17 research outputs found
Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids
Smart grid is a large complex network with a myriad of vulnerabilities,
usually operated in adversarial settings and regulated based on estimated
system states. In this study, we propose a novel highly secure distributed
dynamic state estimation mechanism for wide-area (multi-area) smart grids,
composed of geographically separated subregions, each supervised by a local
control center. We firstly propose a distributed state estimator assuming
regular system operation, that achieves near-optimal performance based on the
local Kalman filters and with the exchange of necessary information between
local centers. To enhance the security, we further propose to (i) protect the
network database and the network communication channels against attacks and
data manipulations via a blockchain (BC)-based system design, where the BC
operates on the peer-to-peer network of local centers, (ii) locally detect the
measurement anomalies in real-time to eliminate their effects on the state
estimation process, and (iii) detect misbehaving (hacked/faulty) local centers
in real-time via a distributed trust management scheme over the network. We
provide theoretical guarantees regarding the false alarm rates of the proposed
detection schemes, where the false alarms can be easily controlled. Numerical
studies illustrate that the proposed mechanism offers reliable state estimation
under regular system operation, timely and accurate detection of anomalies, and
good state recovery performance in case of anomalies
Security Aspects of Internet of Things aided Smart Grids: a Bibliometric Survey
The integration of sensors and communication technology in power systems,
known as the smart grid, is an emerging topic in science and technology. One of
the critical issues in the smart grid is its increased vulnerability to cyber
threats. As such, various types of threats and defense mechanisms are proposed
in literature. This paper offers a bibliometric survey of research papers
focused on the security aspects of Internet of Things (IoT) aided smart grids.
To the best of the authors' knowledge, this is the very first bibliometric
survey paper in this specific field. A bibliometric analysis of all journal
articles is performed and the findings are sorted by dates, authorship, and key
concepts. Furthermore, this paper also summarizes the types of cyber threats
facing the smart grid, the various security mechanisms proposed in literature,
as well as the research gaps in the field of smart grid security.Comment: The paper is published in Elsevier's Internet of Things journal. 25
pages + 20 pages of reference
Quickest Change Detection in Autoregressive Models
The problem of quickest change detection (QCD) in autoregressive (AR) models
is investigated. A system is being monitored with sequentially observed
samples. At some unknown time, a disturbance signal occurs and changes the
distribution of the observations. The disturbance signal follows an AR model,
which is dependent over time. Before the change, observations only consist of
measurement noise, and are independent and identically distributed (i.i.d.).
After the change, observations consist of the disturbance signal and the
measurement noise, are dependent over time, which essentially follow a
continuous-state hidden Markov model (HMM). The goal is to design a stopping
time to detect the disturbance signal as quickly as possible subject to false
alarm constraints. Existing approaches for general non-i.i.d. settings and
discrete-state HMMs cannot be applied due to their high computational
complexity and memory consumption, and they usually assume some asymptotic
stability condition. In this paper, the asymptotic stability condition is
firstly theoretically proved for the AR model by a novel design of forward
variable and auxiliary Markov chain. A computationally efficient Ergodic CuSum
algorithm that can be updated recursively is then constructed and is further
shown to be asymptotically optimal. The data-driven setting where the
disturbance signal parameters are unknown is further investigated, and an
online and computationally efficient gradient ascent CuSum algorithm is
designed. The algorithm is constructed by iteratively updating the estimate of
the unknown parameters based on the maximum likelihood principle and the
gradient ascent approach. The lower bound on its average running length to
false alarm is also derived for practical false alarm control. Simulation
results are provided to demonstrate the performance of the proposed algorithms
Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence
Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented