The green and low carbon transition and development of the electricity industry is the most crucial task in realizing the“dual-carbon target”,and it is urgent to explore the incentive and subsidy mechanism to promot...The green and low carbon transition and development of the electricity industry is the most crucial task in realizing the“dual-carbon target”,and it is urgent to explore the incentive and subsidy mechanism to promote green electricity consumption and the cost-sharing strategy of carbon reduction,to alleviate the pressure of carbon abatement cost of each subject of the electricity supply chain.Against this background,this paper takes into account the low-carbon subsidies provided by the government and the incentive subsidies for users,and studies the optimal decision-making of each subject in the electricity supply chain,so that each of them can obtain the optimal profit and achieve carbon emission reduction at the same time.Firstly,taking into account the direct power purchase mode of large users and the electricity-selling companies emerging after the reform of the power sales side,we have established a cooperative mechanism for sharing the cost of carbon emission reduction in the electricity supply chain and clarified the relationship between the supply and demand of electricity among the main parties.Subsequently,considering government low-carbon subsidies and user incentive subsidies,the optimal decisionmaking model is established under two scenarios of decentralized and centralized cooperative games in the supply chain,respectively,with the objective of maximizing profits and carbon reduction rates.Solving for the optimal proportion of carbon abatement costs shared by each participant in the electricity supply chain in achieving game equilibrium.Finally,we analyze the role of the government’s low-carbon subsidies,users’incentive subsidies,and other factors on the profit and carbon reduction effect of the electricity industry through the example analysis and further analyze the impact of carbon abatement cost-sharing measures to provide recommendations for the electricity industry to realize low-carbon abatement and make decisions.展开更多
Cascading faults have been identified as the primary cause of multiple power outages in recent years.With the emergence of integrated energy systems(IES),the conventional approach to analyzing power grid cascading fau...Cascading faults have been identified as the primary cause of multiple power outages in recent years.With the emergence of integrated energy systems(IES),the conventional approach to analyzing power grid cascading faults is no longer appropriate.A cascading fault analysis method considering multi-energy coupling characteristics is of vital importance.In this study,an innovative analysis method for cascading faults in integrated heat and electricity systems(IHES)is proposed.It considers the degradation characteristics of transmission and energy supply com-ponents in the system to address the impact of component aging on cascading faults.Firstly,degradation models for the current carrying capacity of transmission lines,the water carrying capacity and insulation performance of thermal pipelines,as well as the performance of energy supply equipment during aging,are developed.Secondly,a simulation process for cascading faults in the IHES is proposed.It utilizes an overload-dominated development model to predict the propagation path of cascading faults while also considering network islanding,electric-heating rescheduling,and load shedding.The propagation of cascading faults is reflected in the form of fault chains.Finally,the results of cascading faults under different aging levels are analyzed through numerical examples,thereby verifying the effectiveness and rationality of the proposed model and method.展开更多
According to green view and history data, the demonstrated method is applied to analyze the green rationality on the present structure of thermal, hydraulic and nuclear electric power resource; the green trend of larg...According to green view and history data, the demonstrated method is applied to analyze the green rationality on the present structure of thermal, hydraulic and nuclear electric power resource; the green trend of large-scale on thermal power generating unit in our history; the distribution of between water and coal resource with the space structure of electric power installed capacity, and recommending the premise, dominance and direction of building our green electric power structure in the future.展开更多
Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,th...Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.展开更多
In order to realize automatic tracking drift of resonance frequency of ultrasonic vibration system with high power and high quality factor Q, adaptive fuzzy control was studied with a self-fabricated ultrasonic plasti...In order to realize automatic tracking drift of resonance frequency of ultrasonic vibration system with high power and high quality factor Q, adaptive fuzzy control was studied with a self-fabricated ultrasonic plastic welding machine. At first, relations between amplitude of vibration and frequency as well as main loop current and amplitude of vibration were analyzed. From this analysis, we deduced that frequency tracking process of the vibration system can be concluded as an optimizing problem of one dimensional fluctuant extremum of main loop current in vibration system. Then a method of self-optimizing fuzzy control, used for the realization of automatic frequency tracking in vibration system, is presented on the basis of serf-optimizing adaptive control approach and fuzzy control approach. The result of experiments shows that the fuzzy self-optimizing method can solve the problem of tracking frequency drift very well. Response time of tracking in the system is less than 50 ms, which basically meets the requirements of frequency tracking in ultrasonic plastic welding machine.展开更多
An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady sta...An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady state index based on chaotic theory and least squares support vector machine (LSSVM) is proposed in this paper. At first, the phase space reconstruction of original power quality data is performed to form a new data space containing the attractor. The new data space is used as training samples for the LSSVM. Then in order to predict power quality steady state index accurately, the particle swarm algorithm is adopted to optimize parameters of the LSSVM model. According to the simulation results based on power quality data measured in a certain distribution network, the model applies to several indexes with higher forecasting accuracy and strong practicability.展开更多
In order to make an intensive study of the development of smart power distribution and utilization technology in China, their research hotspots and frontier technology are selected out through combining the informatic...In order to make an intensive study of the development of smart power distribution and utilization technology in China, their research hotspots and frontier technology are selected out through combining the informatics method, and using the CiteSpace which can take keyword cooccurrence analysis and draw the visualization graph. According to this result, we can infer the development trend of smart power distribution and utilization in the future, and providing reference for the researcher whose engage in this domain. The electric related literature was collected from the CNKI database in China. Under the smart power distribution and utilization domain, we also analyze the development of the power quality and the energy internet in detail.展开更多
Capacitive humidity sensors were made of nanometer barium titanate.The pellets were prepared under different pressures between 3920N to 7850N force.The capacitance changes in three orders of magnitude in the relative ...Capacitive humidity sensors were made of nanometer barium titanate.The pellets were prepared under different pressures between 3920N to 7850N force.The capacitance changes in three orders of magnitude in the relative humidity range of 10%～98%,indicating high humidity sensitivity of the sensors.At a certain measuring frequency,the capacitance of the sensors increases as increasing of the preparation pressure,while the sensitivity of the sensors basically remains the same.The frequencies corresponding to the peaks of the dielectric loss of the sensors move to the higher frequency direction as increasing of the relative humidity.At a certain humidity,the frequencies corresponding to the peaks of the dielectric loss move to the higher frequency direction as increasing of the preparation pressure.展开更多
Combined with actual situation of Fengxian power Supply Company, the neutral grounding modes of Fengxian 35 kV and 10 kV power grid are studied in the paper. The different frequencies injected method is used to measur...Combined with actual situation of Fengxian power Supply Company, the neutral grounding modes of Fengxian 35 kV and 10 kV power grid are studied in the paper. The different frequencies injected method is used to measure the capacitive current of Fengxian 28 substations, and the neutral grounding modes of the 28 substations are determined based on the measured values of capacitive current.展开更多
With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in th...With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.展开更多
Ultrathin high-k dielectric ErAlO films were deposited on Si(100)substrates by using radio-frequency magnetron sputtering.The very flat surface of the annealed film with a rms roughness less than 0.25 nm was observed ...Ultrathin high-k dielectric ErAlO films were deposited on Si(100)substrates by using radio-frequency magnetron sputtering.The very flat surface of the annealed film with a rms roughness less than 0.25 nm was observed by using an atomic force microscope.The film shows good thermal stability when annealing at 900℃ for 30 s in the O_(2) ambient.The effective dielectric constant of the film is around 15.2,and a low leakage current of 8.4×10^(-5) A/cm^(2) at an electric field of 1 MV/cm was achieved for the film with the equivalent oxide thickness of 2.0 nm after annealing.展开更多
The randomness and uncertainty of renewable energy generation are expected to significantly change the optimal decision-making of trans-provincial electricity market subjects.Therefore,it is beneficial to optimize the...The randomness and uncertainty of renewable energy generation are expected to significantly change the optimal decision-making of trans-provincial electricity market subjects.Therefore,it is beneficial to optimize the interests of each of these subjects,considering the unpredictable risks of renewable energy under the renewable portfolio standards(RPS)and researching their effects on the optimal decision-making of transprovincial electricity market multi-subjects.First,we develop a trans-provincial trading market mechanism for renewable energy and clarify the electricity supply and demand relation and the green certificates supply and demand relation of trans-provincial electricitymarketmulti-subjects.Then,under the RPS,we construct a multi-subject game model of the power supply chain that recognizes the risks,and adopt the reverse induction method to discuss the optimum risk-taking judgment of each subject in the trans-provincial electricity market.Finally,we useMATLAB to verify the viability and efficacy of the proposed gamemodel,and obtain a certain reference value for the optimal decision-making of trans-provincial electricity market subjects.In summary,we consider the uncertainty risks of renewable energy under RPS,study the effects of the green certificate price and risk aversion coefficient in the RPS mechanism on the optimal decisionmaking of trans-provincial electricity market subjects,and obtain the changing trends of two different power products and those of different electricity market subjects under the influence of the green certificate price and risk aversion coefficient,which have a certain reference value for studying the factors affecting the optimal decision-making of trans-provincial electricity market subjects.展开更多
Optical Character Recognition(OCR)refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image.This paper is a deep study on the recognition effe...Optical Character Recognition(OCR)refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image.This paper is a deep study on the recognition effect of OCR based on Artificial Intelligence(AI)algorithms,in which the different AI algorithms for OCR analysis are classified and reviewed.Firstly,the mechanisms and characteristics of artificial neural network-based OCR are summarized.Secondly,this paper explores machine learning-based OCR,and draws the conclusion that the algorithms available for this form of OCR are still in their infancy,with low generalization and fixed recognition errors,albeit with better recognition effect and higher recognition accuracy.Finally,this paper explores several of the latest algorithms such as deep learning and pattern recognition algorithms.This paper concludes that OCR requires algorithms with higher recognition accuracy.展开更多
Under the background of implementing renewable portfolio standards and the ever-improving tradable green certificate scheme,the increasingly environmentally-friendly preference of power users is leading to changes in ...Under the background of implementing renewable portfolio standards and the ever-improving tradable green certificate scheme,the increasingly environmentally-friendly preference of power users is leading to changes in electricity demand,which,in turn,is driving changes in the decision-making behaviors of various actors in the power supply chain.Based on this,with the goal of pursuing maximum profit,consumer-power-demand functions have been introduced with some consideration of the factors of consumer preference to establish an optimal profit model for each trading subject in non-cooperative states of the power supply chain,under the constraints of meeting renewable energy portfolio standards.Here,the optimal strategy of each trading subject is presented by adopting the reverse induction method.Furthermore,examples are used to analyze factors such as the influence of environmental protection preferences,quota ratios,price substitutions,and market demand as well as the optimal profit of each trading subject in view of providing a reference for the decision-making in the power supply chain trading subjects.展开更多
When using the ATP-EMTP software to simulate the electromagnetic transient of local line or nearby line for a large area power grid, it is necessary to divide the area grid into internal network and external network s...When using the ATP-EMTP software to simulate the electromagnetic transient of local line or nearby line for a large area power grid, it is necessary to divide the area grid into internal network and external network so as to make the external network simple for the restriction of the simulation calculation scale of software. Based on the existing equivalence method, in this paper we proposed a practical equivalent method using the short-circuit calculation program of PSASP. In this way, we completed the equivalence of northwest power grid operating at the large mode of summer 2016. By comparing the power flow and the characteristics of short-circuit in the system before and after the equivalence, this equivalent method is proved to be correct.展开更多
Electricity productivity is regarded as a major assessment indicator in the design of energy saving policies,given that China has entered a“New Normal”of economic development.In fact,enhancing electricity productivi...Electricity productivity is regarded as a major assessment indicator in the design of energy saving policies,given that China has entered a“New Normal”of economic development.In fact,enhancing electricity productivity in an all-round way,as is one of the binding indicators for energy and environmental issues,means that non-growth target of total electric energy consumption in the economic development is feasible.The Gini coefficient,Theil index,and Mean log deviation are utilized to measure regional differences in China’s electricity productivity from 1997 to 2016 in five regions,and conditionalβconvergence is empirically analyzed with the spatial Durbin model.The results show that:(1)China’s electricity productivity is improving,while the overall feature is that the eastern area has a higher efficiency than the western area.(2)The difference in electricity productivity is the smallest in the northeast and the largest in the northwest.Interregional difference plays an important role and is the main cause for the differences.(3)The electricity productivity in China exhibitsβconvergence,except for the northwest.The positive driving factor is urbanization level(0.0485%),and the negative driving factor is FDI(–0.0104%).Moreover,the urbanization rate(0.0669%),foreign direct investment(0.0960%),and the industrial structure(–0.0769%)have a spatial spillover effect on improving regional electricity productivity.Based on this conclusion,the study provides some recommendations for saving energy policy design in China’s power industry.展开更多
In order to ensure that the large-scale application of photovoltaic power generation does not affect the stability of the grid, accurate photovoltaic (PV) power generation forecast is essential. A short-term PV power ...In order to ensure that the large-scale application of photovoltaic power generation does not affect the stability of the grid, accurate photovoltaic (PV) power generation forecast is essential. A short-term PV power generation forecast method using the combination of K-means++, grey relational analysis (GRA) and support vector regression (SVR) based on feature selection (Hybrid Kmeans-GRA-SVR, HKGSVR) was proposed. The historical power data were clustered through the multi-index K-means++ algorithm and divided into ideal and non-ideal weather. The GRA algorithm was used to match the similar day and the nearest neighbor similar day of the prediction day. And selected appropriate input features for different weather types to train the SVR model. Under ideal weather, the average values of MAE, RMSE and R2 were 0.8101, 0.9608 kW and 99.66%, respectively. And this method reduced the average training time by 77.27% compared with the standard SVR model. Under non-ideal weather conditions, the average values of MAE, RMSE and R2 were 1.8337, 2.1379 kW and 98.47%, respectively. And this method reduced the average training time of the standard SVR model by 98.07%. The experimental results show that the prediction accuracy of the proposed model is significantly improved compared to the other five models, which verify the effectiveness of the method.展开更多
The Ramsey number is a foundational result in combinatorics. This article will introduce Ramsey number with the method of graph theory, and the Ramsey pricing theory is applied to the sales price and study of cross su...The Ramsey number is a foundational result in combinatorics. This article will introduce Ramsey number with the method of graph theory, and the Ramsey pricing theory is applied to the sales price and study of cross subsidy. Based on the status of our sales price and cross subsidy, Ramsey pricing methods theoretically guide adjustment thoughts of sales price and solve the practical problems in our life.展开更多
High-performance Cu/Graphene composite wire synergistically strengthened by nano Cr_(3)C_(2) phase was directly synthesized via hot press sintering followed by severe cold plastic deformation, using liquid paraffin an...High-performance Cu/Graphene composite wire synergistically strengthened by nano Cr_(3)C_(2) phase was directly synthesized via hot press sintering followed by severe cold plastic deformation, using liquid paraffin and CuCr alloy powder as the raw materials. Since graphene is in situ formed under the catalysis of copper powder during the sintering process, the problem that graphene is easy to agglomerate and difficult to disperse uniformly in the copper matrix has been solved. The nano Cr_(3)C_(2)-particles nailed at the interface favor to improve the interface bonding. The Cu/Graphene composite possesses high electrical conductivity, hardness, and plasticity. The composite wire exhibits high electrical conductivity of 96.93% IACS, great tensile strength of 488MPa, and excellent resistance to softening. Even after annealing at 400℃ for 1 h, the tensile strength can still reach 268 MPa with a conductivity of about 99.14% IACS.The wire's temperature coefficient of resistance(TCR) is largely reduced to 0.0035/℃ due to the complex structure,which leads the wire to present low resistivity at higher temperatures. Such Cu/Graphene composite wire with excellent comprehensive performance has a good application prospect in high-power density motors.展开更多
Time-series anomaly detection plays a crucial role in the operation of offshore wind turbines.Various wind turbine monitoring systems rely on time-series data to monitor and identify anomalies in real-time,as well as ...Time-series anomaly detection plays a crucial role in the operation of offshore wind turbines.Various wind turbine monitoring systems rely on time-series data to monitor and identify anomalies in real-time,as well as to initiate early warning processes.However,for offshore wind turbines with a high data density,conventional methods have high computational overhead in detecting anomalies while failing to accurately detect anomalies due to variations in data scales.To address this challenge,we propose an efficient anomaly detection method with contrastive learning,called Hawkeye.Hawkeye is based on residual clustering,an unsupervised anomaly detection method for multivariate time-series data.To ensure accurate anomaly detection,a trend-capturing prediction module is also combined with an automatic labeling module.As a result,the most common information can be learned from multivariate time-series data to reconstruct data trends.By evaluating Hawkeye on public datasets and real-world offshore wind turbine operational datasets,the results show that Hawkeye’s F_(1)-score improves by an average of 14% compared with Isolation Forest,and its size shrinks by up to 11.5 times on the largest dataset compared with other methods.The proposed Hawkeye is potential to real-time monitoring and early warning systems for wind turbines,accelerating the development of intelligent operation and maintenance.展开更多
基金supported by the Project of Philosophy and Social Science Foundation of Shanghai,China(Grant No.2020BGL011).
文摘The green and low carbon transition and development of the electricity industry is the most crucial task in realizing the“dual-carbon target”,and it is urgent to explore the incentive and subsidy mechanism to promote green electricity consumption and the cost-sharing strategy of carbon reduction,to alleviate the pressure of carbon abatement cost of each subject of the electricity supply chain.Against this background,this paper takes into account the low-carbon subsidies provided by the government and the incentive subsidies for users,and studies the optimal decision-making of each subject in the electricity supply chain,so that each of them can obtain the optimal profit and achieve carbon emission reduction at the same time.Firstly,taking into account the direct power purchase mode of large users and the electricity-selling companies emerging after the reform of the power sales side,we have established a cooperative mechanism for sharing the cost of carbon emission reduction in the electricity supply chain and clarified the relationship between the supply and demand of electricity among the main parties.Subsequently,considering government low-carbon subsidies and user incentive subsidies,the optimal decisionmaking model is established under two scenarios of decentralized and centralized cooperative games in the supply chain,respectively,with the objective of maximizing profits and carbon reduction rates.Solving for the optimal proportion of carbon abatement costs shared by each participant in the electricity supply chain in achieving game equilibrium.Finally,we analyze the role of the government’s low-carbon subsidies,users’incentive subsidies,and other factors on the profit and carbon reduction effect of the electricity industry through the example analysis and further analyze the impact of carbon abatement cost-sharing measures to provide recommendations for the electricity industry to realize low-carbon abatement and make decisions.
基金supported by Shanghai Rising-Star Program(No.22QA1403900)the National Natural Science Foundation of China(No.71804106)the Noncarbon Energy Conversion and Utilization Institute under the Shanghai Class IV Peak Disciplinary Development Program.
文摘Cascading faults have been identified as the primary cause of multiple power outages in recent years.With the emergence of integrated energy systems(IES),the conventional approach to analyzing power grid cascading faults is no longer appropriate.A cascading fault analysis method considering multi-energy coupling characteristics is of vital importance.In this study,an innovative analysis method for cascading faults in integrated heat and electricity systems(IHES)is proposed.It considers the degradation characteristics of transmission and energy supply com-ponents in the system to address the impact of component aging on cascading faults.Firstly,degradation models for the current carrying capacity of transmission lines,the water carrying capacity and insulation performance of thermal pipelines,as well as the performance of energy supply equipment during aging,are developed.Secondly,a simulation process for cascading faults in the IHES is proposed.It utilizes an overload-dominated development model to predict the propagation path of cascading faults while also considering network islanding,electric-heating rescheduling,and load shedding.The propagation of cascading faults is reflected in the form of fault chains.Finally,the results of cascading faults under different aging levels are analyzed through numerical examples,thereby verifying the effectiveness and rationality of the proposed model and method.
文摘According to green view and history data, the demonstrated method is applied to analyze the green rationality on the present structure of thermal, hydraulic and nuclear electric power resource; the green trend of large-scale on thermal power generating unit in our history; the distribution of between water and coal resource with the space structure of electric power installed capacity, and recommending the premise, dominance and direction of building our green electric power structure in the future.
基金supported by Yunnan Provincial Basic Research Project(202401AT070344,202301AT070443)National Natural Science Foundation of China(62263014,52207105)+1 种基金Yunnan Lancang-Mekong International Electric Power Technology Joint Laboratory(202203AP140001)Major Science and Technology Projects in Yunnan Province(202402AG050006).
文摘Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.
基金Sponsored by the Natural Science Foundation of Shanghai Education Committee(Grant No.05LZ13)Shanghai Leading Academic Discipline Project(Grant No. P1303)Shanghai Elitist Project(Grant No.04YQHB126)
文摘In order to realize automatic tracking drift of resonance frequency of ultrasonic vibration system with high power and high quality factor Q, adaptive fuzzy control was studied with a self-fabricated ultrasonic plastic welding machine. At first, relations between amplitude of vibration and frequency as well as main loop current and amplitude of vibration were analyzed. From this analysis, we deduced that frequency tracking process of the vibration system can be concluded as an optimizing problem of one dimensional fluctuant extremum of main loop current in vibration system. Then a method of self-optimizing fuzzy control, used for the realization of automatic frequency tracking in vibration system, is presented on the basis of serf-optimizing adaptive control approach and fuzzy control approach. The result of experiments shows that the fuzzy self-optimizing method can solve the problem of tracking frequency drift very well. Response time of tracking in the system is less than 50 ms, which basically meets the requirements of frequency tracking in ultrasonic plastic welding machine.
文摘An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady state index based on chaotic theory and least squares support vector machine (LSSVM) is proposed in this paper. At first, the phase space reconstruction of original power quality data is performed to form a new data space containing the attractor. The new data space is used as training samples for the LSSVM. Then in order to predict power quality steady state index accurately, the particle swarm algorithm is adopted to optimize parameters of the LSSVM model. According to the simulation results based on power quality data measured in a certain distribution network, the model applies to several indexes with higher forecasting accuracy and strong practicability.
文摘In order to make an intensive study of the development of smart power distribution and utilization technology in China, their research hotspots and frontier technology are selected out through combining the informatics method, and using the CiteSpace which can take keyword cooccurrence analysis and draw the visualization graph. According to this result, we can infer the development trend of smart power distribution and utilization in the future, and providing reference for the researcher whose engage in this domain. The electric related literature was collected from the CNKI database in China. Under the smart power distribution and utilization domain, we also analyze the development of the power quality and the energy internet in detail.
基金The project is supported by the National Natural Science Foundation of China （60074031, 60474052）.
文摘Capacitive humidity sensors were made of nanometer barium titanate.The pellets were prepared under different pressures between 3920N to 7850N force.The capacitance changes in three orders of magnitude in the relative humidity range of 10%～98%,indicating high humidity sensitivity of the sensors.At a certain measuring frequency,the capacitance of the sensors increases as increasing of the preparation pressure,while the sensitivity of the sensors basically remains the same.The frequencies corresponding to the peaks of the dielectric loss of the sensors move to the higher frequency direction as increasing of the relative humidity.At a certain humidity,the frequencies corresponding to the peaks of the dielectric loss move to the higher frequency direction as increasing of the preparation pressure.
文摘Combined with actual situation of Fengxian power Supply Company, the neutral grounding modes of Fengxian 35 kV and 10 kV power grid are studied in the paper. The different frequencies injected method is used to measure the capacitive current of Fengxian 28 substations, and the neutral grounding modes of the 28 substations are determined based on the measured values of capacitive current.
基金Shanghai Rising-Star Program(Grant No.21QA1403400)Shanghai Sailing Program(Grant No.20YF1414800)Shanghai Key Laboratory of Power Station Automation Technology(Grant No.13DZ2273800).
文摘With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.
基金Supported by the National Natural Science Foundation of China under Grant Nos 60806031 and 11004130the Natural Science Foundation of Zhejiang Province under Grant No Y6100596.
文摘Ultrathin high-k dielectric ErAlO films were deposited on Si(100)substrates by using radio-frequency magnetron sputtering.The very flat surface of the annealed film with a rms roughness less than 0.25 nm was observed by using an atomic force microscope.The film shows good thermal stability when annealing at 900℃ for 30 s in the O_(2) ambient.The effective dielectric constant of the film is around 15.2,and a low leakage current of 8.4×10^(-5) A/cm^(2) at an electric field of 1 MV/cm was achieved for the film with the equivalent oxide thickness of 2.0 nm after annealing.
基金This work was supported by Project of Philosophy and Social Science Foundation of Shanghai,China(Grant No.2020BGL011).
文摘The randomness and uncertainty of renewable energy generation are expected to significantly change the optimal decision-making of trans-provincial electricity market subjects.Therefore,it is beneficial to optimize the interests of each of these subjects,considering the unpredictable risks of renewable energy under the renewable portfolio standards(RPS)and researching their effects on the optimal decision-making of transprovincial electricity market multi-subjects.First,we develop a trans-provincial trading market mechanism for renewable energy and clarify the electricity supply and demand relation and the green certificates supply and demand relation of trans-provincial electricitymarketmulti-subjects.Then,under the RPS,we construct a multi-subject game model of the power supply chain that recognizes the risks,and adopt the reverse induction method to discuss the optimum risk-taking judgment of each subject in the trans-provincial electricity market.Finally,we useMATLAB to verify the viability and efficacy of the proposed gamemodel,and obtain a certain reference value for the optimal decision-making of trans-provincial electricity market subjects.In summary,we consider the uncertainty risks of renewable energy under RPS,study the effects of the green certificate price and risk aversion coefficient in the RPS mechanism on the optimal decisionmaking of trans-provincial electricity market subjects,and obtain the changing trends of two different power products and those of different electricity market subjects under the influence of the green certificate price and risk aversion coefficient,which have a certain reference value for studying the factors affecting the optimal decision-making of trans-provincial electricity market subjects.
基金supported by science and technology projects of Gansu State Grid Corporation of China(52272220002U).
文摘Optical Character Recognition(OCR)refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image.This paper is a deep study on the recognition effect of OCR based on Artificial Intelligence(AI)algorithms,in which the different AI algorithms for OCR analysis are classified and reviewed.Firstly,the mechanisms and characteristics of artificial neural network-based OCR are summarized.Secondly,this paper explores machine learning-based OCR,and draws the conclusion that the algorithms available for this form of OCR are still in their infancy,with low generalization and fixed recognition errors,albeit with better recognition effect and higher recognition accuracy.Finally,this paper explores several of the latest algorithms such as deep learning and pattern recognition algorithms.This paper concludes that OCR requires algorithms with higher recognition accuracy.
基金supported by Project of Philosophy and Social Science Foundation of Shanghai,China(Grant No.2020BGL011).
文摘Under the background of implementing renewable portfolio standards and the ever-improving tradable green certificate scheme,the increasingly environmentally-friendly preference of power users is leading to changes in electricity demand,which,in turn,is driving changes in the decision-making behaviors of various actors in the power supply chain.Based on this,with the goal of pursuing maximum profit,consumer-power-demand functions have been introduced with some consideration of the factors of consumer preference to establish an optimal profit model for each trading subject in non-cooperative states of the power supply chain,under the constraints of meeting renewable energy portfolio standards.Here,the optimal strategy of each trading subject is presented by adopting the reverse induction method.Furthermore,examples are used to analyze factors such as the influence of environmental protection preferences,quota ratios,price substitutions,and market demand as well as the optimal profit of each trading subject in view of providing a reference for the decision-making in the power supply chain trading subjects.
文摘When using the ATP-EMTP software to simulate the electromagnetic transient of local line or nearby line for a large area power grid, it is necessary to divide the area grid into internal network and external network so as to make the external network simple for the restriction of the simulation calculation scale of software. Based on the existing equivalence method, in this paper we proposed a practical equivalent method using the short-circuit calculation program of PSASP. In this way, we completed the equivalence of northwest power grid operating at the large mode of summer 2016. By comparing the power flow and the characteristics of short-circuit in the system before and after the equivalence, this equivalent method is proved to be correct.
基金supported by Funds for the National Natural Science Foundation of China Youth Project(Grant Nos.71103120&51507099)Shanghai Social Science Planning General Project(Grant No.2018BGl019).
文摘Electricity productivity is regarded as a major assessment indicator in the design of energy saving policies,given that China has entered a“New Normal”of economic development.In fact,enhancing electricity productivity in an all-round way,as is one of the binding indicators for energy and environmental issues,means that non-growth target of total electric energy consumption in the economic development is feasible.The Gini coefficient,Theil index,and Mean log deviation are utilized to measure regional differences in China’s electricity productivity from 1997 to 2016 in five regions,and conditionalβconvergence is empirically analyzed with the spatial Durbin model.The results show that:(1)China’s electricity productivity is improving,while the overall feature is that the eastern area has a higher efficiency than the western area.(2)The difference in electricity productivity is the smallest in the northeast and the largest in the northwest.Interregional difference plays an important role and is the main cause for the differences.(3)The electricity productivity in China exhibitsβconvergence,except for the northwest.The positive driving factor is urbanization level(0.0485%),and the negative driving factor is FDI(–0.0104%).Moreover,the urbanization rate(0.0669%),foreign direct investment(0.0960%),and the industrial structure(–0.0769%)have a spatial spillover effect on improving regional electricity productivity.Based on this conclusion,the study provides some recommendations for saving energy policy design in China’s power industry.
文摘In order to ensure that the large-scale application of photovoltaic power generation does not affect the stability of the grid, accurate photovoltaic (PV) power generation forecast is essential. A short-term PV power generation forecast method using the combination of K-means++, grey relational analysis (GRA) and support vector regression (SVR) based on feature selection (Hybrid Kmeans-GRA-SVR, HKGSVR) was proposed. The historical power data were clustered through the multi-index K-means++ algorithm and divided into ideal and non-ideal weather. The GRA algorithm was used to match the similar day and the nearest neighbor similar day of the prediction day. And selected appropriate input features for different weather types to train the SVR model. Under ideal weather, the average values of MAE, RMSE and R2 were 0.8101, 0.9608 kW and 99.66%, respectively. And this method reduced the average training time by 77.27% compared with the standard SVR model. Under non-ideal weather conditions, the average values of MAE, RMSE and R2 were 1.8337, 2.1379 kW and 98.47%, respectively. And this method reduced the average training time of the standard SVR model by 98.07%. The experimental results show that the prediction accuracy of the proposed model is significantly improved compared to the other five models, which verify the effectiveness of the method.
文摘The Ramsey number is a foundational result in combinatorics. This article will introduce Ramsey number with the method of graph theory, and the Ramsey pricing theory is applied to the sales price and study of cross subsidy. Based on the status of our sales price and cross subsidy, Ramsey pricing methods theoretically guide adjustment thoughts of sales price and solve the practical problems in our life.
基金supported by the National Key Research and Development Program of China under Grant2021YFB2500600the Youth Innovation Promotion Association CAS under Grant2022138+2 种基金the National Natural Science Foundation of China under Grant51901221the Institute of Electrical EngineeringCAS under GrantE155710201 and E155710301。
文摘High-performance Cu/Graphene composite wire synergistically strengthened by nano Cr_(3)C_(2) phase was directly synthesized via hot press sintering followed by severe cold plastic deformation, using liquid paraffin and CuCr alloy powder as the raw materials. Since graphene is in situ formed under the catalysis of copper powder during the sintering process, the problem that graphene is easy to agglomerate and difficult to disperse uniformly in the copper matrix has been solved. The nano Cr_(3)C_(2)-particles nailed at the interface favor to improve the interface bonding. The Cu/Graphene composite possesses high electrical conductivity, hardness, and plasticity. The composite wire exhibits high electrical conductivity of 96.93% IACS, great tensile strength of 488MPa, and excellent resistance to softening. Even after annealing at 400℃ for 1 h, the tensile strength can still reach 268 MPa with a conductivity of about 99.14% IACS.The wire's temperature coefficient of resistance(TCR) is largely reduced to 0.0035/℃ due to the complex structure,which leads the wire to present low resistivity at higher temperatures. Such Cu/Graphene composite wire with excellent comprehensive performance has a good application prospect in high-power density motors.
基金supported by Shanghai Electric Power Energy Technology Co.,Ltd.2023 Science and Technology Project under Grant No.33019006220801.
文摘Time-series anomaly detection plays a crucial role in the operation of offshore wind turbines.Various wind turbine monitoring systems rely on time-series data to monitor and identify anomalies in real-time,as well as to initiate early warning processes.However,for offshore wind turbines with a high data density,conventional methods have high computational overhead in detecting anomalies while failing to accurately detect anomalies due to variations in data scales.To address this challenge,we propose an efficient anomaly detection method with contrastive learning,called Hawkeye.Hawkeye is based on residual clustering,an unsupervised anomaly detection method for multivariate time-series data.To ensure accurate anomaly detection,a trend-capturing prediction module is also combined with an automatic labeling module.As a result,the most common information can be learned from multivariate time-series data to reconstruct data trends.By evaluating Hawkeye on public datasets and real-world offshore wind turbine operational datasets,the results show that Hawkeye’s F_(1)-score improves by an average of 14% compared with Isolation Forest,and its size shrinks by up to 11.5 times on the largest dataset compared with other methods.The proposed Hawkeye is potential to real-time monitoring and early warning systems for wind turbines,accelerating the development of intelligent operation and maintenance.
