Visible to the public Biblio

Found 190 results

Filters: Keyword is smart power grids  [Clear All Filters]
2021-04-08
Venkitasubramaniam, P., Yao, J., Pradhan, P..  2015.  Information-Theoretic Security in Stochastic Control Systems. Proceedings of the IEEE. 103:1914–1931.
Infrastructural systems such as the electricity grid, healthcare, and transportation networks today rely increasingly on the joint functioning of networked information systems and physical components, in short, on cyber-physical architectures. Despite tremendous advances in cryptography, physical-layer security and authentication, information attacks, both passive such as eavesdropping, and active such as unauthorized data injection, continue to thwart the reliable functioning of networked systems. In systems with joint cyber-physical functionality, the ability of an adversary to monitor transmitted information or introduce false information can lead to sensitive user data being leaked or result in critical damages to the underlying physical system. This paper investigates two broad challenges in information security in cyber-physical systems (CPSs): preventing retrieval of internal physical system information through monitored external cyber flows, and limiting the modification of physical system functioning through compromised cyber flows. A rigorous analytical framework grounded on information-theoretic security is developed to study these challenges in a general stochastic control system abstraction-a theoretical building block for CPSs-with the objectives of quantifying the fundamental tradeoffs between information security and physical system performance, and through the process, designing provably secure controller policies. Recent results are presented that establish the theoretical basis for the framework, in addition to practical applications in timing analysis of anonymous systems, and demand response systems in a smart electricity grid.
2021-03-29
Fajri, M., Hariyanto, N., Gemsjaeger, B..  2020.  Automatic Protection Implementation Considering Protection Assessment Method of DER Penetration for Smart Distribution Network. 2020 International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP). :323—328.
Due to geographical locations of Indonesia, some technology such as hydro and solar photovoltaics are very attractive to be used and developed. Distribution Energy Resources (DER) is the appropriate schemes implemented to achieve optimal operation respecting the location and capacity of the plant. The Gorontalo sub-system network was chosen as a case study considering both of micro-hydro and PV as contributed to supply the grid. The needs of a smart electrical system are required to improve reliability, power quality, and adaptation to any circumstances during DER application. While the topology was changing over time, intermittent of DER output and bidirectional power flow can be overcome with smart grid systems. In this study, an automation algorithm has been conducted to aid the engineers in solving the protection problems caused by DER implementation. The Protection Security Assessment (PSA) method is used to evaluate the state of the protection system. Determine the relay settings using an adaptive rule-based method on expert systems. The application with a Graphical User Interface (GUI) has been developed to make user easier to get the specific relay settings and locations which are sensitive, fast, reliable, and selective.
2021-02-16
Jin, Z., Yu, P., Guo, S. Y., Feng, L., Zhou, F., Tao, M., Li, W., Qiu, X., Shi, L..  2020.  Cyber-Physical Risk Driven Routing Planning with Deep Reinforcement-Learning in Smart Grid Communication Networks. 2020 International Wireless Communications and Mobile Computing (IWCMC). :1278—1283.
In modern grid systems which is a typical cyber-physical System (CPS), information space and physical space are closely related. Once the communication link is interrupted, it will make a great damage to the power system. If the service path is too concentrated, the risk will be greatly increased. In order to solve this problem, this paper constructs a route planning algorithm that combines node load pressure, link load balance and service delay risk. At present, the existing intelligent algorithms are easy to fall into the local optimal value, so we chooses the deep reinforcement learning algorithm (DRL). Firstly, we build a risk assessment model. The node risk assessment index is established by using the node load pressure, and then the link risk assessment index is established by using the average service communication delay and link balance degree. The route planning problem is then solved by a route planning algorithm based on DRL. Finally, experiments are carried out in a simulation scenario of a power grid system. The results show that our method can find a lower risk path than the original Dijkstra algorithm and the Constraint-Dijkstra algorithm.
2021-01-11
Farokhi, F..  2020.  Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon. 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS). :1–8.
We define discounted differential privacy, as an alternative to (conventional) differential privacy, to investigate privacy of evolving datasets, containing time series over an unbounded horizon. We use privacy loss as a measure of the amount of information leaked by the reports at a certain fixed time. We observe that privacy losses are weighted equally across time in the definition of differential privacy, and therefore the magnitude of privacy-preserving additive noise must grow without bound to ensure differential privacy over an infinite horizon. Motivated by the discounted utility theory within the economics literature, we use exponential and hyperbolic discounting of privacy losses across time to relax the definition of differential privacy under continual observations. This implies that privacy losses in distant past are less important than the current ones to an individual. We use discounted differential privacy to investigate privacy of evolving datasets using additive Laplace noise and show that the magnitude of the additive noise can remain bounded under discounted differential privacy. We illustrate the quality of privacy-preserving mechanisms satisfying discounted differential privacy on smart-meter measurement time-series of real households, made publicly available by Ausgrid (an Australian electricity distribution company).
Cao, S., Zou, J., Du, X., Zhang, X..  2020.  A Successive Framework: Enabling Accurate Identification and Secure Storage for Data in Smart Grid. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1–6.
Due to malicious eavesdropping, forgery as well as other risks, it is challenging to dispose and store collected power data from smart grid in secure manners. Blockchain technology has become a novel method to solve the above problems because of its de-centralization and tamper-proof characteristics. It is especially well known that data stored in blockchain cannot be changed, so it is vital to seek out perfect mechanisms to ensure that data are compliant with high quality (namely, accuracy of the power data) before being stored in blockchain. This will help avoid losses due to low-quality data modification or deletion as needed in smart grid. Thus, we apply the parallel vision theory on the identification of meter readings to realize accurate power data. A cloud-blockchain fusion model (CBFM) is proposed for the storage of accurate power data, allowing for secure conducting of flexible transactions. Only power data calculated by parallel visual system instead of image data collected originally via robot would be stored in blockchain. Hence, we define the quality assurance before data uploaded to blockchain and security guarantee after data stored in blockchain as a successive framework, which is a brand new solution to manage efficiency and security as a whole for power data and data alike in other scenes. Security analysis and performance evaluations are performed, which prove that CBFM is highly secure and efficient impressively.
2020-12-11
Zhang, L., Shen, X., Zhang, F., Ren, M., Ge, B., Li, B..  2019.  Anomaly Detection for Power Grid Based on Time Series Model. 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). :188—192.

In the process of informationization and networking of smart grids, the original physical isolation was broken, potential risks increased, and the increasingly serious cyber security situation was faced. Therefore, it is critical to develop accuracy and efficient anomaly detection methods to disclose various threats. However, in the industry, mainstream security devices such as firewalls are not able to detect and resist some advanced behavior attacks. In this paper, we propose a time series anomaly detection model, which is based on the periodic extraction method of discrete Fourier transform, and determines the sequence position of each element in the period by periodic overlapping mapping, thereby accurately describe the timing relationship between each network message. The experiments demonstrate that our model can detect cyber attacks such as man-in-the-middle, malicious injection, and Dos in a highly periodic network.

2020-11-30
Chen, Z., Bai, B., Chen, D., Chai, W..  2018.  Design of Distribution Devices for Smart Grid Based on Magnetically Tunable Nanocomposite. IEEE Transactions on Power Electronics. 33:2083–2099.
This paper designs three distribution devices for the smart grid, which are, respectively, novel transformer with dc bias restraining ability, energy-saving contactor, and controllable reactor with adjustable intrinsic magnetic state based on the magnetically tunable nanocomposite material core. First, the magnetic performance of this magnetic material was analyzed and the magnetic properties processing method was put forward. One kind of nanocomposite which is close to the semihard magnetic state with low coercivity and high remanence was attained. Nanocomposite with four magnetic properties was processed and prepared using the distribution devices design. Second, in order to adjust the magnetic state better, the magnetization and demagnetization control circuit based on the single-phase supply power of rectification and inverter for the nanocomposite magnetic performance adjustment has been designed, which can mutual transform the material's soft and hard magnetic phases. Finally, based on the nanocomposite and the control circuit, a novel power transformer, an energy-saving contactor, and a magnetically controllable reactor were manufactured for the smart grid. The maintained remanence of the nanocomposite core after the magnetization could neutralize the dc bias magnetic flux in the transformer main core without changing the transformer neutral point connection mode, could pull in the contactor movable core instead of the traditional electromagnetic-type fixed core, and could adjust the reactor core saturation degree instead of the traditional electromagnetic coil. The simulation and experimental results verify the correctness of the design, which provides reliable, intelligent, interactive, and energy-saving power equipment for the smart power grids safe operation.
2020-11-20
Paul, S., Padhy, N. P., Mishra, S. K., Srivastava, A. K..  2019.  UUCA: Utility-User Cooperative Algorithm for Flexible Load Scheduling in Distribution System. 2019 8th International Conference on Power Systems (ICPS). :1—6.
Demand response analysis in smart grid deployment substantiated itself as an important research area in recent few years. Two-way communication between utility and users makes peak load reduction feasible by delaying the operation of deferrable appliances. Flexible appliance rescheduling is preferred to the users compared to traditional load curtailment. Again, if users' preferences are accounted into appliance transferring process, then customers concede a little discomfort to help the utility in peak reduction. This paper presents a novel Utility-User Cooperative Algorithm (UUCA) to lower total electricity cost and gross peak demand while preserving users' privacy and preferences. Main driving force in UUCA to motivate the consumers is a new cost function for their flexible appliances. As a result, utility will experience low peak and due to electricity cost decrement, users will get reduced bill. However, to maintain privacy, the behaviors of one customer have not be revealed either to other customers or to the central utility. To justify the effectiveness, UUCA is executed separately on residential, commercial and industrial customers of a distribution grid. Harmony search optimization technique has proved itself superior compared to other heuristic search techniques to prove efficacy of UUCA.
Sarochar, J., Acharya, I., Riggs, H., Sundararajan, A., Wei, L., Olowu, T., Sarwat, A. I..  2019.  Synthesizing Energy Consumption Data Using a Mixture Density Network Integrated with Long Short Term Memory. 2019 IEEE Green Technologies Conference(GreenTech). :1—4.
Smart cities comprise multiple critical infrastructures, two of which are the power grid and communication networks, backed by centralized data analytics and storage. To effectively model the interdependencies between these infrastructures and enable a greater understanding of how communities respond to and impact them, large amounts of varied, real-world data on residential and commercial consumer energy consumption, load patterns, and associated human behavioral impacts are required. The dissemination of such data to the research communities is, however, largely restricted because of security and privacy concerns. This paper creates an opportunity for the development and dissemination of synthetic energy consumption data which is inherently anonymous but holds similarities to the properties of real data. This paper explores a framework using mixture density network (MDN) model integrated with a multi-layered Long Short-Term Memory (LSTM) network which shows promise in this area of research. The model is trained using an initial sample recorded from residential smart meters in the state of Florida, and is used to generate fully synthetic energy consumption data. The synthesized data will be made publicly available for interested users.
Semwal, S., Badoni, M., Saxena, N..  2019.  Smart Meters for Domestic Consumers: Innovative Methods for Identifying Appliances using NIALM. 2019 Women Institute of Technology Conference on Electrical and Computer Engineering (WITCON ECE). :81—90.
A country drives by their people and the electricity energy, the availability of the electricity power reflects the strength of that country. All most everything depends on the electricity energy, So it is become very important that we use the available energy very efficiently, and here the energy management come in the picture and Non Intrusive appliance Load monitoring (NIALM) is the part of energy management, in which the energy consumption by the particular load is monitored without any intrusion of wire/circuit. In literature, NIALM has been discussed as a monitoring process for conservation of energy using single point sensing (SPS) for extraction of aggregate signal of the appliances' features, ignoring the second function of demand response (DR) assuming that it would be manual or sensor-based. This assumption is not implementable in developing countries like India, because of requirement of extra cost of sensors, and privacy concerns. Surprisingly, despite decades of research on NIALM, none of the suggested procedures has resulted in commercial application. This paper highlights the causes behind non- commercialization, and proposes a viable and easy solution worthy of commercial exploitation both for monitoring and DR management for outage reduction in respect of Indian domestic consumers. Using a approach of multi point sensing (MPS), combined with Independent Component Analysis (ICA), experiments has been done in laboratory environment and CPWD specification has been followed.
Efstathopoulos, G., Grammatikis, P. R., Sarigiannidis, P., Argyriou, V., Sarigiannidis, A., Stamatakis, K., Angelopoulos, M. K., Athanasopoulos, S. K..  2019.  Operational Data Based Intrusion Detection System for Smart Grid. 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). :1—6.

With the rapid progression of Information and Communication Technology (ICT) and especially of Internet of Things (IoT), the conventional electrical grid is transformed into a new intelligent paradigm, known as Smart Grid (SG). SG provides significant benefits both for utility companies and energy consumers such as the two-way communication (both electricity and information), distributed generation, remote monitoring, self-healing and pervasive control. However, at the same time, this dependence introduces new security challenges, since SG inherits the vulnerabilities of multiple heterogeneous, co-existing legacy and smart technologies, such as IoT and Industrial Control Systems (ICS). An effective countermeasure against the various cyberthreats in SG is the Intrusion Detection System (IDS), informing the operator timely about the possible cyberattacks and anomalies. In this paper, we provide an anomaly-based IDS especially designed for SG utilising operational data from a real power plant. In particular, many machine learning and deep learning models were deployed, introducing novel parameters and feature representations in a comparative study. The evaluation analysis demonstrated the efficacy of the proposed IDS and the improvement due to the suggested complex data representation.

Prasad, G., Huo, Y., Lampe, L., Leung, V. C. M..  2019.  Machine Learning Based Physical-Layer Intrusion Detection and Location for the Smart Grid. 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1—6.
Security and privacy of smart grid communication data is crucial given the nature of the continuous bidirectional information exchange between the consumer and the utilities. Data security has conventionally been ensured using cryptographic techniques implemented at the upper layers of the network stack. However, it has been shown that security can be further enhanced using physical layer (PHY) methods. To aid and/or complement such PHY and upper layer techniques, in this paper, we propose a PHY design that can detect and locate not only an active intruder but also a passive eavesdropper in the network. Our method can either be used as a stand-alone solution or together with existing techniques to achieve improved smart grid data security. Our machine learning based solution intelligently and automatically detects and locates a possible intruder in the network by reusing power line transmission modems installed in the grid for communication purposes. Simulation results show that our cost-efficient design provides near ideal intruder detection rates and also estimates its location with a high degree of accuracy.
Roy, D. D., Shin, D..  2019.  Network Intrusion Detection in Smart Grids for Imbalanced Attack Types Using Machine Learning Models. 2019 International Conference on Information and Communication Technology Convergence (ICTC). :576—581.
Smart grid has evolved as the next generation power grid paradigm which enables the transfer of real time information between the utility company and the consumer via smart meter and advanced metering infrastructure (AMI). These information facilitate many services for both, such as automatic meter reading, demand side management, and time-of-use (TOU) pricing. However, there have been growing security and privacy concerns over smart grid systems, which are built with both smart and legacy information and operational technologies. Intrusion detection is a critical security service for smart grid systems, alerting the system operator for the presence of ongoing attacks. Hence, there has been lots of research conducted on intrusion detection in the past, especially anomaly-based intrusion detection. Problems emerge when common approaches of pattern recognition are used for imbalanced data which represent much more data instances belonging to normal behaviors than to attack ones, and these approaches cause low detection rates for minority classes. In this paper, we study various machine learning models to overcome this drawback by using CIC-IDS2018 dataset [1].
Lu, X., Guan, Z., Zhou, X., Du, X., Wu, L., Guizani, M..  2019.  A Secure and Efficient Renewable Energy Trading Scheme Based on Blockchain in Smart Grid. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :1839—1844.
Nowadays, with the diversification and decentralization of energy systems, the energy Internet makes it possible to interconnect distributed energy sources and consumers. In the energy trading market, the traditional centralized model relies entirely on trusted third parties. However, as the number of entities involved in the transactions grows and the forms of transactions diversify, the centralized model gradually exposes problems such as insufficient scalability, High energy consumption, and low processing efficiency. To address these challenges, we propose a secure and efficient energy renewable trading scheme based on blockchain. In our scheme, the electricity market trading model is divided into two levels, which can not only protect the privacy, but also achieve a green computing. In addition, in order to adapt to the relatively weak computing power of the underlying equipment in smart grid, we design a credibility-based equity proof mechanism to greatly improve the system availability. Compared with other similar distributed energy trading schemes, we prove the advantages of our scheme in terms of high operational efficiency and low computational overhead through experimental evaluations. Additionally, we conduct a detailed security analysis to demonstrate that our solution meets the security requirements.
Lardier, W., Varo, Q., Yan, J..  2019.  Quantum-Sim: An Open-Source Co-Simulation Platform for Quantum Key Distribution-Based Smart Grid Communications. 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1—6.
Grid modernization efforts with the latest information and communication technologies will significantly benefit smart grids in the coming years. More optical fibre communications between consumers and the control center will promise better demand response and customer engagement, yet the increasing attack surface and man-in-the-middle (MITM) threats can result in security and privacy challenges. Among the studies for more secure smart grid communications, quantum key distribution protocols (QKD) have emerged as a promising option. To bridge the theoretical advantages of quantum communication to its practical utilization, however, comprehensive investigations have to be conducted with realistic cyber-physical smart grid structures and scenarios. To facilitate research in this direction, this paper proposes an open-source, research-oriented co-simulation platform that orchestrates cyber and power simulators under the MOSAIK framework. The proposed platform allows flexible and realistic power flow-based co-simulation of quantum communications and electrical grids, where different cyber and power topologies, QKD protocols, and attack threats can be investigated. Using quantum-based communication under MITM attacks, the paper presented detailed case studies to demonstrate how the platform enables quick setup of a lowvoltage distribution grid, implementation of different protocols and cryptosystems, as well as evaluations of both communication efficiency and security against MITM attacks. The platform has been made available online to empower researchers in the modelling of quantum-based cyber-physical systems, pilot studies on quantum communications in smart grid, as well as improved attack resilience against malicious intruders.
Romdhane, R. B., Hammami, H., Hamdi, M., Kim, T..  2019.  At the cross roads of lattice-based and homomorphic encryption to secure data aggregation in smart grid. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :1067—1072.

Various research efforts have focused on the problem of customer privacy protection in the smart grid arising from the large deployment of smart energy meters. In fact, the deployed smart meters distribute accurate profiles of home energy use, which can reflect the consumers' behaviour. This paper proposes a privacy-preserving lattice-based homomorphic aggregation scheme. In this approach, the smart household appliances perform the data aggregation while the smart meter works as relay node. Its role is to authenticate the exchanged messages between the home area network appliances and the related gateway. Security analysis show that our scheme guarantees consumer privacy and messages confidentiality and integrity in addition to its robustness against several attacks. Experimental results demonstrate the efficiency of our proposed approach in terms of communication complexity.

Antoniadis, I. I., Chatzidimitriou, K. C., Symeonidis, A. L..  2019.  Security and Privacy for Smart Meters: A Data-Driven Mapping Study. 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). :1—5.
Smart metering systems have been gaining popularity as a vital part of the general smart grid paradigm. Naturally, as new technologies arise to cover this emerging field, so do security and privacy related issues regarding the energy consumer's personal data. These challenges impose the need for the development of new methods through a better understanding of the state-of-the-art. This paper aims at identifying the main categories of security and privacy techniques utilized in smart metering systems from a three-point perspective: i) a field research survey, ii) EU initiatives and findings towards the same direction and iii) a data-driven analysis of the state-of-the-art and the identification of its main topics (or themes) using topic modeling techniques. Detailed quantitative results of this analysis, such as semantic interpretation of the identified topics and a graph representation of the topic trends over time, are presented.
Yogarathinam, A., Chaudhuri, N. R..  2019.  Wide-Area Damping Control Using Multiple DFIG-Based Wind Farms Under Stochastic Data Packet Dropouts. 2019 IEEE Power Energy Society General Meeting (PESGM). :1—1.
Data dropouts in communication network can have a significant impact on wide-area oscillation damping control of a smart power grid with large-scale deployment of distributed and networked phasor measurement units and wind energy resources. Remote feedback signals sent through communication channels encounter data dropout, which is represented by the Gilbert-Elliott model. An observer-driven reduced copy (ORC) approach is presented, which uses the knowledge of the nominal system dynamics during data dropouts to improve the damping performance where conventional feedback would suffer. An expression for the expectation of the bound on the error norm between the actual and the estimated states relating uncertainties in the cyber system due to data dropout and physical system due to change in operating conditions is also derived. The key contribution comes from the analytical derivation of the impact of coupling between the cyber and the physical layer on ORC performance. Monte Carlo simulation is performed to calculate the dispersion of the error bound. Nonlinear time-domain simulations demonstrate that the ORC produces significantly better performance compared to conventional feedback under higher data drop situations.
2020-11-02
Ivanov, I, Maple, C, Watson, T, Lee, S.  2018.  Cyber security standards and issues in V2X communications for Internet of Vehicles. Living in the Internet of Things: Cybersecurity of the IoT – 2018. :1—6.

Significant developments have taken place over the past few years in the area of vehicular communication systems in the ITS environment. It is vital that, in these environments, security is considered in design and implementation since compromised vulnerabilities in one vehicle can be propagated to other vehicles, especially given that V2X communication is through an ad-hoc type network. Recently, many standardisation organisations have been working on creating international standards related to vehicular communication security and the so-called Internet of Vehicles (IoV). This paper presents a discussion of current V2X communications cyber security issues and standardisation approaches being considered by standardisation bodies such as the ISO, the ITU, the IEEE, and the ETSI.

2020-10-14
Khezrimotlagh, Darius, Khazaei, Javad, Asrari, Arash.  2019.  MILP Modeling of Targeted False Load Data Injection Cyberattacks to Overflow Transmission Lines in Smart Grids. 2019 North American Power Symposium (NAPS). :1—7.
Cyber attacks on transmission lines are one of the main challenges in security of smart grids. These targeted attacks, if not detected, might cause cascading problems in power systems. This paper proposes a bi-level mixed integer linear programming (MILP) optimization model for false data injection on targeted buses in a power system to overflow targeted transmission lines. The upper level optimization problem outputs the optimized false data injections on targeted load buses to overflow a targeted transmission line without violating bad data detection constraints. The lower level problem integrates the false data injections into the optimal power flow problem without violating the optimal power flow constraints. A few case studies are designed to validate the proposed attack model on IEEE 118-bus power system.
Ou, Yifan, Deng, Bin, Liu, Xuan, Zhou, Ke.  2019.  Local Outlier Factor Based False Data Detection in Power Systems. 2019 IEEE Sustainable Power and Energy Conference (iSPEC). :2003—2007.
The rapid developments of smart grids provide multiple benefits to the delivery of electric power, but at the same time makes the power grids under the threat of cyber attackers. The transmitted data could be deliberately modified without triggering the alarm of bad data detection procedure. In order to ensure the stable operation of the power systems, it is extremely significant to develop effective abnormal detection algorithms against injected false data. In this paper, we introduce the density-based LOF algorithm to detect the false data and dummy data. The simulation results show that the traditional density-clustering based LOF algorithm can effectively identify FDA, but the detection performance on DDA is not satisfactory. Therefore, we propose the improved LOF algorithm to detect DDA by setting reasonable density threshold.
Trevizan, Rodrigo D., Ruben, Cody, Nagaraj, Keerthiraj, Ibukun, Layiwola L., Starke, Allen C., Bretas, Arturo S., McNair, Janise, Zare, Alina.  2019.  Data-driven Physics-based Solution for False Data Injection Diagnosis in Smart Grids. 2019 IEEE Power Energy Society General Meeting (PESGM). :1—5.
This paper presents a data-driven and physics-based method for detection of false data injection (FDI) in Smart Grids (SG). As the power grid transitions to the use of SG technology, it becomes more vulnerable to cyber-attacks like FDI. Current strategies for the detection of bad data in the grid rely on the physics based State Estimation (SE) process and statistical tests. This strategy is naturally vulnerable to undetected bad data as well as false positive scenarios, which means it can be exploited by an intelligent FDI attack. In order to enhance the robustness of bad data detection, the paper proposes the use of data-driven Machine Intelligence (MI) working together with current bad data detection via a combined Chi-squared test. Since MI learns over time and uses past data, it provides a different perspective on the data than the SE, which analyzes only the current data and relies on the physics based model of the system. This combined bad data detection strategy is tested on the IEEE 118 bus system.
Wang, Yufeng, Shi, Wanjiao, Jin, Qun, Ma, Jianhua.  2019.  An Accurate False Data Detection in Smart Grid Based on Residual Recurrent Neural Network and Adaptive threshold. 2019 IEEE International Conference on Energy Internet (ICEI). :499—504.
Smart grids are vulnerable to cyber-attacks, which can cause significant damage and huge economic losses. Generally, state estimation (SE) is used to observe the operation of the grid. State estimation of the grid is vulnerable to false data injection attack (FDIA), so diagnosing this type of malicious attack has a major impact on ensuring reliable operation of the power system. In this paper, we present an effective FDIA detection method based on residual recurrent neural network (R2N2) prediction model and adaptive judgment threshold. Specifically, considering the data contains both linear and nonlinear components, the R2N2 model divides the prediction process into two parts: the first part uses the linear model to fit the state data; the second part predicts the nonlinearity of the residuals of the linear prediction model. The adaptive judgment threshold is inferred through fitting the Weibull distribution with the sum of squared errors between the predicted values and observed values. The thorough simulation results demonstrate that our scheme performs better than other prediction based FDIA detection schemes.
2020-10-12
Khosravi, Morteza, Fereidunian, Alireza.  2019.  Enhancing Smart Grid Cyber-Security Using A Fuzzy Adaptive Autonomy Expert System. 2019 Smart Grid Conference (SGC). :1–6.

Smart Grid cyber-security sounds to be a critical issue, because of widespread development of information technology. To achieve secure and reliable operation, the complexity of human automation interaction (HAI) necessitates more sophisticated and intelligent methodologies. In this paper, an adaptive autonomy fuzzy expert system is developed using gradient descent algorithm to determine the Level of Automation (LOA), based on the changing of Performance Shaping Factors (PSF). These PSFs indicate the effects of environmental conditions on the performance of HAI. The major advantage of this method is that the fuzzy rule or membership function can be learnt without changing the form of the fuzzy rule in conventional fuzzy control. Because of data shortage, Leave-One-Out Cross-Validation (LOOCV) technique is applied for assessing how the results of proposed system generalizes to the new contingency situations. The expert system database is extracted from superior experts' judgments. In order to regard the importance of each PSF, weighted rules are also considered. In addition, some new environmental conditions are introduced that has not been seen before. Nine scenarios are discussed to reveal the performance of the proposed system. Results confirm that the presented fuzzy expert system can effectively calculates the proper LOA even in the new contingency situations.

2020-10-05
Zhou, Xingyu, Li, Yi, Barreto, Carlos A., Li, Jiani, Volgyesi, Peter, Neema, Himanshu, Koutsoukos, Xenofon.  2019.  Evaluating Resilience of Grid Load Predictions under Stealthy Adversarial Attacks. 2019 Resilience Week (RWS). 1:206–212.
Recent advances in machine learning enable wider applications of prediction models in cyber-physical systems. Smart grids are increasingly using distributed sensor settings for distributed sensor fusion and information processing. Load forecasting systems use these sensors to predict future loads to incorporate into dynamic pricing of power and grid maintenance. However, these inference predictors are highly complex and thus vulnerable to adversarial attacks. Moreover, the adversarial attacks are synthetic norm-bounded modifications to a limited number of sensors that can greatly affect the accuracy of the overall predictor. It can be much cheaper and effective to incorporate elements of security and resilience at the earliest stages of design. In this paper, we demonstrate how to analyze the security and resilience of learning-based prediction models in power distribution networks by utilizing a domain-specific deep-learning and testing framework. This framework is developed using DeepForge and enables rapid design and analysis of attack scenarios against distributed smart meters in a power distribution network. It runs the attack simulations in the cloud backend. In addition to the predictor model, we have integrated an anomaly detector to detect adversarial attacks targeting the predictor. We formulate the stealthy adversarial attacks as an optimization problem to maximize prediction loss while minimizing the required perturbations. Under the worst-case setting, where the attacker has full knowledge of both the predictor and the detector, an iterative attack method has been developed to solve for the adversarial perturbation. We demonstrate the framework capabilities using a GridLAB-D based power distribution network model and show how stealthy adversarial attacks can affect smart grid prediction systems even with a partial control of network.