Pub Date : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632329
J. Maree, S. Gros, Venkatachalam Lakshmanan
A data-driven stochastic MPC strategy is presented as an EMS for the Skagerak Energilab microgrid. Uncertainties, introduced due to the intermittent nature of RES and load demands, are systematically incorporated into the MPC problem via adaptive chance-constraints. These chance-constraints promote admissible probabilistic operation of the microgrid within the stipulated SOC bounds of an ESS. For computational tractability, these chance-constraints are approximated by solving the inverse cumulative distribution function of a disturbance innovation sequence. This disturbance innovation sequence defines the difference between forecast and realized disturbances, and is sampled for a sliding window as disturbances are revealed over closed-loop operation. No a-prior assumptions are made on the distribution function of the disturbance innovation sequence; instead, solving the Maximum Spacings Estimation problem (off-line), we adapt some parametrized distribution function to fit this disturbance innovation sequence. The proposed strategy has computational complexity comparable to nominal deterministic MPC, promote the satisfaction of constraints in a probabilistic sense, and, decrease closed-loop operational costs by 26%.
{"title":"Low-complexity Risk-averse MPC for EMS","authors":"J. Maree, S. Gros, Venkatachalam Lakshmanan","doi":"10.1109/SmartGridComm51999.2021.9632329","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632329","url":null,"abstract":"A data-driven stochastic MPC strategy is presented as an EMS for the Skagerak Energilab microgrid. Uncertainties, introduced due to the intermittent nature of RES and load demands, are systematically incorporated into the MPC problem via adaptive chance-constraints. These chance-constraints promote admissible probabilistic operation of the microgrid within the stipulated SOC bounds of an ESS. For computational tractability, these chance-constraints are approximated by solving the inverse cumulative distribution function of a disturbance innovation sequence. This disturbance innovation sequence defines the difference between forecast and realized disturbances, and is sampled for a sliding window as disturbances are revealed over closed-loop operation. No a-prior assumptions are made on the distribution function of the disturbance innovation sequence; instead, solving the Maximum Spacings Estimation problem (off-line), we adapt some parametrized distribution function to fit this disturbance innovation sequence. The proposed strategy has computational complexity comparable to nominal deterministic MPC, promote the satisfaction of constraints in a probabilistic sense, and, decrease closed-loop operational costs by 26%.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"40 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113974795","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 : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632305
Shahram Ghahremani, Rajvir Sidhu, David K. Y. Yau, Ngai-Man Cheung, Justin Albrethsen
Time delay attacks pose a threat to power systems that conventional cybersecurity methods do not adequately address. Conventional methods analyze the contents of network packets to identify threats; this is not effective against time delay attacks, which do not alter packet contents. To detect and identify time delay attacks, a new method is needed. In this paper, a novel and data-driven deep learning (DL) approach is developed to detect time delay attacks on power systems and simultaneously identify both the time of attack and attack magnitude. While conventional DL networks struggle with multivariate long time series data generated by power systems, this can be improved using attention mechanisms. In this paper, dual attention mechanisms (DA) are used to focus and improve a gated recurrent unit (GRU) network for detecting and identifying time delay attacks. A comparative analysis shows the proposed GRU-DA approach outperforms conventional DL, machine learning (ML), and statistical methods while maintaining low model complexity.
{"title":"Defense against Power System Time Delay Attacks via Attention-based Multivariate Deep Learning","authors":"Shahram Ghahremani, Rajvir Sidhu, David K. Y. Yau, Ngai-Man Cheung, Justin Albrethsen","doi":"10.1109/SmartGridComm51999.2021.9632305","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632305","url":null,"abstract":"Time delay attacks pose a threat to power systems that conventional cybersecurity methods do not adequately address. Conventional methods analyze the contents of network packets to identify threats; this is not effective against time delay attacks, which do not alter packet contents. To detect and identify time delay attacks, a new method is needed. In this paper, a novel and data-driven deep learning (DL) approach is developed to detect time delay attacks on power systems and simultaneously identify both the time of attack and attack magnitude. While conventional DL networks struggle with multivariate long time series data generated by power systems, this can be improved using attention mechanisms. In this paper, dual attention mechanisms (DA) are used to focus and improve a gated recurrent unit (GRU) network for detecting and identifying time delay attacks. A comparative analysis shows the proposed GRU-DA approach outperforms conventional DL, machine learning (ML), and statistical methods while maintaining low model complexity.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132449162","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}
Moving Target Defense (MTD) is a new technology to defend against the false data injection attack (FDIA) on distribution system state estimation (DSSE). It works by proactively perturbing the branch reactance. However, due to the challenges induced by the nonlinear dynamics and the coupling phases in the three-phase AC DSSE model, the analysis on the effectiveness and hiddenness of MTD, which are two essential performance metrics, has not yet been conducted. In this paper, we attempt to optimize the effectiveness and hiddenness of MTD while considering voltage stability. Firstly, we quantify the two metrics with approximated measurement residuals. Based on the quantified metrics, we formulate an optimization problem to maximize the effectiveness with guaranteed hiddenness and ensure voltage stability by minimizing the voltage variation induced by MTD. The original problem is transformed to a polynomial optimization problem based on the observation that the alteration of the projection matrix caused by reactance perturbation is neglectable, such that the near-optimal result can be obtained. Finally, extensive simulations are conducted on the IEEE 13-bus test feeder to evaluate the performance of the proposed MTD.
{"title":"Analysis of Moving Target Defense in Unbalanced and Multiphase Distribution Systems Considering Voltage Stability","authors":"Mengxiang Liu, Chengcheng Zhao, Zhenyong Zhang, Ruilong Deng, Peng Cheng","doi":"10.1109/SmartGridComm51999.2021.9632320","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632320","url":null,"abstract":"Moving Target Defense (MTD) is a new technology to defend against the false data injection attack (FDIA) on distribution system state estimation (DSSE). It works by proactively perturbing the branch reactance. However, due to the challenges induced by the nonlinear dynamics and the coupling phases in the three-phase AC DSSE model, the analysis on the effectiveness and hiddenness of MTD, which are two essential performance metrics, has not yet been conducted. In this paper, we attempt to optimize the effectiveness and hiddenness of MTD while considering voltage stability. Firstly, we quantify the two metrics with approximated measurement residuals. Based on the quantified metrics, we formulate an optimization problem to maximize the effectiveness with guaranteed hiddenness and ensure voltage stability by minimizing the voltage variation induced by MTD. The original problem is transformed to a polynomial optimization problem based on the observation that the alteration of the projection matrix caused by reactance perturbation is neglectable, such that the near-optimal result can be obtained. Finally, extensive simulations are conducted on the IEEE 13-bus test feeder to evaluate the performance of the proposed MTD.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125839564","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 : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632330
Alejandro Tristán Jiménez, C. Kaymakci, A. Sauer
Industrial energy flexibility can play a pivotal supporting role in the transition towards renewable energy sources. Nonetheless, to harness the vast potential of industrial energy flexibility operation-friendly energy flexibility measures need to be identified and characterized. This work presents a step by step approach to mine historical energy consumption data of an industrial system using the k-means algorithm with support of the average silhouette score method to establish the system's typical operation profiles. These profiles can then be used not only to identify specific energy flexibility measures but their energy flexibility potential among other characterization parameters. The paper presents two representative use case examples and concludes by enumerating the benefits and providing an outlook of improvement opportunities for the developed approach.
{"title":"Mining the energy consumption data of industrial systems to identify and characterize energy flexibility capabilities","authors":"Alejandro Tristán Jiménez, C. Kaymakci, A. Sauer","doi":"10.1109/SmartGridComm51999.2021.9632330","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632330","url":null,"abstract":"Industrial energy flexibility can play a pivotal supporting role in the transition towards renewable energy sources. Nonetheless, to harness the vast potential of industrial energy flexibility operation-friendly energy flexibility measures need to be identified and characterized. This work presents a step by step approach to mine historical energy consumption data of an industrial system using the k-means algorithm with support of the average silhouette score method to establish the system's typical operation profiles. These profiles can then be used not only to identify specific energy flexibility measures but their energy flexibility potential among other characterization parameters. The paper presents two representative use case examples and concludes by enumerating the benefits and providing an outlook of improvement opportunities for the developed approach.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130400868","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 : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632300
A. Sanz, José Carlos Ibar, Luis Lacasa
This paper is motivated by the evolution of PLC systems to new hybrid systems combining PLC and RF. In order to achieve a good performance and optimization of the system, the development of a simulator that allows debugging it is crucial. A fundamental part of this evolution is the modelling of the RF channel adapted to the MAC level simulators we are using. This paper presents an introduction to this evolution of PLC systems to new hybrid systems and the results of RF channel field measurements along with models of signal propagation and FER based on them.
{"title":"PLC-RF hybrid communication systems, model and simulation","authors":"A. Sanz, José Carlos Ibar, Luis Lacasa","doi":"10.1109/SmartGridComm51999.2021.9632300","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632300","url":null,"abstract":"This paper is motivated by the evolution of PLC systems to new hybrid systems combining PLC and RF. In order to achieve a good performance and optimization of the system, the development of a simulator that allows debugging it is crucial. A fundamental part of this evolution is the modelling of the RF channel adapted to the MAC level simulators we are using. This paper presents an introduction to this evolution of PLC systems to new hybrid systems and the results of RF channel field measurements along with models of signal propagation and FER based on them.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128927047","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 : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9631998
S. Mohiuddin, Junjian Qi, Sasha Fung, Yu Huang, Yufei Tang
This paper presents a deep learning based multi-label attack detection approach for the distributed control in AC microgrids. The secondary control of AC microgrids is formulated as a constrained optimization problem with voltage and frequency as control variables which is then solved using a distributed primal-dual gradient algorithm. The normally distributed false data injection (FDI) attacks against the proposed distributed control are then designed for the distributed gener-ator's output voltage and active/reactive power measurements. In order to detect the presence of false measurements, a deep learning based attack detection strategy is further developed. The proposed attack detection is formulated as a multi-label classification problem to capture the inconsistency and co-occurrence dependencies in the power flow measurements due to the presence of FDI attacks. With this multi-label classification scheme, a single model is able to identify the presence of different attacks and load change simultaneously. Two different deep learning techniques are compared to design the attack detector, and the performance of the proposed distributed control and the attack detector is demonstrated through simulations on the modified IEEE 34-bus distribution test system.
{"title":"Deep Learning Based Multi-Label Attack Detection for Distributed Control of AC Microgrids","authors":"S. Mohiuddin, Junjian Qi, Sasha Fung, Yu Huang, Yufei Tang","doi":"10.1109/SmartGridComm51999.2021.9631998","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9631998","url":null,"abstract":"This paper presents a deep learning based multi-label attack detection approach for the distributed control in AC microgrids. The secondary control of AC microgrids is formulated as a constrained optimization problem with voltage and frequency as control variables which is then solved using a distributed primal-dual gradient algorithm. The normally distributed false data injection (FDI) attacks against the proposed distributed control are then designed for the distributed gener-ator's output voltage and active/reactive power measurements. In order to detect the presence of false measurements, a deep learning based attack detection strategy is further developed. The proposed attack detection is formulated as a multi-label classification problem to capture the inconsistency and co-occurrence dependencies in the power flow measurements due to the presence of FDI attacks. With this multi-label classification scheme, a single model is able to identify the presence of different attacks and load change simultaneously. Two different deep learning techniques are compared to design the attack detector, and the performance of the proposed distributed control and the attack detector is demonstrated through simulations on the modified IEEE 34-bus distribution test system.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130921992","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 : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632327
M. Hemmati, H. Palahalli, G. Gruosso, S. Grillo
Expansion of distributed energy resources (DERs) leads to more complex and interconnected networks in smart grids. This increased the requirement of fast and standardized information exchanges for stable, resilient, and reliable operations in microgrids. To extend interoperability, modern power grids utilize a sophisticated network of Intelligent Electronic Devices (IEDs). These devices are able to communicate with one another using the IEC-61850 communication protocol. In this article, one particular architecture to inspect Generic Object Oriented Substation Event (GOOSE) services is proposed. phase one of the project resides in design details of an assumed micro- grid simulation as the testbed in Typhoon HIL, and modelling of the characteristics of a generic IED running on a Hardware-In-the-Loop device. While phase two of the project involves the HIL test setup as a novel methodology to approach the communication scenarios of mentioned commercial relays. one particular overload scenario is stated in more detail to investigate the performance of the protection mechanisms and GOOSE services emulated in the IED.
{"title":"Interoperability analysis of IEC61850 protocol using an emulated IED in a HIL microgrid testbed","authors":"M. Hemmati, H. Palahalli, G. Gruosso, S. Grillo","doi":"10.1109/SmartGridComm51999.2021.9632327","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632327","url":null,"abstract":"Expansion of distributed energy resources (DERs) leads to more complex and interconnected networks in smart grids. This increased the requirement of fast and standardized information exchanges for stable, resilient, and reliable operations in microgrids. To extend interoperability, modern power grids utilize a sophisticated network of Intelligent Electronic Devices (IEDs). These devices are able to communicate with one another using the IEC-61850 communication protocol. In this article, one particular architecture to inspect Generic Object Oriented Substation Event (GOOSE) services is proposed. phase one of the project resides in design details of an assumed micro- grid simulation as the testbed in Typhoon HIL, and modelling of the characteristics of a generic IED running on a Hardware-In-the-Loop device. While phase two of the project involves the HIL test setup as a novel methodology to approach the communication scenarios of mentioned commercial relays. one particular overload scenario is stated in more detail to investigate the performance of the protection mechanisms and GOOSE services emulated in the IED.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131203726","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 : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9631996
Kolten Knesek, Patrick Wlazlo, Hao Huang, A. Sahu, A. Goulart, K. Davis
Phasor measurement units (PMUs) are used in power grids across North America to measure the amplitude, phase, and frequency of an alternating voltage or current. PMU's use the IEEE C37.118 protocol to send telemetry to phasor data collectors (PDC) and human machine interface (HMI) workstations in a control center. However, the C37.118 protocol utilizes the internet protocol stack without any authentication mechanism. This means that the protocol is vulnerable to false data injection (FDI) and false command injection (FCI). In order to study different scenarios in which C37.118 protocol's integrity and confidentiality can be compromised, we created a testbed that emulates a C37.118 communication network. In this testbed we conduct FCI and FDI attacks on real-time C37.118 data packets using a packet manipulation tool called Scapy. Using this platform, we generated C37.118 FCI and FDI datasets which are processed by multi-label machine learning classifier algorithms, such as Decision Tree (DT), k-Nearest Neighbor (kNN), and Naive Bayes (NB), to find out how effective machine learning can be at detecting such attacks. Our results show that the DT classifier had the best precision and recall rate.
{"title":"Detecting Attacks on Synchrophasor Protocol Using Machine Learning Algorithms","authors":"Kolten Knesek, Patrick Wlazlo, Hao Huang, A. Sahu, A. Goulart, K. Davis","doi":"10.1109/SmartGridComm51999.2021.9631996","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9631996","url":null,"abstract":"Phasor measurement units (PMUs) are used in power grids across North America to measure the amplitude, phase, and frequency of an alternating voltage or current. PMU's use the IEEE C37.118 protocol to send telemetry to phasor data collectors (PDC) and human machine interface (HMI) workstations in a control center. However, the C37.118 protocol utilizes the internet protocol stack without any authentication mechanism. This means that the protocol is vulnerable to false data injection (FDI) and false command injection (FCI). In order to study different scenarios in which C37.118 protocol's integrity and confidentiality can be compromised, we created a testbed that emulates a C37.118 communication network. In this testbed we conduct FCI and FDI attacks on real-time C37.118 data packets using a packet manipulation tool called Scapy. Using this platform, we generated C37.118 FCI and FDI datasets which are processed by multi-label machine learning classifier algorithms, such as Decision Tree (DT), k-Nearest Neighbor (kNN), and Naive Bayes (NB), to find out how effective machine learning can be at detecting such attacks. Our results show that the DT classifier had the best precision and recall rate.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128155932","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 : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632306
Yudi Huang, Ting-nian He, N. Chaudhuri, T. L. Porta
Due to the severe consequences of the coordinated cyber-physical attack (CCPA), the design of defenses has gained a lot of attention. A popular defense approach is to eliminate the existence of attacks by either securing existing sensors or deploying secured PMUs. In this work, we improve this approach by lowering the defense target from eliminating attacks to preventing outages in order to reduce the required number of secured PMUs. To this end, we formulate the problem of PMU Placement for Outage Prevention (PPOP) as a tri-level non-linear optimization and transform it into a bi-level mixed-integer linear programming (MILP) problem. Then, we propose an alternating optimization algorithm to solve it optimally. Finally, we evaluate our algorithm on IEEE 30-bus, 57-bus, and 118-bus systems, which demonstrates the advantage of the proposed approach in significantly reducing the required number of secured PMUs.
{"title":"Preventing Outages under Coordinated Cyber-Physical Attack with Secured PMUs","authors":"Yudi Huang, Ting-nian He, N. Chaudhuri, T. L. Porta","doi":"10.1109/SmartGridComm51999.2021.9632306","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632306","url":null,"abstract":"Due to the severe consequences of the coordinated cyber-physical attack (CCPA), the design of defenses has gained a lot of attention. A popular defense approach is to eliminate the existence of attacks by either securing existing sensors or deploying secured PMUs. In this work, we improve this approach by lowering the defense target from eliminating attacks to preventing outages in order to reduce the required number of secured PMUs. To this end, we formulate the problem of PMU Placement for Outage Prevention (PPOP) as a tri-level non-linear optimization and transform it into a bi-level mixed-integer linear programming (MILP) problem. Then, we propose an alternating optimization algorithm to solve it optimally. Finally, we evaluate our algorithm on IEEE 30-bus, 57-bus, and 118-bus systems, which demonstrates the advantage of the proposed approach in significantly reducing the required number of secured PMUs.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124283915","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 : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632328
Chih-Yuan Lin, August Fundin, Erik Westring, Tommy Gustafsson, Simin Nadim-Tehrani
Attacks against Supervisory Control and Data Acquisition (SCADA) systems operating critical infrastructures have increased since the appearance of Stuxnet. To defend critical infrastructures, security researchers need realistic datasets to evaluate and benchmark their defense mechanisms such as Anomaly Detection Systems (ADS). However, real-world data collected from critical infrastructures are too sensitive to share openly. Therefore, testbed datasets have become a viable option to balance the requirement of openness and realism. This study provides a data generation framework based on a virtual testbed with a commercial SCADA system and presents an openly available dataset called RICSel21, with packets in IEC-60870-5-104 protocol streams. The dataset is the result of performing 12 attacks, identifying the impact of attacks on a power management system and recording the logs of the seven successful attacks.
{"title":"RICSel21 Data Collection: Attacks in a Virtual Power Network","authors":"Chih-Yuan Lin, August Fundin, Erik Westring, Tommy Gustafsson, Simin Nadim-Tehrani","doi":"10.1109/SmartGridComm51999.2021.9632328","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632328","url":null,"abstract":"Attacks against Supervisory Control and Data Acquisition (SCADA) systems operating critical infrastructures have increased since the appearance of Stuxnet. To defend critical infrastructures, security researchers need realistic datasets to evaluate and benchmark their defense mechanisms such as Anomaly Detection Systems (ADS). However, real-world data collected from critical infrastructures are too sensitive to share openly. Therefore, testbed datasets have become a viable option to balance the requirement of openness and realism. This study provides a data generation framework based on a virtual testbed with a commercial SCADA system and presents an openly available dataset called RICSel21, with packets in IEC-60870-5-104 protocol streams. The dataset is the result of performing 12 attacks, identifying the impact of attacks on a power management system and recording the logs of the seven successful attacks.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"500 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123981030","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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