-term memory network is designed. The domain adversarial approach and gradient inversion mechanism are incorporated into transfer learning to improve the generalization ability of the model. This reduces the domain distribution discrepancy in the data, and uses the rich operation data of power stations with similar output characteristics to predict the output of the target station, which overcomes the challenge of poor accuracy under the conditions of small samples. Additionally, the optimal scheduling model is transformed into a Markov decision process and solved using double-delay deep deterministic policy gradient algorithm. Finally, the effectiveness of the proposed method is validated through a case study involving an improved CIGRE 14-node microgrid.

Thermal power units face a dilemma of accelerated lifespan degradation and extended service duration. On one hand, large-scale integration of new energy sources has increased peak shaving conditions and accelerated losses of the units. On the other hand, service units will reach designed lifespan before carbon neutrality is achieved, while flexible operation of the power system necessitates extending their service life of units. Therefore, it is of great significance to consider the losses caused by varicus operating conditions on the lifespan of the unit and optimize the operating structure of the unit in scheduling simulation for unit longevity and carbon reduction efforts. To make unit life losses in theoretical research more practical, the traditional model that averages the losses in deep peak shaving conditions has been discarded. Instead, new judgment criteria for conventional and various special operating conditions of thermal power units are established. The lifespan loss cost of the unit is integrated into the operating objective function and the corresponding constraint conditions are modified. Finally, a unit commitment model considering the multi-operating condition lifespan losses of thermal power units is constructed. Example simulations indicate that the conventional model underestimates the actual loss cost of the units. In constrast, the proposed model can not only reduce the operating cost and unit life loss by considering the lifespan impacts of multi-operating conditions, but also enhance the peak shaving capacity of thermal power units and promote wind power consumption.

Against the backdrop of “dual carbon” goals, the construction of a new power system with new energy as the main component is the main direction and key way for the transformation and upgrading of the power industry. Research into fast and accurate evaluation of transient power angle stability in the context of new power systems is of great significance. To address this, a new transient power angle stability evaluation method is proposed for power systems with grid-forming new energy based on the physics-informed sequence-to-sequence (PI-seq2seq) neural networks and cascaded convolutional neural networks models. First, the PI-seq2seq network structure is used to predict the future power angle trajectory, and a loss function with physical loss terms is constructed to guide the model training process, which avoids the long-duration time-domain simulation to ensure fast transient evaluation. Then, predicted power angle trajectory is taken as input by the cascade convolutional neural networks to evaluate the transient stability and its confidence level. A threshold judgment mechanism for the evaluation confidence level is configured to realize the transient stability judgment of the non-fixed evaluation length, which overcomes the impact of the fixed power angle curve length on the evaluation results. Finally, the method proposed is verified in the Kundur system, and the simulation results show that it has obtained satisfactory results in both the power angle curve prediction and the stability evaluation.

Utilizing the complex network theory to mitigate vulnerabilities mitigation in integrated energy systems is significant for enhancing the resilience of sustained energy supply, especially against deliberate physical attacks and natural disasters. To implement more precise preventive measures for vulnerable components in integrated energy systems, this paper proposes a weighted betweenness-based protection optimization model for safeguarding vulnerable segments. The model aims to minimize the weighted betweenness loss incurred post attacks and damages, while simultaneously considering strategies such as establishing backup nodes and backup lines, enhancing physical protection of nodes and lines, and adding new lines. These strategies are subject to constraints such as protection requirements, budget limitations, and constraints on the types and quantities of new lines. The model optimization provides the optimal protection strategies within the allocated budget. To address the complex betweenness computations and non-linear objective functions, the model is formulated as a bilevel structure based on the nature of protection measures first. Then, the lower-level model is solved using a local linearization technique, and a “genetic-mixed integer linear programming” algorithm is proposed for solving the model with high precision and efficiency. The simulation results demonstrate that under conditions of equivalent attack and damage, the system with the optimal protection strategy achieves reduction of 45.37% in weighted betweenness loss compared with that without protection. The optimal strategy outperforms the other five protection strategies considered within the allocated protection budget.

Accurate and efficient demand forecasting of customer energy services is crucial for quality and risk management in grid customer service. Therefore, this paper proposes a user energy service demand prediction model based on feature selection. The methodology includes introducing a sampling algorithm to solve the class imbalance problem in the data on the basis of analysing the user energy service data, reducing the dimensionality of the data based on an autoencoder to ensure efficient clustering of the K-mean algorithm, constructing a feature selection algorithm based on a lightweight gradient lifting machine to filter the effective features and improve the training efficiency of the prediction model, and establishing a bidirectional long- and short-term memory neural network multi-label predicting model based on an attentional mechanism to refine the user’s energy service demand. Through the analysis of 720 000 work order data from Guangdong Power Grid over three years, showing that the model proposed can effectively improve the prediction accuracy and speed.

In order to solve the contradiction between lightweight and high reliability in the electric aircraft electric propulsion system, a reliability allocation method considering lightweight constraint is proposed for the first time. An analytical expression between weight and reliability is integrated into the electric propulsion system design model as constraints, and a flexible “weight-reliability” optimization method is proposed, considering the uncertainty of the operating conditions of electric aircraft. This method achieves minimum weight under reliability constraints and maximum reliability under weight constraints. Taking the “Spirit of Innovation” electric aircraft as an example, the results show that the proposed method can reduce the total weight of the power system of the electric aircraft by 3.5% while ensuring the reliability of the system. The applicability of the proposed method is verified in different scenarios designed, providing technical support for the development of high power-to-weight ratio in electric aircrafts.

To address the challenges of complex detection background and poor detection performance for small targets, a transmission line channel security detection algorithm based on the fusion of window self-attention network and the YOLOv5 model is proposed. First, the Swin Transformer (S-T) is employed to optimize the backbone network, expanding the perception field of the model and enhancing its ability to extract effective information. Then, the adaptive spatial feature fusion (ASFF) module is improved to enhance the feature fusion ability of the model. Finally, considering the mismatch between the real frame and the predicted frame, the structural similarity intersection over union (SIoU) is introduced to optimize the boundary errors and improve the generalization ability of the model. The experimental results show that the model proposed achieves a multi-target intrusion detection accuracy of 90.2%, and with significant improvements in the detection of small targets. This approach better meets the requirements of foreign object detection in transmission line channels compared to other object detection algorithms.

Diversified frequency regulation resources are an effective and inevitable approach for addressing frequency safety issues in new power system. Based on a differentiated frequency regulation strategy that coordinates wind power, photovoltaic (PV), thermal power, and energy storage, this paper proposes a source-side battery energy storage system (BESS) optimization method under multiple scenarios by coupling long-term planning with short-term unit commitment. Joint frequency regulation strategies for thermal-storage, wind-storage, and PV-storage systems are developed, refining various functional roles of supporting battery storage to enhance flexibility during frequency regulation. The optimization configuration model aims to minimize both the investment and operational costs of wind-solar-thermal-storage systems. Frequency response capacity available from the power system is set as a security constraint, and high-order multi-machine time-domain simulations are used to verify and iterate frequency security margins in the solution process. The proposed method is validated using an improved IEEE 24-bus system. The results show that battery energy storage can flexibly switch between smoothing fluctuations, reducing renewable energy curtailment, and participating in system frequency regulation.

The increasing penetration of the renewable energy has increased the risks of sub/super synchronous oscillations in power systems. Therefore, it is critical to accurately evaluate the oscillatory stability of renewable power systems ensuring the safe and stable operation of the systems. In this paper, a method for evaluating the oscillatory stability of renewable power systems based on frequency-domain modal analysis is investigated. First, the frequency-domain impedance or admittance models of key equipment and stations are established, including the renewable power generators and stations, transmission lines, synchronous generators, transformers, etc. Next, a system-level frequency-domain network model is constructed based on the actual system topology. Then, the oscillatory stability of the renewable power system is evaluated by solving the zeros of the determinant of the loop impedance matrix or the node admittance matrix of the system. The weak points of the system are identified using the participated matrix of the weak oscillation mode, which provides reference for implementation of oscillation suppression measures. Taking the practical renewable power system in East China as an example, the oscillatory stability of the system considering the varying access capacity of renewables under different grid operating conditions is assessed using the frequency-domain modal analysis method. Finally, the time-domain simulation model of the actual renewable power system is built in PSCAD/EMTDC to verify the theoretical analysis.

The active power output capability and small signal stability in weak grids are key factors that limit stable photovoltaic (PV) power generation. To improve stably generating PV power in weak grids, an adaptive control method for PV inverters based on online tuning of the reactive power-voltage (Q-V) droop gain is proposed. First, to ensure active power output capability in weak grids, a “first optimization” method for the Q-V droop gain is proposed, considering voltage and current constraints. Then, to address stability constraints in weak grids, impedance modeling and stability analysis of the PV inverter system are conducted. A mapping relationship between the “parameter-weakest pole” is established with the weakest pole of the closed-loop system as a stability constraint based on the artificial neural network. A “second adjustment” method for the Q-V droop gain is developed at stably generating active power. Combined with the extended Kalman-filter-based grid impedance estimation, the proposed Q-V droop gain adaptive tuning method is realized. The effectiveness of the proposed adaptive control method is validated on the Modeling Tech real-time simulation platform.

To tackle the challenges of high difficulty and time-consuming micro-site optimization of wind farms in complex terrains, a multi-objective optimization method for micro-site selection is proposed, assisted by proxy model. First, considering the geographical features of complex terrains with significent undulations, the ruggedness index is calculated and the ground flatness is numerically quantified, constraining the points with excessive ruggedness. Then, a mathematical model for three-dimensional windy downward wake superposition calculation of power generation is established, a three-dimensional terrain collector line topology optimization agent model is constructed, and the prediction accuracy of the proxy model is verified, demonstrating the ability to replace numerous calculations in collector line topology optimization and effectively improving the computing efficiency. Finally, taking a real complex terrain wind farm in Xinjiang Uygur Autonomous Region, China as an example, multi-objective micro-site selection of complex terrain wind farm is realized, and the results are compared with those obtained through the single-objective optimization. The simulation results show that the multi-objective discrete state transfer algorithm assisted by the proxy model can reduce the total cable laying length, decrease the construction costs, and provide more feasible layout schemes while optimizing the annual power generation.

The constant power load (CPL) in a DC microgrid can reduce the effective damping of the system, resulting in high frequency voltage oscillations on the DC bus, which threatens the safe and stable operation of the system. To address this issue, this paper proposes a DC-bus voltage oscillation suppressor based on an active capacitor and its control method. The oscillation suppressor is connected in parallel to the DC bus, enabling direct interaction with the DC bus. The energy storage capacitor in the oscillator suppressor effectively stores the transient energy generated by voltage oscillations, thereby reducing the amplitude of voltage oscillation and improving the voltage stability of the bus. The voltage of the power supply in the oscillation suppressor adapts to the voltage of the DC bus, allowing for stable operation in the face of load changes in the system. The design offers advantages such as plug-and-play functionality, strong applicability, and flexible control. In addition, by analyzing the operating mode and mechanism of the oscillation suppressor, a small signal model is established, and the influence of controller parameters on the stability and dynamic performance of the suppressor is analyzed, based on which the controller parameter optimization scheme is proposed. Finally, the effectiveness of the oscillatory suppressor is validated through the experimental results.

The net system is an important component of the deep-sea net cages. As the bone structure of the net system, the rope structure bears the main hydrodynamic load of the net system, making the monitoring its health status essential. Aiming at the online damage monitoring of the rope structure, a finite element numerical model of the rope structure is developed, and the mechanical properties of the rope structure under intact and damaged conditions are compared and analyzed. The results show that when the rope is damaged, the end tension load of the damaged rope decreases sharply, while the end tension load of the adjacent rope increases. Based on the characteristics of these sudden tension changes and the influence of the damaged rope on the load of undamaged rope, three damage identification parameters are defined as the tension correlation coefficient, the total influence value, and the tension variation coefficient of the rope structure. Additionly, an online damage monitoring method based on rope tension signal is proposed. This method identifies the damaged rope by detecting an extremum step in the tension variation coefficient, and determines the specific damaged position by analyzing the proportion relationship between the tension change coefficient and the distance from the end to the damaged position. The research provides a reliable method for online monitoring of rope structure damage for the healthy operation and maintenance of deep-sea net cages.

In order to reduce the power loss of the transmission equipment, enhance the transmission efficiency of the propulsion shafting, and improve the vibration performance of the shafting, a multi-objective optimization design of a ship shafting experimental platform is performed based on the response surface model and genetic algorithm. The central composite design (CCD) method is used to select appropriate experimental points in the optimized design space, and the response surface model is developed with minimum total power consumption and vibration response amplitude. Based on the genetic algorithm, the Pareto optimal solution of response surface model regression function is solved through MATLAB software. The optimal design scheme is obtained by comparing and analyzing several groups of optimization results. The results show that the combined method can reduce the power loss of shafting by approximate 7.10% and reduce the vibration amplitude of shafting by 2.30%, while significantly improving the shafting transmission efficiency and effectively suppressing the vibration problem of propulsion shafting. The fiudings validate the feasibility of the multi-objective optimization method for the ship propulsion shafting.

In engineering practice, wind turbine fault diagnosis encounters situations where the fault category in the training data is different from the actual one. To diagnose unknown wind turbine faults, it is necessary to transfer the fault feature information learned during training to the unknown fault category. Unlike traditional methods that directly establish mapping between fault samples and fault categories, a zero-shot learning (ZSL) method for wind turbine fault diagnosis based on fault attributes is proposed to enable fault feature migration. A fault attribute matrix is established by describing the attributes of each fault, which is embedded into the fault sample space and fault category space. Then, a fault attribute learner is developed based on convolutional neural network (CNN), and a fault classifier is established based on Euclidean distance, forming the diagnosis process where fault attributes are predicted from fault samples and then classified. Finally, the effectiveness and superiority of the proposed fault diagnosis method are validated by comparing it with other zero-shot learning methods.

Large-scale new energy bases in desert, Gobi, and arid regions are key components of new-type power systems in China. Considering factors such as construction cost and carbon emissions, the capacities of thermal power and energy storage in these bases are limited, resulting in constrained flexibility. Consequently, the scheduling and operation of these large bases face significant challenges. This paper proposes a coordinated day-ahead and real-time scheduling method for wind-thermal-storage integrated bases. In the day-ahead stage, the startup/shutdown plans and adjustable output ranges of thermal units are determined based on a rough prediction of wind power. Then, it constructs a wind power accommodation interval based on the adjustable range of thermal power output and the operational constraints of energy storage. In the real-time stage, dispatch strategies are generated using a quantile-based rule according to current wind and solar power output, eliminating the need for high-precision forecasts. It is further demonstrated that the dispatch strategies generated by the quantile rule inherently satisfy system operational constraints. The case study validates the effectiveness of the proposed method for wind-thermal-storage systems. The results demonstrate that the proposed method, which does not rely on point prediction, outperforms rolling optimization methods when the three-step prediction error exceeds 10%. Moreover, the performance of operational scheduling can be improved by enhancing the accuracy of day-ahead or intraday short-term forecasts. The proposed method provides valuable reference for the operation of large-scale new energy bases.

In order to effectively simulate irregular wave groups that better represent the characteristics of real waves, a numerical wave tank based on a semi-mixed Eulerian-Lagrangian algorithm combined with boundary element method was developed in conjunction with a theoretical generation method for irregular wave groups. First, the impact of model parameters on the numerical solution was analyzed. The results showed that the accuracy of wave simulation improved with an increase in damping layer length or a decrease in the time step. Additionly, selecting appropriate deviation distance, distribution range, and the number of source points can balance computation accuracy and stability. Then, unidirectional irregular wave groups were simulated based on the verified model parameters. The numerical results were compared with the physical test data and theoretical values to validate the performance of the numerical tank in simulating irregular wave groups. The findings indicated that the developed numerical wave tank can effectively simulate the generation and propagation of irregular wave groups.

To address the issues of reduced adaptability of secondary frequency regulation caused by changes in power system parameters, a self-adaptive secondary frequency regulation strategy based on distributed model predictive control (DMPC) is proposed. First, a model of a multi-area interconnected power system is built. Based on the frequency response trajectory, a parameter identification model for each area of the system is established. Then, the recursive least square method is used to solve the parameter identification model and update the parameters of each area online. Additionally, with the objective to minimize the area control error (ACE), DMPC is adopted to optimize the power of generators for secondary frequency regulation. Finally, a case study is conducted to demonstrate the effectiveness of the proposed strategy.

A dual-transformer series resonant converter (DTSRC) is proposed to solve the problem of excessive return power and loss of zero voltage switching (ZVS) operation under light load in the dual bridge resonant converter (DBRC). A dual-transformer structure is employed on the proposed converter, which can significantly reduce voltage stress across the transformers. Additionally, the ZVS operation under light-load conditions is achieved and switching losses are minimized by optimizing the turns ratio coefficient. To eliminate backflow power and reduce conduction loss, a minimum current trajectory (MCT) control strategy is implemented for the DTSRC. Thus, synchronous rectification is realized on the secondary side, which makes the converter always operate with minimum current. Finally, a 200 W/100 kHz prototype is built to verify the effectiveness of the proposed solution. The experimental results demonstrate that the ZVS of all active switches is achieved during full power ranges, and the overall operating efficiency of DTSRC exceeds 90%.

In the context of large-scale energy storage stations, such as pumped storage, participating in both spot trading and grid scheduling, it is difficult for the grid to directly access the consumption of renewable energy in the spot market. In this regard, the influence of spot electricity trading on the pumped storage scheduling is considered and a multi-agent scheduling method with an embedded market game model is proposed. First, combined with the power spot market clearing model, with the objective of maximizing the benefits of the pumped storage power station in the spot market, a strategy for the pumped storage power station to participate in the spot trading of electric energy is developed. Then, combined with the two-part electricity price policy, the capacity allocation and power dispatching strategy of the grid operator about the pumped storage is formulated to minimize grid operating costs and the amount of renewable energy discarded in the entire grid. To formulate the proposed scheduling strategy, a bi-level optimization problem with an embedded game model is solved: the decision-making problem of the pumped storage power station participating in the electric energy spot market, and the optimization with the embedded marketing game model of capacity allocation and power scheduling strategy about pumped storage. The decision-making problem of pumped storage in the spot market follows a Stackelberg game model, which is integrated into the optimization problem of pumped storage capacity allocation and power scheduling strategy via the strong dual theory. The embedded bi-objective problem is solved by using the NSGA-II algorithm. Finally, based on the data from a pumped storage power station in East China, a simulation model is built to verify the effectiveness of the proposed method. The test results show that the proposed method can effectively coordinate the decision-making of direct grid dispatching and pumped storage participation in the electric energy spot market, enhancing the economic benefits of pumped storage, reducing the operating cost of the grid, and improving the consumption of renewable energy.

In order to address the power demand and voltage control challenges of a multi-energy network of electricity, heat, and gas coupled within an integrated energy system is an urban park, a distributed coordination methodology that incorporates voltage deviation control is proposed. First, operation models for local equipment and power flow coupling optimization in the electricity-gas-heat network are established. Then, a multi-objective day-ahead dispatch model and a local dispatch model for each agent are proposed, aiming to minimize both the overall operating cost and the mean voltage deviation. To achieve this, an asynchronous coordination approach based on preference prior expressions and the alternating direction method of multipliers (ADMM) is employed to enable distributed scheduling. The integrated energy system composed of a 14-node power grid, a 14-node heating network, and a 15-node gas network is taken as a simulation example, and the accuracy and practicability of the multi-objective programming method proposed are verified by comparing it with the existing multi-objective solutions and analyzing the Pareto front coordinates. Additionly, under the same calculation load distribution, the computational efficiency of the asynchronous coordination method is 16.6% higher than that of the synchronous method, which verifies the effectiveness of the proposed algorithm.

With the construction of new power systems, the stochasticity of high-proportion renewable energy significantly increases the uncertainty in the operation of the power grid, posing severe challenges to its safe, stable, and economically efficient operation. Data-driven artificial intelligence methods, such as deep reinforcement learning, are becoming increasingly important for regulating and assisting decision-making in the power grid in the new power system. However, current online scheduling algorithms based on deep reinforcement learning still face challenges in modeling the high-dimensional decision space and optimizing scheduling strategies, resulting in low model search efficiency and slow convergence. Therefore, a novel online steady-state scheduling method is proposed for the new power system based on hierarchical reinforcement learning, which reduces the decision space by adaptively selecting key nodes for adjustment. In addition, a state context-aware module based on gated recurrent units is introduced to model the high-dimensional environmental state, and a model with the optimization objectives of comprehensive operating costs, energy consumption, and over-limit conditions is constructed considering various operational constraints. The effectiveness of the proposed algorithm is thoroughly validated through experiments on three standard test cases, including IEEE-118, L2RPN-WCCI-2022, and SG-126.

The inertia and frequency regulation resources in power systems with a high proportion of renewable energy are scarce, resulting in prominent problems of frequency stability. To address these problems, this paper incorporates the potential frequency support capability of wind farms into the frequency control measures of the power grid, and proposes an optimization method for wind farm control parameters that considers the security requirements of system inertia. After a credible disturbance, the system inertia security requirement that meets the frequency stability constraint is calculated based on the transient frequency index limit. Then, the primary frequency regulation capability that wind farms can provide under different wind conditions is modeled with the goal of minimizing the adjustment of wind farm virtual inertia and frequency droop parameters under disturbances, and a dynamic optimization model for wind farm frequency control parameters is established. Finally, the frequency control parameters are calculated and numerical tests are conducted on the modified IEEE RTS-79 system. The results show that the proposed parameter optimization method effectively improves the transient frequency response process of wind farms, which helps enhance the frequency stability margin of the system.

To elucidate the micro-scale heat transfer mechanisms during the liquid-vapor phase change process in the wick of the high-temperature alkali metal heat pipes, this paper investigates the micro-scale heat transfer characteristics in the evaporating meniscus region for different alkali metals including potassium, sodium, and lithium by using the contact line heat transfer model. The distributions of liquid film thickness, contact angle, interface temperature, and heat flux at the evaporating meniscus region for different alkali metals are obtained under the same saturation vapor pressure and wall superheat. The results show that due to the high thermal conductivity of alkali metals, the contact line heat transfer characteristics of potassium, sodium, and lithium are significantly different from those of water. For alkali metals, the heat transfer in the micro region near the three-phase contact line is dominated by the thermal resistance at the vapor-liquid interface. Among these alkali metals, lithium has the highest micro-scale heat transfer performances. The thickness of the non-evaporating liquid film, the apparent contact angle and the pressure gradient of the liquid film are self-tuned according to the wall superheat, and a higher superheat results in a thinner non-evaporating liquid film, a larger apparent contact angle, and a larger pressure gradient. The adsorbed film region, where the non-evaporating liquid film is adsorbed on the wall, is dominated by the disjoining pressure. In the thin-film region, both disjoining pressure and capillary pressure contribute to the total pressure difference, which drives the liquid from the intrinsic meniscus region. The curvature of the vapor-liquid interface remains constant, and the capillary pressure dominates in the intrinsic meniscus region.

To address the problems in the profit model and transaction pricing of distributed energy storage providing multiple customized power services for sensitive customers, a multi-grade pricing strategy for distributed energy storage to provide various customized power services is proposed, including reactive power compensation, voltage sag control, and harmonic control. First, based on the four-quadrant operation characteristics of energy storage converter, a multi-grade evaluation indicator system of customized power services is established considering the differentiated user demands for power quality. Then, a cost-to-capacity model is developed for energy storage to provide customized power services in different power quality standards. Next, by taking economic loss of power quality into account, a user customized power utility function is established with individual rational constraint. Afterwards, considering power quality default risk and investment cost constraints, a customized power revenue model of distributed energy storage is constructed. Furthermore, a multi-grade trading framework for distributed energy storage to provide differentiated customized power services and its multi-grade pricing optimization strategy are proposed. Finally, in order to obtain the optimal additional tariff and user purchase package for premium power, the nonlinear multi-grade pricing model is transformed into a mixed integer linear programming model for optimization by using the big M method and transforming the user utility function into a constraint. The comparative analysis of the algorithm demonstrates that the proposed strategy can reduce the annual cost of customized power services for users while simultaneously enhancing the energy storage revenue.

Conventional elastic solutions cannot explain the deformation characteristics of soft rock tunnels exhibiting creep behavior. Therefore, a Burgers model is introduced to demonstrate the space-time effects in the construction period of soft rock tunnel. The influences of different factors on the longitudinal deformation behavior of viscoelastic rock as well as the spatiotemporal evolution patterns have been revealed through numerical simulations. An empirical formula for the displacement release coefficient is derived using the response surface method. The feasibility of the proposed method is validated by comparing the results with on-site monitoring data and other theoretical values. The findings indicate that geostress has a negligible effect on the displacement release coefficient. In constrast, the Kelvin shear modulus, viscosity coefficient, Maxwell shear modulus, and excavation speed significantly affect the displacement release coefficient. According to viscosity time, the hysteresis coefficient can be divided into three categories: low, medium, and high. Empirical formulas are fitted for the longitudinal deformation curve, including time, spatial, and spatiotemporal factors. The research conclusion can provide a more convenient method for predicting the deformation of soft rock tunnels.