Transient stability assessment(TSA)based on artificial intelligence typically has two distinct model management approaches:a unified management approach for all faulted lines and a separate management approach for eac...Transient stability assessment(TSA)based on artificial intelligence typically has two distinct model management approaches:a unified management approach for all faulted lines and a separate management approach for each faulted line.To address the shortcomings of the aforementioned approaches,namely accuracy,training time,and model management complexity,a multi-model management approach for power system TSA based on multi-moment feature clustering has been proposed.First,the steady-state and transient features present under fault conditions were obtained through a transient simulation of line faults.The input sample set was then constructed using the aforementioned multi-moment electrical features and the embedded faulty line numbers.Subsequently,K-means clustering was conducted on each line based on the similarity of their electrical features,employing t-SNE dimensionality reduction.The PSO-CNN model was trained separately for each cluster to generate several independent TSA models.Finally,a model effectiveness evaluation system consisting of five metrics was established,and the effect of the sample imbalance ratio on the model effectiveness was investigated.The model effectiveness was evaluated using the IEEE 39-bus system algorithm.The results showed that the multi-model management strategy based on multi-moment feature clustering can effectively combine the two advantages of superior evaluation performance and streamlined model management by fully extracting system features.Moreover,this approach allows for more flexible adjustments to line topology changes.展开更多
Ice load on underwater vehicles breaking through ice covers from underneath is a significant concern for researchers in polar exploration,and the research on this problem is still in its early stages.Both mechanical e...Ice load on underwater vehicles breaking through ice covers from underneath is a significant concern for researchers in polar exploration,and the research on this problem is still in its early stages.Both mechanical experimental measurement and numerical simulation pose research challenges.This study focuses on the ice load of a cylinder structure breaking upward through the ice sheet form underneath in the Small Ice Model Basin of China Ship Scientific Research Center(CSSRC SIMB).A high-speed camera system was employed to observe the ice sheet failure during the tests,in which,with the loading position as center,local radial cracks and circumferential cracks were generated.A load sensor was used to measure the overall ice load during this process.Meanwhile,a numerical model was developed using LS-DYNA for validation and comparison.With this model,numerical simulation was conducted under various ice thicknesses and upgoing speeds to analyze the instantaneous curves of ice load.The calculation results were statistically analyzed under different working conditions to determine the influence of the factors on the ice load of the cylinder.The study explores the measurement method about ice load of objects vertically breaking through model ice sheet and is expected to provide some fundamental insights into the safety design of underwater structures operating in ice waters.展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a...Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a novel integrated data-driven framework that combines particle filtering and model predictive health(PF-MPH)model for the predictive health manage-ment of power transformers.Initially,the particle filter probabilistically estimates power transformers'remaining life(R_(L))using direct winding hotspot temperature(χ_(H))measurements.The obtained R_(L)will then be used to calculate the degree of poly-merisation(DP)level and assess the current insulation condition.After that,a comparative analysis between direct and model-basedχ_(H)measurement methods is performed to highlight the superior accuracy of direct measurements for predictive health management.Then,the MPH optimisation algorithm,which uses the R_(L)and DP forecasts from the PF method,derives an optimal trajectory over the transformer's R_(L)that balances the costs of increased loading against the benefits gained from prolonged insulation longevity.The findings show that the proposed PF-MPH model has successfully reduced the χ_(H)by 2.46%over the predicted 19 years.This approach is expected to enable grid operators to optimise transformer loading schedules to extend the R_(L)of these critical assets in a cost-effective manner.展开更多
This study is devoted to a novel fractional friction-damage model for quasi-brittle rock materials subjected to cyclic loadings in the framework of micromechanics.The total damage of material describing the microstruc...This study is devoted to a novel fractional friction-damage model for quasi-brittle rock materials subjected to cyclic loadings in the framework of micromechanics.The total damage of material describing the microstructural degradation is decomposed into two parts:an instantaneous part arising from monotonic loading and a fatigue-related one induced by cyclic loading,relating to the initiation and propagation of microcracks.The inelastic deformation arises directly from frictional sliding along microcracks,inherently coupled with the damage effect.A fractional plastic flow rule is introduced using stress-fractional plasticity operations and covariant transformation approach,instead of classical plastic flow function.Additionally,the progression of fatigue damage is intricately tied to subcracks and can be calculated through application of a convolution law.The number of loading cycles serves as an integration variable,establishing a connection between inelastic deformation and the evolution of fatigue damage.In order to verify the accuracy of the proposed model,comparison between analytical solutions and experimental data are carried out on three different rocks subjected to conventional triaxial compression and cyclic loading tests.The evolution of damage variables is also investigated along with the cumulative deformation and fatigue lifetime.The improvement of the fractional model is finally discussed by comparing with an existing associated fatigue model in literature.展开更多
In order to solve the problems of low overload power in MEMS cantilever beams and low sensitivity in traditional MEMS fixed beams,a novel MEMS microwave power detection chip based on the dual-guided fixed beam is desi...In order to solve the problems of low overload power in MEMS cantilever beams and low sensitivity in traditional MEMS fixed beams,a novel MEMS microwave power detection chip based on the dual-guided fixed beam is designed.A gap between guiding beams and measuring electrodes is designed to accelerate the release of the sacrificial layer,effectively enhanc-ing chip performance.A load sensing model for the MEMS fixed beam microwave power detection chip is proposed,and the mechanical characteristics are analyzed based on the uniform load applied.The overload power and sensitivity are investi-gated using the load sensing model,and experimental results are compared with theoretical results.The detection chip exhibits excellent microwave characteristic in the 9-11 GHz frequency band,with a return loss less than-10 dB.At a signal fre-quency of 10 GHz,the theoretical sensitivity is 13.8 fF/W,closely matching the measured value of 14.3 fF/W,with a relative error of only 3.5%.These results demonstrate that the proposed load sensing model provides significant theoretical support for the design and performance optimization of MEMS microwave power detection chips.展开更多
Salt cavern energy storage technology contributes to energy reserves and renewable energy scale-up.This study focuses on salt cavern gas storage in Jintan to assess the long-term stability of its surrounding rock unde...Salt cavern energy storage technology contributes to energy reserves and renewable energy scale-up.This study focuses on salt cavern gas storage in Jintan to assess the long-term stability of its surrounding rock under frequent operation.The fatigue test results indicate that stress holding significantly reduces fatigue life,with the magnitude of stress level outweighing the duration of holding time in determining peak strain.Employing a machine learning approach,the impact of various factors on fatigue life and peak strain was quantified,revealing that higher stress limits and stress holding adversely impact the fatigue index,whereas lower stress limits and rate exhibit a positive effect.A novel fatigue-creep composite damage constitutive model is constructed,which is able to consider stress magnitude,rate,and stress holding.The model,validated through multi-path tests,accurately captures the elasto-viscous behavior of salt rock during loading,unloading,and stress holding.Sensitivity analysis further reveals the time-and stress-dependent behavior of model parameters,clarifying that strain changes stem not only from stress variations but are also influenced by alterations in elasto-viscous parameters.This study provides a new method for the mechanical assessment of salt cavern gas storage surrounding rocks.展开更多
Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a ...Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a two-area power system.Methods Two areas were connected through an AC tie line in parallel with a DC link to stabilize the frequency of oscillations in both areas.The PI parameters were tuned using the cuckoo search algorithm(CSA)to minimize the integral absolute error(IAE).A state matrix was provided,and the stability of the system was verified by calculating the eigenvalues.The frequency response was investigated for load variation,changes in the generator rate constraint,the turbine time constant,and the governor time constant.Results The CSA was compared with particle swarm optimization algorithm(PSO)under identical conditions.The system was modeled based on a state-space mathematical representation and simulated using MATLAB.The results demonstrated the effectiveness of the proposed controller based on both algorithms and,it is clear that CSA is superior to PSO.Conclusion The CSA algorithm smoothens the system response,reduces ripples,decreases overshooting and settling time,and improves the overall system performance under different disturbances.展开更多
Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environment...Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.展开更多
In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle ...In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle position,and actual power,lagged features were generated to capture temporal dependencies.Among 24 evaluated models,the ensemble bagging approach achieved the best performance,with R^(2) values of 0.89 at 0 min and 0.75 at 60 min.Shapley Additive exPlanations(SHAP)analysis revealed that while wind speed is the primary driver for short-term predictions,air temperature and nacelle position become more influential at longer forecasting horizons.These findings underscore the reliability of short-term predictions and the potential benefits of integrating hybrid AI and probabilistic models for extended forecasts.Our work contributes a robust and explainable framework to support Sri Lanka’s renewable energy transition,and future research will focus on real-time deployment and uncertainty quantification.展开更多
Stratospheric airships are lighter-than-air vehicles capable of continuous flying for months.The energy balance of the airship is the key to long-duration flights.The stratospheric airship is entirely powered by the s...Stratospheric airships are lighter-than-air vehicles capable of continuous flying for months.The energy balance of the airship is the key to long-duration flights.The stratospheric airship is entirely powered by the solar array.It is necessary to accurately predict the output power of the array for any flight state.Because of the uneven solar radiation received by the solar array,the traditional model based on components has a slow simulation speed.In this study,a data-driven surrogate modeling approach for prediction the output power of the solar array is proposed.The surrogate model is trained using the samples obtained from the high-accuracy simulation model.By using the input parameter preprocessor,the accuracy of the surrogate model in predicting the output power of the solar array is improved to 98.65%.In addition,the predictive speed of the surrogate model is ten million times faster than the traditional simulation model.Finally,the surrogate model is used to predict the energy balance of stratospheric airships flying throughout the year under actual global wind fields.展开更多
This work reviews models and methods for determining the dynamic response of pavements to moving vehicle loads in the framework of continuum-based three dimensional models and linear theories.This review emphasizes th...This work reviews models and methods for determining the dynamic response of pavements to moving vehicle loads in the framework of continuum-based three dimensional models and linear theories.This review emphasizes the most representative models and methods of analysis in the existing literature and illustrates all of them by numerical examples.Thus,13 such examples are presented here in some detail.Both flexible and rigid(concrete)pavement models involving simple and elaborate cases with respect to geometry and material behavior are considered.Thus,homogeneous or layered half-spaces with isotropic or cross-anisotropic and elastic,viscoelastic or poroelastic properties are considered.The vehicles are modeled as simple point or distributed loads or discrete spring-mass-dashpot system moving with constant or variable velocity.The dynamic response of the above pavement-vehicle systems is obtained by analytical/numerical or purely numerical methods of solution.Analytical/numerical methods have mainly to do with Fourier transforms or complex Fourier series with respect to both space and time.Purely numerical methods involve the finite element method(FEM)and the boundary element method(BEM)working in time or frequency domain.Critical discussions on the advantages and disadvantages of the various pavement-vehicle models and their methods of analysis are provided and the effects of the main parameters on the pavement response are determined through parametric studies and presented in the examples.Finally,conclusions are provided and suggestions for future research are made.展开更多
This paper deals with reduction of losses in electric power distribution system through a dynamic reconfiguration case study of a grid in the city of Mostar,Bosnia and Herzegovina.The proposed solution is based on a n...This paper deals with reduction of losses in electric power distribution system through a dynamic reconfiguration case study of a grid in the city of Mostar,Bosnia and Herzegovina.The proposed solution is based on a nonlinear model predictive control algorithm which determines the optimal switching operations of the distribution system.The goal of the control algorithm is to find the optimal radial network topology which minimizes cumulative active power losses and maximizes voltages across the network while simultaneously satisfying all system constraints.The optimization results are validated through multiple simulations(using real power demand data collected for a few characteristic days during winter and summer)which demonstrate the efficiency and usefulness of the developed control algorithm in reducing the grid losses by up to 14%.展开更多
Captive model tests are one of the most common methods to calculate the maneuvering hydrodynamic coefficients and characteristics of surface and underwater vehicles.Considerable attention must be paid to selecting and...Captive model tests are one of the most common methods to calculate the maneuvering hydrodynamic coefficients and characteristics of surface and underwater vehicles.Considerable attention must be paid to selecting and designing the most suitable laboratory equipment for towing tanks.A computational fluid dynamics(CFD)-based method is implemented to determine the loads acting on the towing facility of the submarine model.A reversed topology is also used to ensure the appropriateness of the load cells in the developed method.In this study,the numerical simulations were evaluated using the experimental results of the SUBOFF benchmark submarine model of the Defence Advanced Research Projects Agency.The maximum and minimum loads acting on the 2.5-meter submarine model were measured by determining the body’s lightest and heaviest maneuvering test scenarios.In addition to having sufficient endurance against high loads,the precision in measuring the light load was also investigated.The horizontal planar motion mechanism(HPMM)facilities in the National Iranian Marine Laboratory were developed by locating the load cells inside the submarine model.The results were presented as a case study.A numerical-based method was developed to obtain the appropriate load measurement facilities.Load cells of HPMM test basins can be selected by following the two-way procedure presented in this study.展开更多
The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of...The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.展开更多
As the demand for more efficient and adaptable power distribution systems intensifies, especially in rural areas, innovative solutions like the Capacitor-Coupled Substation with a Controllable Network Transformer (CCS...As the demand for more efficient and adaptable power distribution systems intensifies, especially in rural areas, innovative solutions like the Capacitor-Coupled Substation with a Controllable Network Transformer (CCS-CNT) are becoming increasingly critical. Traditional power distribution networks, often limited by unidirectional flow capabilities and inflexibility, struggle to meet the complex demands of modern energy systems. The CCS-CNT system offers a transformative approach by enabling bidirectional power flow between high-voltage transmission lines and local distribution networks, a feature that is essential for integrating renewable energy sources and ensuring reliable electrification in underserved regions. This paper presents a detailed mathematical representation of power flow within the CCS-CNT system, emphasizing the control of both active and reactive power through the adjustment of voltage levels and phase angles. A control algorithm is developed to dynamically manage power flow, ensuring optimal performance by minimizing losses and maintaining voltage stability across the network. The proposed CCS-CNT system demonstrates significant potential in enhancing the efficiency and reliability of power distribution, making it particularly suited for rural electrification and other applications where traditional methods fall short. The findings underscore the system's capability to adapt to varying operational conditions, offering a robust solution for modern power distribution challenges.展开更多
This paper presents a new approach for deriving a power system aggregate load area model (ALAM). In this approach, an equivalent area load model is derived to represent the load characters for a particular area load o...This paper presents a new approach for deriving a power system aggregate load area model (ALAM). In this approach, an equivalent area load model is derived to represent the load characters for a particular area load of a power system network. The Particle Swarm Optimization (PSO) method is employed to identify the unknown parameters of the generalised system, ALAM, based on the system measurement directly using a one-step scheme. Simulation studies are carried out for an IEEE 14-Bus power system and an IEEE 57-Bus power system. Simulation results show that the ALAM can represent the area load characters accurately under different operational conditions and at different power system states.展开更多
The validity of electric power system simulation or prediction models depends on static load model. Measurement- based approach is the unique method to identify them adequately. The measured power depends on both load...The validity of electric power system simulation or prediction models depends on static load model. Measurement- based approach is the unique method to identify them adequately. The measured power depends on both load reaction to supply voltage alteration and random process of load alteration Basically, there is no any universal method that can single out the inherent static load model from experimental data. The paper offers a proprietary technique which is the particular solution of the task. The technique considers the selection of neighboring measurement pairs with the supply voltage altering significantly be-tween them, the exclusion of selected pairs by load power factor and subsequent selection of the inherent static load model presented as the polynomial load model. The usage of the technique to identify static load model at “Fenster” industrial enterprise （in Borisov city） is presented. The ideas considered in the paper can be used for future development of static load model identification methods with the data obtained during both active experiment and in other operating models of electric power systems.展开更多
Because of the randomness and uncertainty,integration of large-scale wind farms in a power system will exert significant influences on the distribution of power flow.This paper uses polynomial normal transformation me...Because of the randomness and uncertainty,integration of large-scale wind farms in a power system will exert significant influences on the distribution of power flow.This paper uses polynomial normal transformation method to deal with non-normal random variable correlation,and solves probabilistic load flow based on Kriging method.This method is a kind of smallest unbiased variance estimation method which estimates unknown information via employing a point within the confidence scope of weighted linear combination.Compared with traditional approaches which need a greater number of calculation times,long simulation time,and large memory space,Kriging method can rapidly estimate node state variables and branch current power distribution situation.As one of the generator nodes in the western Yunnan power grid,a certain wind farm is chosen for empirical analysis.Results are used to compare with those by Monte Carlo-based accurate solution,which proves the validity and veracity of the model in wind farm power modeling as output of the actual turbine through PSD-BPA.展开更多
基金supported by the Science and Technology Project of SGCC(5100-202199558A-0-5-ZN).
文摘Transient stability assessment(TSA)based on artificial intelligence typically has two distinct model management approaches:a unified management approach for all faulted lines and a separate management approach for each faulted line.To address the shortcomings of the aforementioned approaches,namely accuracy,training time,and model management complexity,a multi-model management approach for power system TSA based on multi-moment feature clustering has been proposed.First,the steady-state and transient features present under fault conditions were obtained through a transient simulation of line faults.The input sample set was then constructed using the aforementioned multi-moment electrical features and the embedded faulty line numbers.Subsequently,K-means clustering was conducted on each line based on the similarity of their electrical features,employing t-SNE dimensionality reduction.The PSO-CNN model was trained separately for each cluster to generate several independent TSA models.Finally,a model effectiveness evaluation system consisting of five metrics was established,and the effect of the sample imbalance ratio on the model effectiveness was investigated.The model effectiveness was evaluated using the IEEE 39-bus system algorithm.The results showed that the multi-model management strategy based on multi-moment feature clustering can effectively combine the two advantages of superior evaluation performance and streamlined model management by fully extracting system features.Moreover,this approach allows for more flexible adjustments to line topology changes.
文摘Ice load on underwater vehicles breaking through ice covers from underneath is a significant concern for researchers in polar exploration,and the research on this problem is still in its early stages.Both mechanical experimental measurement and numerical simulation pose research challenges.This study focuses on the ice load of a cylinder structure breaking upward through the ice sheet form underneath in the Small Ice Model Basin of China Ship Scientific Research Center(CSSRC SIMB).A high-speed camera system was employed to observe the ice sheet failure during the tests,in which,with the loading position as center,local radial cracks and circumferential cracks were generated.A load sensor was used to measure the overall ice load during this process.Meanwhile,a numerical model was developed using LS-DYNA for validation and comparison.With this model,numerical simulation was conducted under various ice thicknesses and upgoing speeds to analyze the instantaneous curves of ice load.The calculation results were statistically analyzed under different working conditions to determine the influence of the factors on the ice load of the cylinder.The study explores the measurement method about ice load of objects vertically breaking through model ice sheet and is expected to provide some fundamental insights into the safety design of underwater structures operating in ice waters.
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
基金supported by Shandong Provincial Natural Science Foundation(ZR2024ME229,ZR2024ZD29).
文摘Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a novel integrated data-driven framework that combines particle filtering and model predictive health(PF-MPH)model for the predictive health manage-ment of power transformers.Initially,the particle filter probabilistically estimates power transformers'remaining life(R_(L))using direct winding hotspot temperature(χ_(H))measurements.The obtained R_(L)will then be used to calculate the degree of poly-merisation(DP)level and assess the current insulation condition.After that,a comparative analysis between direct and model-basedχ_(H)measurement methods is performed to highlight the superior accuracy of direct measurements for predictive health management.Then,the MPH optimisation algorithm,which uses the R_(L)and DP forecasts from the PF method,derives an optimal trajectory over the transformer's R_(L)that balances the costs of increased loading against the benefits gained from prolonged insulation longevity.The findings show that the proposed PF-MPH model has successfully reduced the χ_(H)by 2.46%over the predicted 19 years.This approach is expected to enable grid operators to optimise transformer loading schedules to extend the R_(L)of these critical assets in a cost-effective manner.
基金Fundamental Research Funds for the Central Universities(Grant No.B230201059)for the support.
文摘This study is devoted to a novel fractional friction-damage model for quasi-brittle rock materials subjected to cyclic loadings in the framework of micromechanics.The total damage of material describing the microstructural degradation is decomposed into two parts:an instantaneous part arising from monotonic loading and a fatigue-related one induced by cyclic loading,relating to the initiation and propagation of microcracks.The inelastic deformation arises directly from frictional sliding along microcracks,inherently coupled with the damage effect.A fractional plastic flow rule is introduced using stress-fractional plasticity operations and covariant transformation approach,instead of classical plastic flow function.Additionally,the progression of fatigue damage is intricately tied to subcracks and can be calculated through application of a convolution law.The number of loading cycles serves as an integration variable,establishing a connection between inelastic deformation and the evolution of fatigue damage.In order to verify the accuracy of the proposed model,comparison between analytical solutions and experimental data are carried out on three different rocks subjected to conventional triaxial compression and cyclic loading tests.The evolution of damage variables is also investigated along with the cumulative deformation and fatigue lifetime.The improvement of the fractional model is finally discussed by comparing with an existing associated fatigue model in literature.
基金supported by the National Natural Science Foundation of China(61904089)the Province Natural Science Foundation of Jiangsu(BK20190731).
文摘In order to solve the problems of low overload power in MEMS cantilever beams and low sensitivity in traditional MEMS fixed beams,a novel MEMS microwave power detection chip based on the dual-guided fixed beam is designed.A gap between guiding beams and measuring electrodes is designed to accelerate the release of the sacrificial layer,effectively enhanc-ing chip performance.A load sensing model for the MEMS fixed beam microwave power detection chip is proposed,and the mechanical characteristics are analyzed based on the uniform load applied.The overload power and sensitivity are investi-gated using the load sensing model,and experimental results are compared with theoretical results.The detection chip exhibits excellent microwave characteristic in the 9-11 GHz frequency band,with a return loss less than-10 dB.At a signal fre-quency of 10 GHz,the theoretical sensitivity is 13.8 fF/W,closely matching the measured value of 14.3 fF/W,with a relative error of only 3.5%.These results demonstrate that the proposed load sensing model provides significant theoretical support for the design and performance optimization of MEMS microwave power detection chips.
基金supported by the National Natural Science Foundation of China(Nos.52374078,U24A20616 and 52074043)the Sichuan-Chongqing Science and Technology Innovation Cooperation Program Project(No.2024TIAD-CYKJCXX0011)the Fundamental Research Funds for the Central Universities(No.2023CDJKYJH021)。
文摘Salt cavern energy storage technology contributes to energy reserves and renewable energy scale-up.This study focuses on salt cavern gas storage in Jintan to assess the long-term stability of its surrounding rock under frequent operation.The fatigue test results indicate that stress holding significantly reduces fatigue life,with the magnitude of stress level outweighing the duration of holding time in determining peak strain.Employing a machine learning approach,the impact of various factors on fatigue life and peak strain was quantified,revealing that higher stress limits and stress holding adversely impact the fatigue index,whereas lower stress limits and rate exhibit a positive effect.A novel fatigue-creep composite damage constitutive model is constructed,which is able to consider stress magnitude,rate,and stress holding.The model,validated through multi-path tests,accurately captures the elasto-viscous behavior of salt rock during loading,unloading,and stress holding.Sensitivity analysis further reveals the time-and stress-dependent behavior of model parameters,clarifying that strain changes stem not only from stress variations but are also influenced by alterations in elasto-viscous parameters.This study provides a new method for the mechanical assessment of salt cavern gas storage surrounding rocks.
基金Supported by the Russian Science Foundation(Agreement 23-41-10001,https://rscf.ru/project/23-41-10001/).
文摘Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a two-area power system.Methods Two areas were connected through an AC tie line in parallel with a DC link to stabilize the frequency of oscillations in both areas.The PI parameters were tuned using the cuckoo search algorithm(CSA)to minimize the integral absolute error(IAE).A state matrix was provided,and the stability of the system was verified by calculating the eigenvalues.The frequency response was investigated for load variation,changes in the generator rate constraint,the turbine time constant,and the governor time constant.Results The CSA was compared with particle swarm optimization algorithm(PSO)under identical conditions.The system was modeled based on a state-space mathematical representation and simulated using MATLAB.The results demonstrated the effectiveness of the proposed controller based on both algorithms and,it is clear that CSA is superior to PSO.Conclusion The CSA algorithm smoothens the system response,reduces ripples,decreases overshooting and settling time,and improves the overall system performance under different disturbances.
基金supported by the Startup Grant(PG18929)awarded to F.Shokoohi.
文摘Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.
文摘In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle position,and actual power,lagged features were generated to capture temporal dependencies.Among 24 evaluated models,the ensemble bagging approach achieved the best performance,with R^(2) values of 0.89 at 0 min and 0.75 at 60 min.Shapley Additive exPlanations(SHAP)analysis revealed that while wind speed is the primary driver for short-term predictions,air temperature and nacelle position become more influential at longer forecasting horizons.These findings underscore the reliability of short-term predictions and the potential benefits of integrating hybrid AI and probabilistic models for extended forecasts.Our work contributes a robust and explainable framework to support Sri Lanka’s renewable energy transition,and future research will focus on real-time deployment and uncertainty quantification.
基金supported by the National Natural Science Foundation of China(Nos.51775021,52302511)the Fundamental Research Funds for the Central Universities,China(Nos.YWF-23-JC-01,YWF-23-JC-04,YWF-23-JC-09)。
文摘Stratospheric airships are lighter-than-air vehicles capable of continuous flying for months.The energy balance of the airship is the key to long-duration flights.The stratospheric airship is entirely powered by the solar array.It is necessary to accurately predict the output power of the array for any flight state.Because of the uneven solar radiation received by the solar array,the traditional model based on components has a slow simulation speed.In this study,a data-driven surrogate modeling approach for prediction the output power of the solar array is proposed.The surrogate model is trained using the samples obtained from the high-accuracy simulation model.By using the input parameter preprocessor,the accuracy of the surrogate model in predicting the output power of the solar array is improved to 98.65%.In addition,the predictive speed of the surrogate model is ten million times faster than the traditional simulation model.Finally,the surrogate model is used to predict the energy balance of stratospheric airships flying throughout the year under actual global wind fields.
文摘This work reviews models and methods for determining the dynamic response of pavements to moving vehicle loads in the framework of continuum-based three dimensional models and linear theories.This review emphasizes the most representative models and methods of analysis in the existing literature and illustrates all of them by numerical examples.Thus,13 such examples are presented here in some detail.Both flexible and rigid(concrete)pavement models involving simple and elaborate cases with respect to geometry and material behavior are considered.Thus,homogeneous or layered half-spaces with isotropic or cross-anisotropic and elastic,viscoelastic or poroelastic properties are considered.The vehicles are modeled as simple point or distributed loads or discrete spring-mass-dashpot system moving with constant or variable velocity.The dynamic response of the above pavement-vehicle systems is obtained by analytical/numerical or purely numerical methods of solution.Analytical/numerical methods have mainly to do with Fourier transforms or complex Fourier series with respect to both space and time.Purely numerical methods involve the finite element method(FEM)and the boundary element method(BEM)working in time or frequency domain.Critical discussions on the advantages and disadvantages of the various pavement-vehicle models and their methods of analysis are provided and the effects of the main parameters on the pavement response are determined through parametric studies and presented in the examples.Finally,conclusions are provided and suggestions for future research are made.
基金supported in part by the European Regional Development Fund under Grant KK.01.1.1.01.0009(DATACROSS).
文摘This paper deals with reduction of losses in electric power distribution system through a dynamic reconfiguration case study of a grid in the city of Mostar,Bosnia and Herzegovina.The proposed solution is based on a nonlinear model predictive control algorithm which determines the optimal switching operations of the distribution system.The goal of the control algorithm is to find the optimal radial network topology which minimizes cumulative active power losses and maximizes voltages across the network while simultaneously satisfying all system constraints.The optimization results are validated through multiple simulations(using real power demand data collected for a few characteristic days during winter and summer)which demonstrate the efficiency and usefulness of the developed control algorithm in reducing the grid losses by up to 14%.
文摘Captive model tests are one of the most common methods to calculate the maneuvering hydrodynamic coefficients and characteristics of surface and underwater vehicles.Considerable attention must be paid to selecting and designing the most suitable laboratory equipment for towing tanks.A computational fluid dynamics(CFD)-based method is implemented to determine the loads acting on the towing facility of the submarine model.A reversed topology is also used to ensure the appropriateness of the load cells in the developed method.In this study,the numerical simulations were evaluated using the experimental results of the SUBOFF benchmark submarine model of the Defence Advanced Research Projects Agency.The maximum and minimum loads acting on the 2.5-meter submarine model were measured by determining the body’s lightest and heaviest maneuvering test scenarios.In addition to having sufficient endurance against high loads,the precision in measuring the light load was also investigated.The horizontal planar motion mechanism(HPMM)facilities in the National Iranian Marine Laboratory were developed by locating the load cells inside the submarine model.The results were presented as a case study.A numerical-based method was developed to obtain the appropriate load measurement facilities.Load cells of HPMM test basins can be selected by following the two-way procedure presented in this study.
基金funded by the National Key Research and Development Program of China(2024YFE0106800)Natural Science Foundation of Shandong Province(ZR2021ME199).
文摘The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.
基金supported by National Natural Science Foundation of China(61533013,61273144)Scientific Technology Research and Development Plan Project of Tangshan(13130298B)Scientific Technology Research and Development Plan Project of Hebei(z2014070)
文摘As the demand for more efficient and adaptable power distribution systems intensifies, especially in rural areas, innovative solutions like the Capacitor-Coupled Substation with a Controllable Network Transformer (CCS-CNT) are becoming increasingly critical. Traditional power distribution networks, often limited by unidirectional flow capabilities and inflexibility, struggle to meet the complex demands of modern energy systems. The CCS-CNT system offers a transformative approach by enabling bidirectional power flow between high-voltage transmission lines and local distribution networks, a feature that is essential for integrating renewable energy sources and ensuring reliable electrification in underserved regions. This paper presents a detailed mathematical representation of power flow within the CCS-CNT system, emphasizing the control of both active and reactive power through the adjustment of voltage levels and phase angles. A control algorithm is developed to dynamically manage power flow, ensuring optimal performance by minimizing losses and maintaining voltage stability across the network. The proposed CCS-CNT system demonstrates significant potential in enhancing the efficiency and reliability of power distribution, making it particularly suited for rural electrification and other applications where traditional methods fall short. The findings underscore the system's capability to adapt to varying operational conditions, offering a robust solution for modern power distribution challenges.
文摘This paper presents a new approach for deriving a power system aggregate load area model (ALAM). In this approach, an equivalent area load model is derived to represent the load characters for a particular area load of a power system network. The Particle Swarm Optimization (PSO) method is employed to identify the unknown parameters of the generalised system, ALAM, based on the system measurement directly using a one-step scheme. Simulation studies are carried out for an IEEE 14-Bus power system and an IEEE 57-Bus power system. Simulation results show that the ALAM can represent the area load characters accurately under different operational conditions and at different power system states.
文摘The validity of electric power system simulation or prediction models depends on static load model. Measurement- based approach is the unique method to identify them adequately. The measured power depends on both load reaction to supply voltage alteration and random process of load alteration Basically, there is no any universal method that can single out the inherent static load model from experimental data. The paper offers a proprietary technique which is the particular solution of the task. The technique considers the selection of neighboring measurement pairs with the supply voltage altering significantly be-tween them, the exclusion of selected pairs by load power factor and subsequent selection of the inherent static load model presented as the polynomial load model. The usage of the technique to identify static load model at “Fenster” industrial enterprise （in Borisov city） is presented. The ideas considered in the paper can be used for future development of static load model identification methods with the data obtained during both active experiment and in other operating models of electric power systems.
文摘Because of the randomness and uncertainty,integration of large-scale wind farms in a power system will exert significant influences on the distribution of power flow.This paper uses polynomial normal transformation method to deal with non-normal random variable correlation,and solves probabilistic load flow based on Kriging method.This method is a kind of smallest unbiased variance estimation method which estimates unknown information via employing a point within the confidence scope of weighted linear combination.Compared with traditional approaches which need a greater number of calculation times,long simulation time,and large memory space,Kriging method can rapidly estimate node state variables and branch current power distribution situation.As one of the generator nodes in the western Yunnan power grid,a certain wind farm is chosen for empirical analysis.Results are used to compare with those by Monte Carlo-based accurate solution,which proves the validity and veracity of the model in wind farm power modeling as output of the actual turbine through PSD-BPA.
