Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been dev...Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high accuracy.Nevertheless,the existing method-ologies cannot obtain a high level of specificity and sensitivity.The present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet architecture.The LCSC model comprises two distinct stages.The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes.Subsequently,an improved AlexNet architecture is employed to classify lung cancer.During the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate nodules.The suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or malignant.The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.展开更多
Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particula...Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.展开更多
An integrated energy service company in an industrial park or commercial building is responsible for managing all energy sources in their local region, including electricity, water, gas, heating, and cooling. To reduc...An integrated energy service company in an industrial park or commercial building is responsible for managing all energy sources in their local region, including electricity, water, gas, heating, and cooling. To reduce energy wastage and increase energy utilization, it is necessary to perform efficiency analyses and diagnoses on integrated energy systems(IESs). However, the integrated energy data necessary for energy efficiency analyses and diagnoses come from a wide variety of instruments, each of which uses different transmission protocols and data formats. This makes it challenging to handle energy-flow data in a unified manner. Thus, we have constructed a unified model for diagnosing energy usage abnormalities in IESs. Using this model, the data are divided into working days and non-working days, and benchmark values are calculated after the data have been weighted to enable unified analysis of several types of energy data. The energy-flow data may then be observed, managed, and compared in all aspects to monitor sudden changes in energy usage and energy wastage. The abnormal data identified and selected by the unified model are then subjected to big-data analysis using technical management tools, enabling the detection of user problems such as abnormalities pertaining to acquisition device, metering, and energy usage. This model facilitates accurate metering of energy data and improves energy efficiency. The study has significant implications in terms of fulfilling the energy saving.展开更多
In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF...In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic.展开更多
The phenomenon of electrical potential differences along the plant apoplast has been reported for more than a century. Earlier works of harvesting energy from trees reported nW range of power with a few hundred-mV ope...The phenomenon of electrical potential differences along the plant apoplast has been reported for more than a century. Earlier works of harvesting energy from trees reported nW range of power with a few hundred-mV open circuit voltage and near uA range short circuit current. In this work, we show that if we cut a stem into pieces, each segment would maintain nearly the same open circuit voltage and short circuit current regardless of length. Using a pico-ampere meter, we also found that the living cells in the vascular cambial and secondary xylem and phloem tissues are the source of electricity. They provide a relatively constant voltage and current to external environment for reasons still under investigation. We demonstrate that by cascading separated stems we can accumulate up to 2 V of open circuit voltage. We also demonstrate by connecting them in parallel we can increase the short circuit current.展开更多
A review on thermal power plant automation development in China over 50 years is presented. The level of thermal power automation is introduced, especially for 200 MW and above units which are clarified into three cat...A review on thermal power plant automation development in China over 50 years is presented. The level of thermal power automation is introduced, especially for 200 MW and above units which are clarified into three categories by grade. The conditions, existing problems, relevant solutions and policies are summarized chronologically in aspects of centralized control, automatic regulation and controllability of main and auxiliary units, turbine control system, furnace security protection, and computer application in thermal power plants. This paper also points out the development tendency of thermal power plant automation and concepts of some vocabularies.展开更多
Windbelt generators have been proposed as small, green power sources for battery charging applications. Some of the reported results lack detailed information about how key parameters influence the output power of the...Windbelt generators have been proposed as small, green power sources for battery charging applications. Some of the reported results lack detailed information about how key parameters influence the output power of the generator. In this work, we built prototypes with different architectures to study the voltage generation and power delivery as functions of belt tension, length, and electrical load at various wind speeds. We also studied the maximum power delivery conditions before the breakdown of the belt oscillation occurs. Our results are obtained from windbelt generators with two types of architectures： a conventional design with an adjustable belt that uses weight for tension control, and a revised design with a belt oscillation perpendicular to the coil axis. We have concluded that the breakdown of the belt oscillation at lower output resistances is a primary bottleneck that will limit windbelt systems to only very low power applications.展开更多
To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a clust...To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole.Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties.In this paper,we introduce the stochastic power flexibility range(PFR)and timecoupling flexibility(TCF)to describe the power flexibility of VPP.In this model,both operational constraints and the randomness of the DERs’output are incorporated,and a combined model and data-driven solution is proposed to obtain the stochastic PFR,TCF,and cost function of VPP.The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding,considering uncertainties.Finally,a numerical test is performed.The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.展开更多
Calculation of static voltage stability margin(SVSM)of AC/DC power systems with lots of renewable energy sources(RESs)integration requires consideration of uncertain load growth and renewable energy generation output....Calculation of static voltage stability margin(SVSM)of AC/DC power systems with lots of renewable energy sources(RESs)integration requires consideration of uncertain load growth and renewable energy generation output.This paper presents a bi-level optimal power flow(BLOPF)model to identify the worst-case SVSM of an AC/DC power system with line commutation converter-based HVDC and multi-terminal voltage sourced converter-based HVDC transmission lines.Constraints of uncertain load growth’s hypercone model and control mode switching of DC converter stations are considered in the BLOPF model.Moreover,uncertain RES output fluctuations are described as intervals,and two three-level optimal power flow(TLOPF)models are established to identify interval bounds of the system worst-case SVSM.The two TLOPF models are both transformed into max–min bi-level optimization models according to independent characteristics of different uncertain variables.Then,transforming the inner level model into its dual form,max–min BLOPF models are simplified to single-level optimization models for direct solution.Calculation results on the modified IEEE-39 bus AC/DC case and an actual large-scale AC/DC case in China indicate correctness and efficiency of the proposed identification method.展开更多
Flexible load can optimize the load curve,which is an important means to promote renewable energy consumption.The peculiarities of electricity,heat,cooling and gas loads are analyzed in this paper,considering the fuzz...Flexible load can optimize the load curve,which is an important means to promote renewable energy consumption.The peculiarities of electricity,heat,cooling and gas loads are analyzed in this paper,considering the fuzzy degree of human perception for water temperature,and the characteristic model of hot water load is established.Considering the fuzzy degree of human perception of ambient temperature,the characteristic model of cooling load is established by using PMV and PPD index.Meanwhile,considering four combinations of cut load,translatable load,transferable load and alternative load,and considering the coupling relationship of composite parts,different response models of load are established respectively.With the minimum cost of the system,including operation and compensation costs as the objective function,the optimization scheduling model of the regional integrated energy system is established,and the Gurobi solver is used for simulation analysis to solve the optimal output and load response curve of each piece of equipment.The results show that the load curve can be optimized,the flexible regulation ability of the regional integrated energy system can be enhanced,the energy loss of the system can be reduced,and the wind power consumption ability of the system can be increased by considering the integrated demand response.展开更多
The ubiquitous power Internet of Things(UPIoT)uses modern information technology and advanced communication technologies to realize interconnection and human-computer interaction in all aspects of the power system.UPI...The ubiquitous power Internet of Things(UPIoT)uses modern information technology and advanced communication technologies to realize interconnection and human-computer interaction in all aspects of the power system.UPIoT has the characteristics of comprehensive state perception and efficient information processing,and has broad application prospects for transformation of the energy industry.The fundamental facility of the UPIoT is the sensor-based information network.By using advanced sensors,Wireless Sensor Networks(WSNs),and advanced data processing technologies,Internet of Things can be realized in the power system.In this paper,a framework of WSNs based on advanced sensors towards UPIoT is proposed.In addition,the most advanced sensors for UPIoT purposes are reviewed,along with an explanation of how the sensor data obtained in UPIoT is utilized in various scenarios.展开更多
To improve the resilience of distribution networks(DNs)in the event of extreme natural disasters such as typhoons and rainstorms,it is imperative to efficiently implement distribution service restoration(DSR)to restor...To improve the resilience of distribution networks(DNs)in the event of extreme natural disasters such as typhoons and rainstorms,it is imperative to efficiently implement distribution service restoration(DSR)to restore loads as soon as possible.In previous studies,DSR has mainly adopted the distributed resource model with droop or PQ control.This inhibits the exploitation of the potential of distributed generators(DGs)in load restoration when the DN loses support from the upstream transmission network.Thus,this paper proposes a multi-resource collaborative service restoration(MRCSR)approach for DNs incorporating local soft open points,DGs,and tie switches.The MRCSR model is developed by integrating a decentralized hierarchical droop control(DHDC)strategy and incorporating the frequency and voltage features of the load demand.A two-stage iterative feedback optimization(TSIFO)algorithm is then developed to analyze the MRCSR model in an accurate and efficient manner.Finally,the proposed model and algo-rithm are tested on the modified IEEE 33-bus system and a practical distribution system of the Taiwan Power Company to verify their effectiveness and advantages over existing approaches.展开更多
A hybrid UHVDC transmission system applying LCC as the rectifier and MMC as the inverter combines the advantages of both converter types,which makes this protection scheme more complicated.A new pilot protection schem...A hybrid UHVDC transmission system applying LCC as the rectifier and MMC as the inverter combines the advantages of both converter types,which makes this protection scheme more complicated.A new pilot protection scheme for a three-terminal hybrid DC transmission system applying energy functions is proposed.The energy function for LCC is applied to MMC to derive the energy level of the hybrid system.Furthermore,an improved Hausdorff distance(IHD)algorithm is proposed to detect the difference in energy levels between the normal and fault states.An abrupt change in energy level is characterized by IHD change rate.Time points at which the IHD change rate exceeds the threshold at converter stations are applied to determine the fault line and to estimate the fault section.The proposed protection scheme is then verified by a simulation model of the Wudongde±800 kV three-terminal hybrid UHVDC transmission project.The appropriate sampling frequency is selected for a real-time calculation,and the threshold is selected considering the effect of noise.Results show the proposed scheme can identify and trip fault lines quickly and effectively,even for a 600Ωgrounding fault.Other waveshape similarity algorithms are compared and analyzed.Compared with existing protection schemes,the proposed scheme transmits less data to improve communication speed and reliability.展开更多
Demand response has been recognized as a valuable functionality of power systems for mitigating power imbalances.This paper proposes a hierarchical control strategy among the distribution system operator(DSO),load agg...Demand response has been recognized as a valuable functionality of power systems for mitigating power imbalances.This paper proposes a hierarchical control strategy among the distribution system operator(DSO),load aggregators(LAs),and thermostatically controlled loads(TCLs);the strategy includes a scheduling layer and an executive layer to provide load regulation.In the scheduling layer,the DSO(leader)offers compensation price(CP)strategies,and the LAs(followers)respond to CP strategies with available regulation power(ARP)strategies.Profits of the DSO and LAs are modeled according to their behaviors during the load regulation process.Stackelberg game is adopted to capture interactions among the players and leader and to obtain the optimal strategy for each participant to achieve utility.Moreover,considering inevitable random factors in practice,e.g.,renewable generation and behavior of users,two different stochastic models based on sample average approximation(SAA)and parameter modification are formulated with improved scheduling accuracy.In the executive layer,distributed TCLs are triggered based on strategies determined in the scheduling layer.A self-triggering method that does not violate user privacy is presented,where TCLs receive external signals from the LA and independently determine whether to alter their operation statuses.Numerical simulations are performed on the modified IEEE-24 bus system to verify effectiveness of the proposed strategy.展开更多
Observability analysis(OA)is vital to obtaining the available input measurements of state estimation(SE)in an integrated electricity and heating system(IEHS).Considering the thermal quasi-dynamics in pipelines,the mea...Observability analysis(OA)is vital to obtaining the available input measurements of state estimation(SE)in an integrated electricity and heating system(IEHS).Considering the thermal quasi-dynamics in pipelines,the measurement equations in heating systems are dependent on the estimated results,leading to an interdependency between OA and SE.Conventional OA methods require measurement equations be known exactly before SE is performed,and they are not applicable to IEHSs.To bridge this gap,a scenario-based OA scheme for IEHSs is devised that yields reliable analysis results for a predefined set of time-delay scenarios to cope with this interdependency.As its core procedure,the observable state identification and observability restoration are formulated in terms of integer linear programming.Numerical tests are conducted to demonstrate the validity and superiority of the proposed formulation.展开更多
Use of traditional mineral oil(MO)as a liquid insulation in transformers has spanned more than 130 years.However,MO has poor heat resistance,a low ignition point,and is a non-renewable resource,which does not meet dev...Use of traditional mineral oil(MO)as a liquid insulation in transformers has spanned more than 130 years.However,MO has poor heat resistance,a low ignition point,and is a non-renewable resource,which does not meet development requirements for high-performance and environmentally friendly insulation oil.Consequently,researchers have explored alternatives such as natural ester(NE)and synthetic ester(SE)oils,as well as mixed insulation oils.Mixed insulating oil is a blend of diverse insulating oil types,with optimal performance achieved by adjusting proportions of base oils.This article summarizes the innovative achievements and development of mixed insulation oil in terms of development of mixed ratio,basic physical chemical properties,electrical properties,thermal stability,and application including operation and maintenance technology.Through these efforts,this article aims to provide recommendations for future development of mixed insulating oils to advance liquid dielectric research based on enhancement mechanisms.展开更多
Pressure monitoring of a transformer oil tank can grasp the pressure change process caused by gas production when severe internal defects occur and take timely measures to ensure the safe operation of the transformer....Pressure monitoring of a transformer oil tank can grasp the pressure change process caused by gas production when severe internal defects occur and take timely measures to ensure the safe operation of the transformer.Existing pressure sensors generally use metal encapsulation or have an air cavity structure,threatening the transformer’s insulation if it is directly used inside the transformer.To this end,this paper proposes a method for developing a high-sensitivity,large-range,and metallizationfree optical pressure sensing device with temperature compensation.Fiber grating is encapsulated by fluorosilicone rubber and supplemented by an epoxy resin shielding shell on the outside.At the same time,a double-grating vertical arrangement is adopted to improve pressure measurement sensitivity,further avoiding the influence of temperature rise caused by a defect of the transformer on the measurement result of the sensor.In addition,by optimizing the geometric structure of the internal sensitizing element,pre-stretching length of the fiber grating,gap distance,and other parameters,probe size can be reduced while ensuring the sensor’s performance.Results show the proposed method can meet the requirements of sensor fabrication with different sensitivities and ranges,and to a certain extent,both high sensitivity and extensive ranges can be taken into account.The sensitivity of the fabricated prototype is 15 pm/kPa,and the range is about 0.2 MPa.At the same time,the metal-free feature of the sensor makes it suitable for use in various oil-immersed power equipment.It records oil pressure changes caused by oil discharge breakdown,making it sensitive to small pressure changes in early failures.展开更多
Increasing penetration of distributed energy resources in the distribution network(DN)is threatening safe operation of the DN,which necessitates setup of the ancillary service market in the DN.In the ancillary service...Increasing penetration of distributed energy resources in the distribution network(DN)is threatening safe operation of the DN,which necessitates setup of the ancillary service market in the DN.In the ancillary service market,distribution system operator(DSO)is responsible for safety of the DN by procuring available capacities of aggregators.Unlike existing studies,this paper proposes a novel market mechanism composed of two parts:choice rule and payment rule.The proposed choice rule simultaneously considers social welfare and fairness,encouraging risk-averse aggregators to participate in the ancillary service market.It is then formulated as a linear programming problem,and a distributed solution using the multi-cut Benders decomposition is presented.Moreover,successful implementation of the choice rule depends on each aggregator’s truthful adoption of private parameters.Therefore,a payment rule is also designed,which is proved to possess two properties:incentive compatibility and individual rationality.Simulation results demonstrate effectiveness of the proposed choice rule on improving fairness and verify properties of the payment rule.展开更多
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(Grant Number IMSIU-RP23044).
文摘Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high accuracy.Nevertheless,the existing method-ologies cannot obtain a high level of specificity and sensitivity.The present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet architecture.The LCSC model comprises two distinct stages.The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes.Subsequently,an improved AlexNet architecture is employed to classify lung cancer.During the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate nodules.The suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or malignant.The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.
文摘Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.
基金supported by National Key Research and Development Program of China (No.2017YFB903304)the State Grid Science and Technology Program (Hybrid Simnlation Key Technology for Integrated Energy System and Platform Construction)
文摘An integrated energy service company in an industrial park or commercial building is responsible for managing all energy sources in their local region, including electricity, water, gas, heating, and cooling. To reduce energy wastage and increase energy utilization, it is necessary to perform efficiency analyses and diagnoses on integrated energy systems(IESs). However, the integrated energy data necessary for energy efficiency analyses and diagnoses come from a wide variety of instruments, each of which uses different transmission protocols and data formats. This makes it challenging to handle energy-flow data in a unified manner. Thus, we have constructed a unified model for diagnosing energy usage abnormalities in IESs. Using this model, the data are divided into working days and non-working days, and benchmark values are calculated after the data have been weighted to enable unified analysis of several types of energy data. The energy-flow data may then be observed, managed, and compared in all aspects to monitor sudden changes in energy usage and energy wastage. The abnormal data identified and selected by the unified model are then subjected to big-data analysis using technical management tools, enabling the detection of user problems such as abnormalities pertaining to acquisition device, metering, and energy usage. This model facilitates accurate metering of energy data and improves energy efficiency. The study has significant implications in terms of fulfilling the energy saving.
文摘In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic.
基金Acknowledgments This material is based upon work supported by the National Science Foundation under Grant No. EEC-0540832. The authors also wish to acknowledge the contributions to discussions on plant electrophysiology by Dr. Dan Kostov and Dr. Xing Chen.
文摘The phenomenon of electrical potential differences along the plant apoplast has been reported for more than a century. Earlier works of harvesting energy from trees reported nW range of power with a few hundred-mV open circuit voltage and near uA range short circuit current. In this work, we show that if we cut a stem into pieces, each segment would maintain nearly the same open circuit voltage and short circuit current regardless of length. Using a pico-ampere meter, we also found that the living cells in the vascular cambial and secondary xylem and phloem tissues are the source of electricity. They provide a relatively constant voltage and current to external environment for reasons still under investigation. We demonstrate that by cascading separated stems we can accumulate up to 2 V of open circuit voltage. We also demonstrate by connecting them in parallel we can increase the short circuit current.
文摘A review on thermal power plant automation development in China over 50 years is presented. The level of thermal power automation is introduced, especially for 200 MW and above units which are clarified into three categories by grade. The conditions, existing problems, relevant solutions and policies are summarized chronologically in aspects of centralized control, automatic regulation and controllability of main and auxiliary units, turbine control system, furnace security protection, and computer application in thermal power plants. This paper also points out the development tendency of thermal power plant automation and concepts of some vocabularies.
文摘Windbelt generators have been proposed as small, green power sources for battery charging applications. Some of the reported results lack detailed information about how key parameters influence the output power of the generator. In this work, we built prototypes with different architectures to study the voltage generation and power delivery as functions of belt tension, length, and electrical load at various wind speeds. We also studied the maximum power delivery conditions before the breakdown of the belt oscillation occurs. Our results are obtained from windbelt generators with two types of architectures： a conventional design with an adjustable belt that uses weight for tension control, and a revised design with a belt oscillation perpendicular to the coil axis. We have concluded that the breakdown of the belt oscillation at lower output resistances is a primary bottleneck that will limit windbelt systems to only very low power applications.
基金supported in part by the National Natural Science Foundation of China under Grant U2066601,51725703Southern Power Grid Technical Project GDKJXM20185069(032000KK52180069).
文摘To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole.Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties.In this paper,we introduce the stochastic power flexibility range(PFR)and timecoupling flexibility(TCF)to describe the power flexibility of VPP.In this model,both operational constraints and the randomness of the DERs’output are incorporated,and a combined model and data-driven solution is proposed to obtain the stochastic PFR,TCF,and cost function of VPP.The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding,considering uncertainties.Finally,a numerical test is performed.The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.
基金supported by the National Natural Science Foundation of China(Grant No.51977080)the Natural Science Foundation of Guangdong Province(Grant No.2022A1515010332)supported by the U.S.National Science Foundation(Grant#2124849).
文摘Calculation of static voltage stability margin(SVSM)of AC/DC power systems with lots of renewable energy sources(RESs)integration requires consideration of uncertain load growth and renewable energy generation output.This paper presents a bi-level optimal power flow(BLOPF)model to identify the worst-case SVSM of an AC/DC power system with line commutation converter-based HVDC and multi-terminal voltage sourced converter-based HVDC transmission lines.Constraints of uncertain load growth’s hypercone model and control mode switching of DC converter stations are considered in the BLOPF model.Moreover,uncertain RES output fluctuations are described as intervals,and two three-level optimal power flow(TLOPF)models are established to identify interval bounds of the system worst-case SVSM.The two TLOPF models are both transformed into max–min bi-level optimization models according to independent characteristics of different uncertain variables.Then,transforming the inner level model into its dual form,max–min BLOPF models are simplified to single-level optimization models for direct solution.Calculation results on the modified IEEE-39 bus AC/DC case and an actual large-scale AC/DC case in China indicate correctness and efficiency of the proposed identification method.
基金supported by the National Natural Science Foundation of China(51577086)Jiangsu Key University Science Research Project(19KJA510012)+1 种基金Six talent peaks project in Jiangsu Province(TD-XNY004)Jiangsu Qinglan Project.
文摘Flexible load can optimize the load curve,which is an important means to promote renewable energy consumption.The peculiarities of electricity,heat,cooling and gas loads are analyzed in this paper,considering the fuzzy degree of human perception for water temperature,and the characteristic model of hot water load is established.Considering the fuzzy degree of human perception of ambient temperature,the characteristic model of cooling load is established by using PMV and PPD index.Meanwhile,considering four combinations of cut load,translatable load,transferable load and alternative load,and considering the coupling relationship of composite parts,different response models of load are established respectively.With the minimum cost of the system,including operation and compensation costs as the objective function,the optimization scheduling model of the regional integrated energy system is established,and the Gurobi solver is used for simulation analysis to solve the optimal output and load response curve of each piece of equipment.The results show that the load curve can be optimized,the flexible regulation ability of the regional integrated energy system can be enhanced,the energy loss of the system can be reduced,and the wind power consumption ability of the system can be increased by considering the integrated demand response.
基金the National Natural Science Foundation of China(No.51921005).
文摘The ubiquitous power Internet of Things(UPIoT)uses modern information technology and advanced communication technologies to realize interconnection and human-computer interaction in all aspects of the power system.UPIoT has the characteristics of comprehensive state perception and efficient information processing,and has broad application prospects for transformation of the energy industry.The fundamental facility of the UPIoT is the sensor-based information network.By using advanced sensors,Wireless Sensor Networks(WSNs),and advanced data processing technologies,Internet of Things can be realized in the power system.In this paper,a framework of WSNs based on advanced sensors towards UPIoT is proposed.In addition,the most advanced sensors for UPIoT purposes are reviewed,along with an explanation of how the sensor data obtained in UPIoT is utilized in various scenarios.
基金supported by the National Natural Science Foundation of China(No.52007056,No.52207094,and No.52377095)the Science and Technology Innovation Program of Hunan Province(No.2023RC3114)the Key Research and Development Program of Hunan Province(No.2021SK2051).
文摘To improve the resilience of distribution networks(DNs)in the event of extreme natural disasters such as typhoons and rainstorms,it is imperative to efficiently implement distribution service restoration(DSR)to restore loads as soon as possible.In previous studies,DSR has mainly adopted the distributed resource model with droop or PQ control.This inhibits the exploitation of the potential of distributed generators(DGs)in load restoration when the DN loses support from the upstream transmission network.Thus,this paper proposes a multi-resource collaborative service restoration(MRCSR)approach for DNs incorporating local soft open points,DGs,and tie switches.The MRCSR model is developed by integrating a decentralized hierarchical droop control(DHDC)strategy and incorporating the frequency and voltage features of the load demand.A two-stage iterative feedback optimization(TSIFO)algorithm is then developed to analyze the MRCSR model in an accurate and efficient manner.Finally,the proposed model and algo-rithm are tested on the modified IEEE 33-bus system and a practical distribution system of the Taiwan Power Company to verify their effectiveness and advantages over existing approaches.
基金supported by the Science and Technology Project of China Southern Power Co.,Ltd.(CGYKJXM20180508).
文摘A hybrid UHVDC transmission system applying LCC as the rectifier and MMC as the inverter combines the advantages of both converter types,which makes this protection scheme more complicated.A new pilot protection scheme for a three-terminal hybrid DC transmission system applying energy functions is proposed.The energy function for LCC is applied to MMC to derive the energy level of the hybrid system.Furthermore,an improved Hausdorff distance(IHD)algorithm is proposed to detect the difference in energy levels between the normal and fault states.An abrupt change in energy level is characterized by IHD change rate.Time points at which the IHD change rate exceeds the threshold at converter stations are applied to determine the fault line and to estimate the fault section.The proposed protection scheme is then verified by a simulation model of the Wudongde±800 kV three-terminal hybrid UHVDC transmission project.The appropriate sampling frequency is selected for a real-time calculation,and the threshold is selected considering the effect of noise.Results show the proposed scheme can identify and trip fault lines quickly and effectively,even for a 600Ωgrounding fault.Other waveshape similarity algorithms are compared and analyzed.Compared with existing protection schemes,the proposed scheme transmits less data to improve communication speed and reliability.
基金supported by the Natural Science Foundation of Jiangsu Province(SBK2023043599)Introduction of teacher research start-up fees(423167)National Natural Science Foundation of China(51837004,U2066601)。
文摘Demand response has been recognized as a valuable functionality of power systems for mitigating power imbalances.This paper proposes a hierarchical control strategy among the distribution system operator(DSO),load aggregators(LAs),and thermostatically controlled loads(TCLs);the strategy includes a scheduling layer and an executive layer to provide load regulation.In the scheduling layer,the DSO(leader)offers compensation price(CP)strategies,and the LAs(followers)respond to CP strategies with available regulation power(ARP)strategies.Profits of the DSO and LAs are modeled according to their behaviors during the load regulation process.Stackelberg game is adopted to capture interactions among the players and leader and to obtain the optimal strategy for each participant to achieve utility.Moreover,considering inevitable random factors in practice,e.g.,renewable generation and behavior of users,two different stochastic models based on sample average approximation(SAA)and parameter modification are formulated with improved scheduling accuracy.In the executive layer,distributed TCLs are triggered based on strategies determined in the scheduling layer.A self-triggering method that does not violate user privacy is presented,where TCLs receive external signals from the LA and independently determine whether to alter their operation statuses.Numerical simulations are performed on the modified IEEE-24 bus system to verify effectiveness of the proposed strategy.
基金supported by National Natural Science Foundation of China(52177086)Fundamental Research Funds for the Central Universities(2023ZYGXZR063).
文摘Observability analysis(OA)is vital to obtaining the available input measurements of state estimation(SE)in an integrated electricity and heating system(IEHS).Considering the thermal quasi-dynamics in pipelines,the measurement equations in heating systems are dependent on the estimated results,leading to an interdependency between OA and SE.Conventional OA methods require measurement equations be known exactly before SE is performed,and they are not applicable to IEHSs.To bridge this gap,a scenario-based OA scheme for IEHSs is devised that yields reliable analysis results for a predefined set of time-delay scenarios to cope with this interdependency.As its core procedure,the observable state identification and observability restoration are formulated in terms of integer linear programming.Numerical tests are conducted to demonstrate the validity and superiority of the proposed formulation.
基金supported in part by the National Natural Science Foundation of China under Grant 52077015the National Natural Science Foundation of China under Grant HWQB2023001the Graduate Research and Innovation Foundation of Chongqing,China under Grant CYB23025.
文摘Use of traditional mineral oil(MO)as a liquid insulation in transformers has spanned more than 130 years.However,MO has poor heat resistance,a low ignition point,and is a non-renewable resource,which does not meet development requirements for high-performance and environmentally friendly insulation oil.Consequently,researchers have explored alternatives such as natural ester(NE)and synthetic ester(SE)oils,as well as mixed insulation oils.Mixed insulating oil is a blend of diverse insulating oil types,with optimal performance achieved by adjusting proportions of base oils.This article summarizes the innovative achievements and development of mixed insulation oil in terms of development of mixed ratio,basic physical chemical properties,electrical properties,thermal stability,and application including operation and maintenance technology.Through these efforts,this article aims to provide recommendations for future development of mixed insulating oils to advance liquid dielectric research based on enhancement mechanisms.
基金supported by The National Key R&D Program of China,(2020YFB0905902)the Science and technology project of SGCC(State Grid Corporation of China)Key Technologies of Power Internet of Things.
文摘Pressure monitoring of a transformer oil tank can grasp the pressure change process caused by gas production when severe internal defects occur and take timely measures to ensure the safe operation of the transformer.Existing pressure sensors generally use metal encapsulation or have an air cavity structure,threatening the transformer’s insulation if it is directly used inside the transformer.To this end,this paper proposes a method for developing a high-sensitivity,large-range,and metallizationfree optical pressure sensing device with temperature compensation.Fiber grating is encapsulated by fluorosilicone rubber and supplemented by an epoxy resin shielding shell on the outside.At the same time,a double-grating vertical arrangement is adopted to improve pressure measurement sensitivity,further avoiding the influence of temperature rise caused by a defect of the transformer on the measurement result of the sensor.In addition,by optimizing the geometric structure of the internal sensitizing element,pre-stretching length of the fiber grating,gap distance,and other parameters,probe size can be reduced while ensuring the sensor’s performance.Results show the proposed method can meet the requirements of sensor fabrication with different sensitivities and ranges,and to a certain extent,both high sensitivity and extensive ranges can be taken into account.The sensitivity of the fabricated prototype is 15 pm/kPa,and the range is about 0.2 MPa.At the same time,the metal-free feature of the sensor makes it suitable for use in various oil-immersed power equipment.It records oil pressure changes caused by oil discharge breakdown,making it sensitive to small pressure changes in early failures.
基金supported by the National Natural Science Foundation of China(No.52177077).
文摘Increasing penetration of distributed energy resources in the distribution network(DN)is threatening safe operation of the DN,which necessitates setup of the ancillary service market in the DN.In the ancillary service market,distribution system operator(DSO)is responsible for safety of the DN by procuring available capacities of aggregators.Unlike existing studies,this paper proposes a novel market mechanism composed of two parts:choice rule and payment rule.The proposed choice rule simultaneously considers social welfare and fairness,encouraging risk-averse aggregators to participate in the ancillary service market.It is then formulated as a linear programming problem,and a distributed solution using the multi-cut Benders decomposition is presented.Moreover,successful implementation of the choice rule depends on each aggregator’s truthful adoption of private parameters.Therefore,a payment rule is also designed,which is proved to possess two properties:incentive compatibility and individual rationality.Simulation results demonstrate effectiveness of the proposed choice rule on improving fairness and verify properties of the payment rule.
