Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909689
Carlo Corinaldesi, A. Fleischhacker, L. Lang, J. Radl, D. Schwabeneder, G. Lettner
The amount of distributed generation and consumption is growing worldwide and consequently also the need to increase the hosting capacity of distribution systems. To achieve this, possible measures include building new lines, enhancing the grid infrastructure, and providing higher efficiency of the existing grid. Several projects propose to include the control of power generation and Demand-Side-Management, like solar photovoltaic power curtailment and managing flexible demand, as a measure to increase the hosting capacity of the grid. The use of automated demand response through Information and Communications Technologies seems to be a promising solution, which can be rapidly implemented cost-effectively in distribution systems. Automated Demand Response includes a set of activities aimed to reduce or shift electricity use to improve the grid operation. Generators and consumers may be encouraged by the right incentives to activate their flexibilities and consequently increase the hosting capacity of the electricity distribution systems. For this reason, smart grid technologies are expected to play an essential role in the development of future power systems. In this paper, we present the results of different European case studies, which investigated the impact of market-driven automated flexibility management in smart grids. The findings indicate that automated Demand Response can contribute to distribution system stability and increase the renewable energy share, but also requires high computational power and complex coordination architectures.
{"title":"European Case Studies for Impact of Market-driven Flexibility Management in Distribution Systems","authors":"Carlo Corinaldesi, A. Fleischhacker, L. Lang, J. Radl, D. Schwabeneder, G. Lettner","doi":"10.1109/SmartGridComm.2019.8909689","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909689","url":null,"abstract":"The amount of distributed generation and consumption is growing worldwide and consequently also the need to increase the hosting capacity of distribution systems. To achieve this, possible measures include building new lines, enhancing the grid infrastructure, and providing higher efficiency of the existing grid. Several projects propose to include the control of power generation and Demand-Side-Management, like solar photovoltaic power curtailment and managing flexible demand, as a measure to increase the hosting capacity of the grid. The use of automated demand response through Information and Communications Technologies seems to be a promising solution, which can be rapidly implemented cost-effectively in distribution systems. Automated Demand Response includes a set of activities aimed to reduce or shift electricity use to improve the grid operation. Generators and consumers may be encouraged by the right incentives to activate their flexibilities and consequently increase the hosting capacity of the electricity distribution systems. For this reason, smart grid technologies are expected to play an essential role in the development of future power systems. In this paper, we present the results of different European case studies, which investigated the impact of market-driven automated flexibility management in smart grids. The findings indicate that automated Demand Response can contribute to distribution system stability and increase the renewable energy share, but also requires high computational power and complex coordination architectures.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131737687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909715
Georgios Kornaros, Dimitrios Bakoyiannis, Othon Tomoutzoglou, M. Coppola, G. Gherardi
As the cybersecurity risks in automotives grow due to vehicles increasing connectivity, enhancing the cybersecurity cognition of automotive networks has become an urgent requirement in industry. While modern microcontrollers offer ARM’s based Trustzone feature to secure critical applications, secure intra-vehicular protocols are required to design next generation vehicles. With standard security solutions not being practical to vehicles due to resource constraints and compatibility issues, in this work we present a lightweight technique to enable both a virtual trusted channel and a normal untrusted channel over the same physical CAN-bus network, which we call TrustNet. The goal of TrustNet is to secure CAN-bus sensitive communications by protecting against masquerade and replay attacks with minimum overhead and full legacy support, and at the same time to provide normal-world communications.
{"title":"TrustNet: Ensuring Normal-world and Trusted-world CAN-bus Networking","authors":"Georgios Kornaros, Dimitrios Bakoyiannis, Othon Tomoutzoglou, M. Coppola, G. Gherardi","doi":"10.1109/SmartGridComm.2019.8909715","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909715","url":null,"abstract":"As the cybersecurity risks in automotives grow due to vehicles increasing connectivity, enhancing the cybersecurity cognition of automotive networks has become an urgent requirement in industry. While modern microcontrollers offer ARM’s based Trustzone feature to secure critical applications, secure intra-vehicular protocols are required to design next generation vehicles. With standard security solutions not being practical to vehicles due to resource constraints and compatibility issues, in this work we present a lightweight technique to enable both a virtual trusted channel and a normal untrusted channel over the same physical CAN-bus network, which we call TrustNet. The goal of TrustNet is to secure CAN-bus sensitive communications by protecting against masquerade and replay attacks with minimum overhead and full legacy support, and at the same time to provide normal-world communications.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131743131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909814
A. Sahu, Hao Huang, K. Davis, S. Zonouz
Automatic optimal response systems are essential for preserving power system resilience and ensuring faster recovery from emergency under cyber compromise. Numerous research works have developed such response engine for cyber and physical system recovery separately. In this paper, we propose a novel cyber-physical decision support system, SCORE, that computes optimal actions considering pure and hybrid cyber-physical states, using Markov Decision Process (MDP). Such an automatic decision making engine can assist power system operators and network administrators to make a faster response to prevent cascading failures and attack escalation respectively. The hybrid nature of the engine makes the reward and state transition model of the MDP unique. Value iteration and policy iteration techniques are used to compute the optimal actions. Tests are performed on three and five substation power systems to recover from attacks that compromise relays to cause transmission line overflow. The paper also analyses the impact of reward and state transition model on computation. Corresponding results verify the efficacy of the proposed engine.
{"title":"SCORE: A Security-Oriented Cyber-Physical Optimal Response Engine","authors":"A. Sahu, Hao Huang, K. Davis, S. Zonouz","doi":"10.1109/SmartGridComm.2019.8909814","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909814","url":null,"abstract":"Automatic optimal response systems are essential for preserving power system resilience and ensuring faster recovery from emergency under cyber compromise. Numerous research works have developed such response engine for cyber and physical system recovery separately. In this paper, we propose a novel cyber-physical decision support system, SCORE, that computes optimal actions considering pure and hybrid cyber-physical states, using Markov Decision Process (MDP). Such an automatic decision making engine can assist power system operators and network administrators to make a faster response to prevent cascading failures and attack escalation respectively. The hybrid nature of the engine makes the reward and state transition model of the MDP unique. Value iteration and policy iteration techniques are used to compute the optimal actions. Tests are performed on three and five substation power systems to recover from attacks that compromise relays to cause transmission line overflow. The paper also analyses the impact of reward and state transition model on computation. Corresponding results verify the efficacy of the proposed engine.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121608531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-06DOI: 10.1109/SmartGridComm.2019.8909767
S. Lakshminarayana, E. Belmega, H. Poor
This work proposes a moving target defense (MTD) strategy to detect coordinated cyber-physical attacks (CCPAs) against power grids. A CCPA consists of a physical attack, such as disconnecting a transmission line, followed by a coordinated cyber attack that injects false data into the sensor measurements to mask the effects of the physical attack. Such attacks can lead to undetectable line outages and cause significant damage to the grid. The main idea of the proposed approach is to invalidate the knowledge that the attackers use to mask the effects of the physical attack by actively perturbing the grid’s transmission line reactances using distributed flexible AC transmission system (D-FACTS) devices. We identify the MTD design criteria in this context to thwart CCPAs. The proposed MTD design consists of two parts. First, we identify the subset of links for D-FACTS device deployment that enables the defender to detect CCPAs against any link in the system. Then, in order to minimize the defense cost during the system’s operational time, we use a game-theoretic approach to identify the best subset of links (within the D-FACTS deployment set) to perturb which will provide adequate protection. Extensive simulations performed using the MATPOWER simulator on IEEE bus systems verify the effectiveness of our approach in detecting CCPAs and reducing the operator’s defense cost.
{"title":"Moving-Target Defense for Detecting Coordinated Cyber-Physical Attacks in Power Grids","authors":"S. Lakshminarayana, E. Belmega, H. Poor","doi":"10.1109/SmartGridComm.2019.8909767","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909767","url":null,"abstract":"This work proposes a moving target defense (MTD) strategy to detect coordinated cyber-physical attacks (CCPAs) against power grids. A CCPA consists of a physical attack, such as disconnecting a transmission line, followed by a coordinated cyber attack that injects false data into the sensor measurements to mask the effects of the physical attack. Such attacks can lead to undetectable line outages and cause significant damage to the grid. The main idea of the proposed approach is to invalidate the knowledge that the attackers use to mask the effects of the physical attack by actively perturbing the grid’s transmission line reactances using distributed flexible AC transmission system (D-FACTS) devices. We identify the MTD design criteria in this context to thwart CCPAs. The proposed MTD design consists of two parts. First, we identify the subset of links for D-FACTS device deployment that enables the defender to detect CCPAs against any link in the system. Then, in order to minimize the defense cost during the system’s operational time, we use a game-theoretic approach to identify the best subset of links (within the D-FACTS deployment set) to perturb which will provide adequate protection. Extensive simulations performed using the MATPOWER simulator on IEEE bus systems verify the effectiveness of our approach in detecting CCPAs and reducing the operator’s defense cost.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130332567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-21DOI: 10.1109/SmartGridComm.2019.8909703
Yutao Jiao, Ping Wang, D. Niyato, Jun Zhao, B. Lin, Dong In Kim
Wireless power transfer (WPT) is a promising technology to prolong the lifetime of sensor and communication devices, i.e., workers, in completing crowdsourcing tasks by providing continuous and cost-effective energy supplies. In this paper, we propose a wireless powered spatial crowdsourcing (SC) framework which consists of two mutual dependent phases: task allocation phase and data crowdsourcing phase. In the task allocation phase, we propose a Stackelberg game based mechanism for the SC platform to efficiently allocate spatial tasks and wireless charging power to each worker. In the data crowdsourcing phase, the workers may have an incentive to misreport its real working location to improve its own utility, which manipulates the SC platform. To address this issue, we present a strategyproof deployment mechanism for the SC platform to deploy its mobile base station. We apply the Moulin’s generalized median mechanism and analyze the worst-case performance in maximizing the SC platform’s utility. Finally, numerical experiments reveal the effectiveness of the proposed framework in allocating tasks and charging power to workers while avoiding the dishonest worker’s manipulation.
{"title":"Task Allocation and Mobile Base Station Deployment in Wireless Powered Spatial Crowdsourcing","authors":"Yutao Jiao, Ping Wang, D. Niyato, Jun Zhao, B. Lin, Dong In Kim","doi":"10.1109/SmartGridComm.2019.8909703","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909703","url":null,"abstract":"Wireless power transfer (WPT) is a promising technology to prolong the lifetime of sensor and communication devices, i.e., workers, in completing crowdsourcing tasks by providing continuous and cost-effective energy supplies. In this paper, we propose a wireless powered spatial crowdsourcing (SC) framework which consists of two mutual dependent phases: task allocation phase and data crowdsourcing phase. In the task allocation phase, we propose a Stackelberg game based mechanism for the SC platform to efficiently allocate spatial tasks and wireless charging power to each worker. In the data crowdsourcing phase, the workers may have an incentive to misreport its real working location to improve its own utility, which manipulates the SC platform. To address this issue, we present a strategyproof deployment mechanism for the SC platform to deploy its mobile base station. We apply the Moulin’s generalized median mechanism and analyze the worst-case performance in maximizing the SC platform’s utility. Finally, numerical experiments reveal the effectiveness of the proposed framework in allocating tasks and charging power to workers while avoiding the dishonest worker’s manipulation.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116074794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-15DOI: 10.1109/SmartGridComm.2019.8909765
Zachary J. Lee, D. Johansson, S. Low
Smart electric vehicle charging has recently garnered significant attention in the research community due to need to charge vast numbers of electric vehicles (EVs) economically, as well as the potential of providing grid services using EVs. However, research into practical online charging algorithms has been hampered by the lack of a widely available, realistic simulation environment in which to evaluate algorithms and test assumptions. To meet this need, we have developed ACN-Sim, a data-driven, open-source simulator based on our experience building and operating real-world charging systems. ACN-Sim provides a modular, extensible architecture which models the complexity of real charging systems, including battery charging behavior and unbalanced three-phase infrastructure. In addition, ACN-Sim integrates with a broader ecosystem of research tools for EV charging, including ACN-Data, an open dataset of EV charging sessions to provide realistic simulation scenarios, and ACN-Live, a framework for field-testing charging algorithms.
{"title":"ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research","authors":"Zachary J. Lee, D. Johansson, S. Low","doi":"10.1109/SmartGridComm.2019.8909765","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909765","url":null,"abstract":"Smart electric vehicle charging has recently garnered significant attention in the research community due to need to charge vast numbers of electric vehicles (EVs) economically, as well as the potential of providing grid services using EVs. However, research into practical online charging algorithms has been hampered by the lack of a widely available, realistic simulation environment in which to evaluate algorithms and test assumptions. To meet this need, we have developed ACN-Sim, a data-driven, open-source simulator based on our experience building and operating real-world charging systems. ACN-Sim provides a modular, extensible architecture which models the complexity of real charging systems, including battery charging behavior and unbalanced three-phase infrastructure. In addition, ACN-Sim integrates with a broader ecosystem of research tools for EV charging, including ACN-Data, an open dataset of EV charging sessions to provide realistic simulation scenarios, and ACN-Live, a framework for field-testing charging algorithms.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132101024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-11DOI: 10.1109/SmartGridComm.2019.8909795
Xiang Pan, Tianyu Zhao, Minghua Chen
We develop DeepOPF as a Deep Neural Network (DNN) based approach for solving direct current optimal power flow (DC-OPF) problems. DeepOPF is inspired by the observation that solving DC-OPF for a given power network is equivalent to characterizing a high-dimensional mapping between the load inputs and the dispatch and transmission decisions. We construct and train a DNN model to learn such mapping, then we apply it to obtain optimized operating decisions upon arbitrary load inputs. We adopt uniform sampling to address the over-fitting problem common in generic DNN approaches. We leverage on a useful structure in DC-OPF to significantly reduce the mapping dimension, subsequently cutting down the size of our DNN model and the amount of training data/time needed. We also design a post-processing procedure to ensure the feasibility of the obtained solution. Simulation results of IEEE test cases show that DeepOPF always generates feasible solutions with negligible optimality loss, while speeding up the computing time by two orders of magnitude as compared to conventional approaches implemented in a state-of-the-art solver.
{"title":"DeepOPF: Deep Neural Network for DC Optimal Power Flow","authors":"Xiang Pan, Tianyu Zhao, Minghua Chen","doi":"10.1109/SmartGridComm.2019.8909795","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909795","url":null,"abstract":"We develop DeepOPF as a Deep Neural Network (DNN) based approach for solving direct current optimal power flow (DC-OPF) problems. DeepOPF is inspired by the observation that solving DC-OPF for a given power network is equivalent to characterizing a high-dimensional mapping between the load inputs and the dispatch and transmission decisions. We construct and train a DNN model to learn such mapping, then we apply it to obtain optimized operating decisions upon arbitrary load inputs. We adopt uniform sampling to address the over-fitting problem common in generic DNN approaches. We leverage on a useful structure in DC-OPF to significantly reduce the mapping dimension, subsequently cutting down the size of our DNN model and the amount of training data/time needed. We also design a post-processing procedure to ensure the feasibility of the obtained solution. Simulation results of IEEE test cases show that DeepOPF always generates feasible solutions with negligible optimality loss, while speeding up the computing time by two orders of magnitude as compared to conventional approaches implemented in a state-of-the-art solver.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128452837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-08DOI: 10.1109/SmartGridComm.2019.8909791
M. Pilz, Luluwah Al-Fagih
Global warming is endangering the earth’s ecosystem. It is imperative for us to limit green house gas emissions in order to combat rising global average temperatures. One way to move forward is the integration of renewable energy resources on all levels of the power system, i.e. from large-scale energy producers to individual households. The future smart grid provides the technology for this. In this paper, a novel demand-side management concept is proposed. It is implemented by a utility company which focuses on renewable energy. Through a specific billing mechanism, prosumers are encouraged to balance load and supply. A game-theoretic approach models households as self-determined rational energy users, that want to reduce their individual electricity costs. To achieve this, they selfishly share energy with their neighbours and also schedule their energy storage systems. The scheme is designed such that monetary transactions between households are not necessary. Thus, it provides an alternative approach to energy trading schemes from the literature.
{"title":"Selfish Energy Sharing in Prosumer Communities: A Demand-Side Management Concept","authors":"M. Pilz, Luluwah Al-Fagih","doi":"10.1109/SmartGridComm.2019.8909791","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909791","url":null,"abstract":"Global warming is endangering the earth’s ecosystem. It is imperative for us to limit green house gas emissions in order to combat rising global average temperatures. One way to move forward is the integration of renewable energy resources on all levels of the power system, i.e. from large-scale energy producers to individual households. The future smart grid provides the technology for this. In this paper, a novel demand-side management concept is proposed. It is implemented by a utility company which focuses on renewable energy. Through a specific billing mechanism, prosumers are encouraged to balance load and supply. A game-theoretic approach models households as self-determined rational energy users, that want to reduce their individual electricity costs. To achieve this, they selfishly share energy with their neighbours and also schedule their energy storage systems. The scheme is designed such that monetary transactions between households are not necessary. Thus, it provides an alternative approach to energy trading schemes from the literature.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127770911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-06DOI: 10.1109/SmartGridComm.2019.8909739
Zhigang Chu, Andrea Pinceti, R. Biswas, O. Kosut, A. Pal, L. Sankar
Intelligently designed false data injection (FDI) attacks have been shown to be able to bypass the χ2-test based bad data detector (BDD), resulting in physical consequences (such as line overloads) in the power system. In this paper, using synthetic PMU measurements and intelligently designed FDI attacks, it is shown that if an attack is suddenly injected into the system, a predictive filter with sufficient accuracy is able to detect it. However, an attacker can gradually increase the magnitude of the attack to avoid detection, and still cause damage to the system.
{"title":"Can Predictive Filters Detect Gradually Ramping False Data Injection Attacks Against PMUs?","authors":"Zhigang Chu, Andrea Pinceti, R. Biswas, O. Kosut, A. Pal, L. Sankar","doi":"10.1109/SmartGridComm.2019.8909739","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909739","url":null,"abstract":"Intelligently designed false data injection (FDI) attacks have been shown to be able to bypass the χ2-test based bad data detector (BDD), resulting in physical consequences (such as line overloads) in the power system. In this paper, using synthetic PMU measurements and intelligently designed FDI attacks, it is shown that if an attack is suddenly injected into the system, a predictive filter with sufficient accuracy is able to detect it. However, an attacker can gradually increase the magnitude of the attack to avoid detection, and still cause damage to the system.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130629271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-06DOI: 10.1109/SmartGridComm.2019.8909815
Joshua Comden, Marcello Colombino, A. Bernstein, Zhenhua Liu
In this paper, we propose an analytical framework to quantify the amount of data samples needed to obtain accurate state estimation in a power system – a problem known as sample complexity analysis in computer science. Motivated by the increasing adoption of distributed energy resources into the distribution-level grids, it becomes imperative to estimate the state of distribution grids in order to ensure stable operation. Traditional power system state estimation techniques mainly focus on the transmission network which involve solving an overdetermined system and eliminating bad data. However, distribution networks are typically underdetermined due to the large number of connection points and high cost of pervasive installation of measurement devices. In this paper, we consider the recently proposed state-estimation method for underdetermined systems that is based on matrix completion. In particular, a constrained matrix completion algorithm was proposed, wherein the standard matrix completion problem is augmented with additional equality constraints representing the physics (namely power-flow constraints). We analyze the sample complexity of this general method by proving an upper bound on the sample complexity that depends directly on the properties of these constraints that can lower number of needed samples as compared to the unconstrained problem. To demonstrate the improvement that the constraints add to state estimation, we test the method on a 141-bus distribution network case study and compare it to the traditional least squares minimization state estimation method.
{"title":"Sample Complexity of Power System State Estimation using Matrix Completion","authors":"Joshua Comden, Marcello Colombino, A. Bernstein, Zhenhua Liu","doi":"10.1109/SmartGridComm.2019.8909815","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909815","url":null,"abstract":"In this paper, we propose an analytical framework to quantify the amount of data samples needed to obtain accurate state estimation in a power system – a problem known as sample complexity analysis in computer science. Motivated by the increasing adoption of distributed energy resources into the distribution-level grids, it becomes imperative to estimate the state of distribution grids in order to ensure stable operation. Traditional power system state estimation techniques mainly focus on the transmission network which involve solving an overdetermined system and eliminating bad data. However, distribution networks are typically underdetermined due to the large number of connection points and high cost of pervasive installation of measurement devices. In this paper, we consider the recently proposed state-estimation method for underdetermined systems that is based on matrix completion. In particular, a constrained matrix completion algorithm was proposed, wherein the standard matrix completion problem is augmented with additional equality constraints representing the physics (namely power-flow constraints). We analyze the sample complexity of this general method by proving an upper bound on the sample complexity that depends directly on the properties of these constraints that can lower number of needed samples as compared to the unconstrained problem. To demonstrate the improvement that the constraints add to state estimation, we test the method on a 141-bus distribution network case study and compare it to the traditional least squares minimization state estimation method.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121398444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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