In this paper, a security protocol for the advanced metering infrastructure （AMI） in smart grid is proposed. Through the AMI, customers and the service provider achieve two-way communication. Real-time monitoring an...In this paper, a security protocol for the advanced metering infrastructure （AMI） in smart grid is proposed. Through the AMI, customers and the service provider achieve two-way communication. Real-time monitoring and demand response can be applied because of the information exchanged. Since the information contains much privacy of the customer, and the control messages need to be authenticated, security needs to be ensured for the communication in the AM1. Due to the complicated network structure of the AMI, the asymmetric communications, and various security requirements, existing security protocols for other networks can hardly be applied into the AMI directly. Therefore, a security protocol specifically for the AMI to meet the security requirements is proposed. Our proposed security protocol includes initial authentication, secure uplink data aggregation, secure downlink data transmission, and domain secrets update. Compared with existing researches in related areas, our proposed security protocol takes the asymmetric communications of the AMI and various security requirements in smart grid into consideration.展开更多
The widely adoption of Electric Vehicle (EV) has been identified as a major challenge for future development of smart grids. The ever increasing electric vehicle charging further increases the energy demand. This pape...The widely adoption of Electric Vehicle (EV) has been identified as a major challenge for future development of smart grids. The ever increasing electric vehicle charging further increases the energy demand. This paper reports the development of an Advanced Metering Infrastructure (AMI) as an effective tool to reshape the load profile of EV charging by adopting appropriate demand side management strategy. This paper presents a total solution for EV charging service platform (EVAMI) based on power line and internet communication. It must be stressed that the development of Third Party Customer Service Platform in this investigation facilitates a single bill to be issued to EV owners. Hence, EV owners understand their energy usage and thus may perform energy saving activity efficiently. Experiment and evaluation of the proposed system show that the throughput achieved is about 5 Mbps at 10 ms end to end delay in Power line Communication. By introducing two dimensional dynamic pricing and charging schedule, the proposed EVAMI successfully reduces 36% peak consumption and increases the “off peak” consumption by 54%. Therefore the EVAMI does not only reduce the peak consumption but also relocates the energy demand effectively.展开更多
Advanced Metering Infrastructure(AMI)is the metering network of the smart grid that enables bidirectional communications between each consumer’s premises and the provider’s control center.The massive amount of data ...Advanced Metering Infrastructure(AMI)is the metering network of the smart grid that enables bidirectional communications between each consumer’s premises and the provider’s control center.The massive amount of data collected supports the real-time decision-making required for diverse applications.The communication infrastructure relies on different network types,including the Internet.This makes the infrastructure vulnerable to various attacks,which could compromise security or have devastating effects.However,traditional machine learning solutions cannot adapt to the increasing complexity and diversity of attacks.The objective of this paper is to develop an Anomaly Detection System(ADS)based on deep learning using the CIC-IDS2017 dataset.However,this dataset is highly imbalanced;thus,a two-step sampling technique:random under-sampling and the Synthetic Minority Oversampling Technique(SMOTE),is proposed to balance the dataset.The proposed system utilizes a multiple hidden layer Auto-encoder(AE)for feature extraction and dimensional reduction.In addition,an ensemble voting based on both Random Forest(RF)and Convolu-tional Neural Network(CNN)is developed to classify the multiclass attack cate-gories.The proposed system is evaluated and compared with six different state-of-the-art machine learning and deep learning algorithms:Random Forest(RF),Light Gradient Boosting Machine(LightGBM),eXtreme Gradient Boosting(XGboost),Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and bidirectional LSTM(biLSTM).Experimental results show that the proposed model enhances the detection for each attack class compared with the other machine learning and deep learning models with overall accuracy(98.29%),precision(99%),recall(98%),F_(1) score(98%),and the UNDetection rate(UND)(8%).展开更多
An advanced metering infrastructure(AMI)system plays a key role in the smart grid(SG),but it is vulnerable to cyberattacks.Current detection methods for AMI cyberattacks mainly focus on the data center or a distribute...An advanced metering infrastructure(AMI)system plays a key role in the smart grid(SG),but it is vulnerable to cyberattacks.Current detection methods for AMI cyberattacks mainly focus on the data center or a distributed independent node.On one hand,it is difficult to train an excellent detection intrusion model on a self-learning independent node.On the other hand,large amounts of data are shared over the network and uploaded to a central node for training.These processes may compromise data privacy,cause communication delay,and incur high communication costs.With these limitations,we propose an intrusion detection method for AMI system based on federated learning(FL).The intrusion detection system is deployed in the data concentrators for training,and only its model parameters are communicated to the data center.Furthermore,the data center distributes the learning to each data concentrator through aggregation and weight assignments for collaborative learning.An optimized deep neural network(DNN)is exploited for this proposed method,and extensive experiments based on the NSL-KDD dataset are carried out.From the results,this proposed method improves detection performance and reduces computation costs,communication delays,and communication overheads while guaranteeing data privacy.展开更多
With the proliferation of smart grid research, the Advanced Metering Infrastructure （AMI） has become the first ubiquitous and fixed computing platform. However, due to the unique characteristics of AMI, such as comp...With the proliferation of smart grid research, the Advanced Metering Infrastructure （AMI） has become the first ubiquitous and fixed computing platform. However, due to the unique characteristics of AMI, such as complex network structure, resource-constrained smart meter, and privacy-sensitive data, it is an especially challenging issue to make AMI secure. Energy theft is one of the most important concerns related to the smart grid implementation. It is estimated that utility companies lose more than S25 billion every year due to energy theft around the world. To address this challenge, in this paper, we discuss the background of AMI and identify major security requirements that AMI should meet. Specifically, an attack tree based threat model is first presented to illustrate the energy-theft behaviors in AMI. Then, we summarize the current AMI energy-theft detection schemes into three categories, i.e., classification-based, state estimation-based, and game theory-based ones, and make extensive comparisons and discussions on them. In order to provide a deep understanding of security vulnerabilities and solutions in AMI and shed light on future research directions, we also explore some open challenges and potential solutions for energy-theft detection.展开更多
The use of unmanned aerial vehicles(UAVs) in the collection of data from wireless devices, sensor nodes, and the Internet of Things(IoT) devices has recently received significant attention. In this paper, we investiga...The use of unmanned aerial vehicles(UAVs) in the collection of data from wireless devices, sensor nodes, and the Internet of Things(IoT) devices has recently received significant attention. In this paper, we investigate the data collection process from a set of smart meters in advanced metering infrastructure(AMI) enabled by UAVs. The objective is to minimize the total annual cost of the electric utility by jointly optimizing the number of UAVs, their power source sizing, the charging locations as well as the data collection trip planning. This is achieved while considering the energy budgets of batteries of UAVs and the required amount of collected data. The problem is formulated as a mixed-integer nonlinear programming(MINLP), which is decoupled into two sub-problems where a candidate UAV and a number of buildings are first grouped into trips via genetic algorithms(GAs), and then the optimum trip path is found using a traveling salesman problem(TSP) branch and bound algorithm. Simulation results show that the battery capacity or the number of UAVs increases as the coverage area or the density increases.展开更多
针对目前配电线路参数缺少精准辨识的难题,提出了基于高级量测体系(advanced meter infrastructure,AMI)量测信息的低压配电网线路参数辨识方法。该方法基于AMI量测数据,从网络末节点开始对整个网络的支路参数进行分步估计,建立关于低...针对目前配电线路参数缺少精准辨识的难题,提出了基于高级量测体系(advanced meter infrastructure,AMI)量测信息的低压配电网线路参数辨识方法。该方法基于AMI量测数据,从网络末节点开始对整个网络的支路参数进行分步估计,建立关于低压配电线路阻抗参数的非线性方程组,并采用最小二乘法对其进行求解,实现全网可观测线路的辨识和估计。算例证明了方法的可行性与有效性。展开更多
基金supported by the National Science Fourdation under Grant No.CNS-1423408
文摘In this paper, a security protocol for the advanced metering infrastructure （AMI） in smart grid is proposed. Through the AMI, customers and the service provider achieve two-way communication. Real-time monitoring and demand response can be applied because of the information exchanged. Since the information contains much privacy of the customer, and the control messages need to be authenticated, security needs to be ensured for the communication in the AM1. Due to the complicated network structure of the AMI, the asymmetric communications, and various security requirements, existing security protocols for other networks can hardly be applied into the AMI directly. Therefore, a security protocol specifically for the AMI to meet the security requirements is proposed. Our proposed security protocol includes initial authentication, secure uplink data aggregation, secure downlink data transmission, and domain secrets update. Compared with existing researches in related areas, our proposed security protocol takes the asymmetric communications of the AMI and various security requirements in smart grid into consideration.
文摘The widely adoption of Electric Vehicle (EV) has been identified as a major challenge for future development of smart grids. The ever increasing electric vehicle charging further increases the energy demand. This paper reports the development of an Advanced Metering Infrastructure (AMI) as an effective tool to reshape the load profile of EV charging by adopting appropriate demand side management strategy. This paper presents a total solution for EV charging service platform (EVAMI) based on power line and internet communication. It must be stressed that the development of Third Party Customer Service Platform in this investigation facilitates a single bill to be issued to EV owners. Hence, EV owners understand their energy usage and thus may perform energy saving activity efficiently. Experiment and evaluation of the proposed system show that the throughput achieved is about 5 Mbps at 10 ms end to end delay in Power line Communication. By introducing two dimensional dynamic pricing and charging schedule, the proposed EVAMI successfully reduces 36% peak consumption and increases the “off peak” consumption by 54%. Therefore the EVAMI does not only reduce the peak consumption but also relocates the energy demand effectively.
文摘Advanced Metering Infrastructure(AMI)is the metering network of the smart grid that enables bidirectional communications between each consumer’s premises and the provider’s control center.The massive amount of data collected supports the real-time decision-making required for diverse applications.The communication infrastructure relies on different network types,including the Internet.This makes the infrastructure vulnerable to various attacks,which could compromise security or have devastating effects.However,traditional machine learning solutions cannot adapt to the increasing complexity and diversity of attacks.The objective of this paper is to develop an Anomaly Detection System(ADS)based on deep learning using the CIC-IDS2017 dataset.However,this dataset is highly imbalanced;thus,a two-step sampling technique:random under-sampling and the Synthetic Minority Oversampling Technique(SMOTE),is proposed to balance the dataset.The proposed system utilizes a multiple hidden layer Auto-encoder(AE)for feature extraction and dimensional reduction.In addition,an ensemble voting based on both Random Forest(RF)and Convolu-tional Neural Network(CNN)is developed to classify the multiclass attack cate-gories.The proposed system is evaluated and compared with six different state-of-the-art machine learning and deep learning algorithms:Random Forest(RF),Light Gradient Boosting Machine(LightGBM),eXtreme Gradient Boosting(XGboost),Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and bidirectional LSTM(biLSTM).Experimental results show that the proposed model enhances the detection for each attack class compared with the other machine learning and deep learning models with overall accuracy(98.29%),precision(99%),recall(98%),F_(1) score(98%),and the UNDetection rate(UND)(8%).
基金supported in part by the National Natural Science Foundation of China(No.51807013)the Foundation of Hunan Educational Committee(No.18B137)+1 种基金the Research Project in Hunan Province Education Department(No.21C0577)Postgraduate Research and Innovation Project of Hunan Province,China(No.CX20210791)。
文摘An advanced metering infrastructure(AMI)system plays a key role in the smart grid(SG),but it is vulnerable to cyberattacks.Current detection methods for AMI cyberattacks mainly focus on the data center or a distributed independent node.On one hand,it is difficult to train an excellent detection intrusion model on a self-learning independent node.On the other hand,large amounts of data are shared over the network and uploaded to a central node for training.These processes may compromise data privacy,cause communication delay,and incur high communication costs.With these limitations,we propose an intrusion detection method for AMI system based on federated learning(FL).The intrusion detection system is deployed in the data concentrators for training,and only its model parameters are communicated to the data center.Furthermore,the data center distributes the learning to each data concentrator through aggregation and weight assignments for collaborative learning.An optimized deep neural network(DNN)is exploited for this proposed method,and extensive experiments based on the NSL-KDD dataset are carried out.From the results,this proposed method improves detection performance and reduces computation costs,communication delays,and communication overheads while guaranteeing data privacy.
基金supported by China Scholarship Councilthe National Natural Science Foundation of China (Nos. 61170261 and 61202369)NSERC,Canada
文摘With the proliferation of smart grid research, the Advanced Metering Infrastructure （AMI） has become the first ubiquitous and fixed computing platform. However, due to the unique characteristics of AMI, such as complex network structure, resource-constrained smart meter, and privacy-sensitive data, it is an especially challenging issue to make AMI secure. Energy theft is one of the most important concerns related to the smart grid implementation. It is estimated that utility companies lose more than S25 billion every year due to energy theft around the world. To address this challenge, in this paper, we discuss the background of AMI and identify major security requirements that AMI should meet. Specifically, an attack tree based threat model is first presented to illustrate the energy-theft behaviors in AMI. Then, we summarize the current AMI energy-theft detection schemes into three categories, i.e., classification-based, state estimation-based, and game theory-based ones, and make extensive comparisons and discussions on them. In order to provide a deep understanding of security vulnerabilities and solutions in AMI and shed light on future research directions, we also explore some open challenges and potential solutions for energy-theft detection.
基金supported by project#EFRG18-GER-CEN-10 from the American University of Sharjah。
文摘The use of unmanned aerial vehicles(UAVs) in the collection of data from wireless devices, sensor nodes, and the Internet of Things(IoT) devices has recently received significant attention. In this paper, we investigate the data collection process from a set of smart meters in advanced metering infrastructure(AMI) enabled by UAVs. The objective is to minimize the total annual cost of the electric utility by jointly optimizing the number of UAVs, their power source sizing, the charging locations as well as the data collection trip planning. This is achieved while considering the energy budgets of batteries of UAVs and the required amount of collected data. The problem is formulated as a mixed-integer nonlinear programming(MINLP), which is decoupled into two sub-problems where a candidate UAV and a number of buildings are first grouped into trips via genetic algorithms(GAs), and then the optimum trip path is found using a traveling salesman problem(TSP) branch and bound algorithm. Simulation results show that the battery capacity or the number of UAVs increases as the coverage area or the density increases.
文摘针对目前配电线路参数缺少精准辨识的难题,提出了基于高级量测体系(advanced meter infrastructure,AMI)量测信息的低压配电网线路参数辨识方法。该方法基于AMI量测数据,从网络末节点开始对整个网络的支路参数进行分步估计,建立关于低压配电线路阻抗参数的非线性方程组,并采用最小二乘法对其进行求解,实现全网可观测线路的辨识和估计。算例证明了方法的可行性与有效性。
