This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure(AMI) to realize economy and reliability targets.AMI is a crucial part of the smart grid to measure,collect,and analy...This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure(AMI) to realize economy and reliability targets.AMI is a crucial part of the smart grid to measure,collect,and analyze data about energy usage and power quality from customer premises.From the communication perspective,the AMI consists of smart meters,Home Area Network(HAN) gateways and data concentrators;in particular,the redundancy optimization problem focus on deciding which data concentrator needs redundancy.In order to solve the problem,we first develop a quantitative analysis model for the network economic loss caused by the data concentrator failures.Then,we establish a complete redundancy optimization model,which comprehensively consider the factors of reliability and economy.Finally,an advanced redundancy deployment method based on genetic algorithm(GA) is developed to solve the proposed problem.The simulation results testify that the proposed redundancy optimization method is capable to build a reliable and economic smart grid communication network.展开更多
The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power collectionsystems.However,the intelligent classificati...The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power collectionsystems.However,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault categories.To address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct modules.The first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the classifiers.To enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse autoencoder.To tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation module.Finally,anovel Stacking ensemble classifier for SM fault diagnosis is developed.In contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM data.The proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,China.Experimental results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.展开更多
New methodology of designing the differential pressure flow meters for fluid energy carriers is developed in order to provide minimum uncertainty of results of flow rate measurement. This methodology is implemented in...New methodology of designing the differential pressure flow meters for fluid energy carriers is developed in order to provide minimum uncertainty of results of flow rate measurement. This methodology is implemented in “Raskhod-RU” CAD system for computer aided design and calculation of differential pressure flow meters. “Raskhod-RU” CAD meets the requirements of new Standards implemented in CIS countries (GOST 8.586.1,2,3,4,5-2005) and provides accomplishment of the following tasks: verification of conditions (constraints) for application of the differential pressure method according to the requirements of new Standards;calculation of parameters of primary device, pipe straight lengths and flow meter in general according to the requirements of new Standards;calculation of uncertainty of results of fluid flow rate and volume measurement.展开更多
The most important performance indicator of a fertilizer metering mechanism is the evenness of fertilizer flow.In this study,a regression mathematical model between the key operating parameters of the fluted roller me...The most important performance indicator of a fertilizer metering mechanism is the evenness of fertilizer flow.In this study,a regression mathematical model between the key operating parameters of the fluted roller meter and the flow evenness was developed to simulate a fluted-roller meter for metering diammonium phosphate fertilizer using the discrete element method(DEM).The model was verified by bench test using the same equipment and parameters as the DEM model.Selected working parameters of the fluted-roller meter,including roll length(L),roll rotational speed(n),and flap angle(α)(for fertilizer discharge control),were optimized to maximize the flow evenness.Flow evenness was assessed by the coefficient of variation(CV)of the discharging mass during the operation.The simulation and experiment results showed the similar trends,in terms of effects of machine parameters on the CV.The relative errors ranged from 0.2%to 34.6%with a mean of 10.5%.This demonstrated that the DEM model was feasible to simulate the metering process of the fluted-roller meter.The machine parameters that significantly affected the values of CV in descending order wereα,L and n.Both simulation and measurement results revealed that the optimal machine parameters,represented by the minimum value of CV,were observed at L=45 mm,n=55 r/min andα=22.5°.This combination of parameters returned CV values of 10.89%and 9.55%for simulations and measurements,respectively.The study provided useful information for guiding the design and selection of machine parameters for metering devices for fertilizer applications.展开更多
基金supported by the National HighTech ResearchDevelopment Program of China (863) under Grant No.2012AA050801
文摘This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure(AMI) to realize economy and reliability targets.AMI is a crucial part of the smart grid to measure,collect,and analyze data about energy usage and power quality from customer premises.From the communication perspective,the AMI consists of smart meters,Home Area Network(HAN) gateways and data concentrators;in particular,the redundancy optimization problem focus on deciding which data concentrator needs redundancy.In order to solve the problem,we first develop a quantitative analysis model for the network economic loss caused by the data concentrator failures.Then,we establish a complete redundancy optimization model,which comprehensively consider the factors of reliability and economy.Finally,an advanced redundancy deployment method based on genetic algorithm(GA) is developed to solve the proposed problem.The simulation results testify that the proposed redundancy optimization method is capable to build a reliable and economic smart grid communication network.
基金supported by the National Key R&D Program of China(No.2022YFB2403800)the National Natural Science Foundation of China(No.52277118)+1 种基金the Natural Science Foundation of Tianjin(No.22JCZDJC00660)the Open Fund in the State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources(No.LAPS23018).
文摘The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power collectionsystems.However,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault categories.To address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct modules.The first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the classifiers.To enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse autoencoder.To tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation module.Finally,anovel Stacking ensemble classifier for SM fault diagnosis is developed.In contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM data.The proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,China.Experimental results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.
文摘New methodology of designing the differential pressure flow meters for fluid energy carriers is developed in order to provide minimum uncertainty of results of flow rate measurement. This methodology is implemented in “Raskhod-RU” CAD system for computer aided design and calculation of differential pressure flow meters. “Raskhod-RU” CAD meets the requirements of new Standards implemented in CIS countries (GOST 8.586.1,2,3,4,5-2005) and provides accomplishment of the following tasks: verification of conditions (constraints) for application of the differential pressure method according to the requirements of new Standards;calculation of parameters of primary device, pipe straight lengths and flow meter in general according to the requirements of new Standards;calculation of uncertainty of results of fluid flow rate and volume measurement.
基金This research was part of a project of Development of Technology and Equipment for Fertilizer Application based on 3S Technology,sponsored by the National Research and Development Program(2016YFD0200600,2016YFD0200601).
文摘The most important performance indicator of a fertilizer metering mechanism is the evenness of fertilizer flow.In this study,a regression mathematical model between the key operating parameters of the fluted roller meter and the flow evenness was developed to simulate a fluted-roller meter for metering diammonium phosphate fertilizer using the discrete element method(DEM).The model was verified by bench test using the same equipment and parameters as the DEM model.Selected working parameters of the fluted-roller meter,including roll length(L),roll rotational speed(n),and flap angle(α)(for fertilizer discharge control),were optimized to maximize the flow evenness.Flow evenness was assessed by the coefficient of variation(CV)of the discharging mass during the operation.The simulation and experiment results showed the similar trends,in terms of effects of machine parameters on the CV.The relative errors ranged from 0.2%to 34.6%with a mean of 10.5%.This demonstrated that the DEM model was feasible to simulate the metering process of the fluted-roller meter.The machine parameters that significantly affected the values of CV in descending order wereα,L and n.Both simulation and measurement results revealed that the optimal machine parameters,represented by the minimum value of CV,were observed at L=45 mm,n=55 r/min andα=22.5°.This combination of parameters returned CV values of 10.89%and 9.55%for simulations and measurements,respectively.The study provided useful information for guiding the design and selection of machine parameters for metering devices for fertilizer applications.
