Cascading failure can cause great damage to complex networks, so it is of great significance to improve the network robustness against cascading failure. Many previous existing works on load-redistribution strategies ...Cascading failure can cause great damage to complex networks, so it is of great significance to improve the network robustness against cascading failure. Many previous existing works on load-redistribution strategies require global information, which is not suitable for large scale networks, and some strategies based on local information assume that the load of a node is always its initial load before the network is attacked, and the load of the failure node is redistributed to its neighbors according to their initial load or initial residual capacity. This paper proposes a new load-redistribution strategy based on local information considering an ever-changing load. It redistributes the loads of the failure node to its nearest neighbors according to their current residual capacity, which makes full use of the residual capacity of the network. Experiments are conducted on two typical networks and two real networks, and the experimental results show that the new load-redistribution strategy can reduce the size of cascading failure efficiently.展开更多
A nearest-neighbor-based detector against load redistribution attacks is presented.The detector is designed to scale from small-scale to very large-scale systems while guaranteeing consistent detection performance.Ext...A nearest-neighbor-based detector against load redistribution attacks is presented.The detector is designed to scale from small-scale to very large-scale systems while guaranteeing consistent detection performance.Extensive testing is performed on a realistic large-scale system to evaluate the perfor-mance of the proposed detector against a wide range of attacks,from simple random noise attacks to sophisticated load redistribution attacks.The detection capability is analyzed against different attack parameters to evaluate its sensitivity.A statistical test that leverages the proposed detector is introduced to identify which loads are likely to have been maliciously modified,thus,localizing the attack subgraph.This test is based on ascribing to each load a risk measure(probability of being attacked)and then computing the best posterior likelihood that minimizes log-loss.展开更多
The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastr...The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastructure,the cyber vulnerability of load meters has attracted the adversary’s great attention.In this paper,we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements.By taking advantage of the changing properties of real-world load profile,we propose a deeply hidden load data attack(i.e.,DH-LDA)that can evade bad data detection,clustering-based detection,and price anomaly detection.The main contributions of this work are as follows:(i)We design a stealthy attack framework that exploits historical load patterns to generate load data with minimal statistical deviation from normalmeasurements,thereby maximizing concealment;(ii)We identify the optimal time window for data injection to ensure that the altered nodal prices follow natural fluctuations,enhancing the undetectability of the attack in real-time market operations;(iii)We develop a resilience evaluation metric and formulate an optimization-based approach to quantify the electricity market’s robustness against DH-LDAs.Our experiments show that the adversary can gain profits from the electricity market while remaining undetected.展开更多
In complex network systems,load redistribution strategy has become a key means to ensure stable system operation.In practical application,load redistribution strategy needs to comprehensively consider several factors,...In complex network systems,load redistribution strategy has become a key means to ensure stable system operation.In practical application,load redistribution strategy needs to comprehensively consider several factors,such as node degree,node residual capacity,and shortest path length.To address this issue,this paper proposes a local load redistribution strategy based on entropy weight TOPSIS method.The entropy weight TOPSIS method combines the advantages of entropy weight method and TOPSIS method,and determines the weights of each index by calculating its entropy value,which can objectively reflect the importance of each index in decision-making and avoid the interference of subjective factors.Meanwhile,the TOPSIS method evaluates the advantages and disadvantages of each node by comparing the distance of each node from the ideal solution and the negative ideal solution,so as to determine the more suitable node as the assignable node.In this paper,the effectiveness of local load redistribution strategy based on entropy weight TOPSIS method is verified on BA scale-free network,WS small-world network,and WS-BA composite network,and the results show that the assignable nodes of the load redistribution strategy based on node’s maximum residual capacity and entropy weight TOPSIS have not only higher values of residual capacity,but also their node degree value and shortest path length value,which indicates that the load reallocation strategy is more integrated in selecting the assignable nodes.展开更多
In an era where power systems face increased cyber threats,social media data,especially public sentiment during outages,emerges as a crucial component for devising defense strategies.We present a methodology that inte...In an era where power systems face increased cyber threats,social media data,especially public sentiment during outages,emerges as a crucial component for devising defense strategies.We present a methodology that integrates sentiment analysis of social media data with advanced reinforcement learning techniques to tackle uncertain load redistribution cyberattacks.This approach first employs VADER and Support Vector Machine(SVM)sentiment analysis on collected social media data,revealing insightful information about power outages and public sentiment.Proximal Policy Optimization(PPO),a state-of-the-art reinforcement learning method,is then applied in the second stage to leverage these insights,manage outage uncertainty,and optimize defense strategies.The efficacy of this methodology is demonstrated on a modified IEEE 6-bus system.The results underscore our approach's effectiveness in utilizing social media data for a nuanced,targeted response to cyberattacks.This pioneering methodology offers a promising direction for enhancing power grid resilience against cyberattacks and natural disasters,highlighting the value of social media sentiment analysis in power systems security.展开更多
The safety analysis of reinforced concrete buildings during construction should be based on the comprehensive understanding of loads, load effects, structural resistance, and available safety index of the structure. T...The safety analysis of reinforced concrete buildings during construction should be based on the comprehensive understanding of loads, load effects, structural resistance, and available safety index of the structure. This paper analyzes the characteristics and probabilistic models of resistance, loads, and load effects. A method was developed to calculate the probability of failure based on Monte Carlo simulation and models proposed in previous articles. Construction examples were used to analyze the influence of live load on the probability of failure. The results show that when the live load increases, the maximum probability of failure increases with acceleration. The results suggest that the construction live load should be carefully addressed during construction.展开更多
基金Project supported by the National Basic Research Program of China(Grant No.2013CB328903)the Special Fund of 2011 Internet of Things Development of Ministry of Industry and Information Technology,China(Grant No.2011BAJ03B13-2)+1 种基金the National Natural Science Foundation of China(Grant No.61473050)the Key Science and Technology Program of Chongqing,China(Grant No.cstc2012gg-yyjs40008)
文摘Cascading failure can cause great damage to complex networks, so it is of great significance to improve the network robustness against cascading failure. Many previous existing works on load-redistribution strategies require global information, which is not suitable for large scale networks, and some strategies based on local information assume that the load of a node is always its initial load before the network is attacked, and the load of the failure node is redistributed to its neighbors according to their initial load or initial residual capacity. This paper proposes a new load-redistribution strategy based on local information considering an ever-changing load. It redistributes the loads of the failure node to its nearest neighbors according to their current residual capacity, which makes full use of the residual capacity of the network. Experiments are conducted on two typical networks and two real networks, and the experimental results show that the new load-redistribution strategy can reduce the size of cascading failure efficiently.
基金the National Science Foundation(No.CNS-1449080,No.OAC-1934766)the Power System Engineering Research Center(PSERC)under projects S-72 and S-87。
文摘A nearest-neighbor-based detector against load redistribution attacks is presented.The detector is designed to scale from small-scale to very large-scale systems while guaranteeing consistent detection performance.Extensive testing is performed on a realistic large-scale system to evaluate the perfor-mance of the proposed detector against a wide range of attacks,from simple random noise attacks to sophisticated load redistribution attacks.The detection capability is analyzed against different attack parameters to evaluate its sensitivity.A statistical test that leverages the proposed detector is introduced to identify which loads are likely to have been maliciously modified,thus,localizing the attack subgraph.This test is based on ascribing to each load a risk measure(probability of being attacked)and then computing the best posterior likelihood that minimizes log-loss.
基金supported by the project Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastructure,the cyber vulnerability of load meters has attracted the adversary’s great attention.In this paper,we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements.By taking advantage of the changing properties of real-world load profile,we propose a deeply hidden load data attack(i.e.,DH-LDA)that can evade bad data detection,clustering-based detection,and price anomaly detection.The main contributions of this work are as follows:(i)We design a stealthy attack framework that exploits historical load patterns to generate load data with minimal statistical deviation from normalmeasurements,thereby maximizing concealment;(ii)We identify the optimal time window for data injection to ensure that the altered nodal prices follow natural fluctuations,enhancing the undetectability of the attack in real-time market operations;(iii)We develop a resilience evaluation metric and formulate an optimization-based approach to quantify the electricity market’s robustness against DH-LDAs.Our experiments show that the adversary can gain profits from the electricity market while remaining undetected.
文摘In complex network systems,load redistribution strategy has become a key means to ensure stable system operation.In practical application,load redistribution strategy needs to comprehensively consider several factors,such as node degree,node residual capacity,and shortest path length.To address this issue,this paper proposes a local load redistribution strategy based on entropy weight TOPSIS method.The entropy weight TOPSIS method combines the advantages of entropy weight method and TOPSIS method,and determines the weights of each index by calculating its entropy value,which can objectively reflect the importance of each index in decision-making and avoid the interference of subjective factors.Meanwhile,the TOPSIS method evaluates the advantages and disadvantages of each node by comparing the distance of each node from the ideal solution and the negative ideal solution,so as to determine the more suitable node as the assignable node.In this paper,the effectiveness of local load redistribution strategy based on entropy weight TOPSIS method is verified on BA scale-free network,WS small-world network,and WS-BA composite network,and the results show that the assignable nodes of the load redistribution strategy based on node’s maximum residual capacity and entropy weight TOPSIS have not only higher values of residual capacity,but also their node degree value and shortest path length value,which indicates that the load reallocation strategy is more integrated in selecting the assignable nodes.
基金Supported by the National Natural Science Foundation of China(72293575,71974187)。
文摘In an era where power systems face increased cyber threats,social media data,especially public sentiment during outages,emerges as a crucial component for devising defense strategies.We present a methodology that integrates sentiment analysis of social media data with advanced reinforcement learning techniques to tackle uncertain load redistribution cyberattacks.This approach first employs VADER and Support Vector Machine(SVM)sentiment analysis on collected social media data,revealing insightful information about power outages and public sentiment.Proximal Policy Optimization(PPO),a state-of-the-art reinforcement learning method,is then applied in the second stage to leverage these insights,manage outage uncertainty,and optimize defense strategies.The efficacy of this methodology is demonstrated on a modified IEEE 6-bus system.The results underscore our approach's effectiveness in utilizing social media data for a nuanced,targeted response to cyberattacks.This pioneering methodology offers a promising direction for enhancing power grid resilience against cyberattacks and natural disasters,highlighting the value of social media sentiment analysis in power systems security.
文摘The safety analysis of reinforced concrete buildings during construction should be based on the comprehensive understanding of loads, load effects, structural resistance, and available safety index of the structure. This paper analyzes the characteristics and probabilistic models of resistance, loads, and load effects. A method was developed to calculate the probability of failure based on Monte Carlo simulation and models proposed in previous articles. Construction examples were used to analyze the influence of live load on the probability of failure. The results show that when the live load increases, the maximum probability of failure increases with acceleration. The results suggest that the construction live load should be carefully addressed during construction.
