Danilczyk, William, Sun, Yan Lindsay, He, Haibo.
2021.
Smart Grid Anomaly Detection using a Deep Learning Digital Twin. 2020 52nd North American Power Symposium (NAPS). :1—6.
The power grid is considered to be the most critical piece of infrastructure in the United States because each of the other fifteen critical infrastructures, as defined by the Cyberse-curity and Infrastructure Security Agency (CISA), require the energy sector to properly function. Due the critical nature of the power grid, the ability to detect anomalies in the power grid is of critical importance to prevent power outages, avoid damage to sensitive equipment and to maintain a working power grid. Over the past few decades, the modern power grid has evolved into a large Cyber Physical System (CPS) equipped with wide area monitoring systems (WAMS) and distributed control. As smart technology advances, the power grid continues to be upgraded with high fidelity sensors and measurement devices, such as phasor measurement units (PMUs), that can report the state of the system with a high temporal resolution. However, this influx of data can often become overwhelming to the legacy Supervisory Control and Data Acquisition (SCADA) system, as well as, the power system operator. In this paper, we propose using a deep learning (DL) convolutional neural network (CNN) as a module within the Automatic Network Guardian for ELectrical systems (ANGEL) Digital Twin environment to detect physical faults in a power system. The presented approach uses high fidelity measurement data from the IEEE 9-bus and IEEE 39-bus benchmark power systems to not only detect if there is a fault in the power system but also applies the algorithm to classify which bus contains the fault.
Roy, Sohini, Sen, Arunabha.
2021.
Identification and Mitigation of False Data Injection using Multi State Implicative Interdependency Model (MSIIM) for Smart Grid. 2021 IEEE International Conference on Communications Workshops (ICC Workshops). :1—6.
Smart grid monitoring, automation and control will completely rely on PMU based sensor data soon. Accordingly, a high throughput, low latency Information and Communication Technology (ICT) infrastructure should be opted in this regard. Due to the low cost, low power profile, dynamic nature, improved accuracy and scalability, wireless sensor networks (WSNs) can be a good choice. Yet, the efficiency of a WSN depends a lot on the network design and the routing technique. In this paper a new design of the ICT network for smart grid using WSN is proposed. In order to understand the interactions between different entities, detect their operational levels, design the routing scheme and identify false data injection by particular ICT entities, a new model of interdependency called the Multi State Implicative Interdependency Model (MSIIM) is proposed in this paper, which is an updated version of the Modified Implicative Interdependency Model (MIIM) [1]. MSIIM considers the data dependency and operational accuracy of entities together with structural and functional dependencies between them. A multi-path secure routing technique is also proposed in this paper which relies on the MSIIM model for its functioning. Simulation results prove that MSIIM based False Data Injection (FDI) detection and mitigation works better and faster than existing methods.
Wenlong, Wang, Jianquan, Liang.
2021.
Research on Node Anomaly Detection Method in Smart Grid by Beta Distribution Theory. 2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). :755—758.
As the extensive use of the wireless sensor networks in Advanced Metering Infrastructure (AMI) of Smart Grid, the network security of AMI becomes more important. Thus, an optimization of trust management mechanism of Beta distribution theory is put forward in this article. First of all, a self-adaption method of trust features sampling is proposed, that adjusts acquisition frequency according to fluctuation of trust attribute collected, which makes the consumption of network resource minimum under the precondition of ensuring accuracy of trust value; Then, the collected trust attribute is judged based on the Mahalanobis distance; Finally, calculate the nodes’ trust value by the optimization of the Beta distribution theory. As the simulation shows, the trust management scheme proposed is suited to WSNs in AMI, and able to reflect the trust value of nodes in a variety of circumstances change better.
Jena, Prasanta Kumar, Ghosh, Subhojit, Koley, Ebha.
2021.
An Optimal PMU Placement Scheme for Detection of Malicious Attacks in Smart Grid. 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE). :1—6.
State estimation is the core operation performed within the energy management system (EMS) of smart grid. Hence, the reliability and integrity of a smart grid relies heavily on the performance of sensor measurement dependent state estimation process. The increasing penetration of cyber control into the smart grid operations has raised severe concern for executing a secured state estimation process. The limitation with regard to monitoring large number of sensors allows an intruder to manipulate sensor information, as one of the soft targets for disrupting power system operations. Phasor measurement unit (PMU) can be adopted as an alternative to immunize the state estimation from corrupted conventional sensor measurements. However, the high installation cost of PMUs restricts its installation throughout the network. In this paper a graphical approach is proposed to identify minimum PMU placement locations, so as to detect any intrusion of malicious activity within the smart grid. The high speed synchronized PMU information ensures processing of secured set of sensor measurements to the control center. The results of PMU information based linear state estimation is compared with the conventional non-linear state estimation to detect any attack within the system. The effectiveness of the proposed scheme has been validated on IEEE 14 bus test system.
Xing, Ningzhe, Wu, Peng, Jin, Shen, Yao, Jiming, Xu, Zhichen.
2021.
Task Classification Unloading Algorithm For Mobile Edge Computing in Smart Grid. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 5:1636—1640.
With the rapid development of smart grid, the data generated by grid services are growing rapidly, and the requirements for time delay are becoming more and more stringent. The storage and computing capacity of the existing terminal equipment can not meet the needs of high bandwidth and low delay of the system at the same time. Fortunately, mobile edge computing (MEC) can provide users with nearby storage and computing services at the network edge, this can give an option to simultaneously meet the requirement of high bandwidth and low delay. Aiming at the problem of service offload scheduling in edge computing, this paper proposes a delay optimized task offload algorithm based on task priority classification. Firstly, the priority of power grid services is divided by using analytic hierarchy process (AHP), and the processing efficiency and quality of service of emergency tasks are guaranteed by giving higher weight coefficients to delay constraints and security levels. Secondly, the service is initialized and unloaded according to the task preprocessing time. Finally, the reasonable subchannel allocation is carried out based on the task priority design decision method. Simulation results show that compared with the traditional approaches, our algorithm can effectively improve the overall system revenue and reduce the average user task delay.
Karimi, A., Ahmadi, A., Shahbazi, Z., Shafiee, Q., Bevrani, H..
2021.
A Resilient Control Method Against False Data Injection Attack in DC Microgrids. 2021 7th International Conference on Control, Instrumentation and Automation (ICCIA). :1—6.
The expression of cyber-attacks on communication links in smart grids has emerged recently. In microgrids, cooperation between agents through communication links is required, thus, microgrids can be considered as cyber-physical-systems and they are vulnerable to cyber-attack threats. Cyber-attacks can cause damages in control systems, therefore, the resilient control methods are necessary. In this paper, a resilient control approach against false data injection attack is proposed for secondary control of DC microgrids. In the proposed framework, a PI controller with an adjustable gain is utilized to eliminate the injected false data. The proposed control method is employed for both sensor and link attacks. Convergence analysis of the measurement sensors and the secondary control objectives under the studied control method is performed. Finally, a DC microgrid with four units is built in Matlab/Simulink environment to verify the proposed approach.
Kayalvizhy, V., Banumathi, A..
2021.
A Survey on Cyber Security Attacks and Countermeasures in Smart Grid Metering Network. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). :160—165.
Smart grid (SG) network is one of the recently improved networks of tangled entities, objects, and smart metering infrastructure (SMI). It plays a vital part in sensing, acquiring, observing, aggregating, controlling, and dealing with various kinds of fields in SG. The SMI or advanced metering infrastructure (AMI) is proposed to make available a real-time transmissions connection among users and services are Time of use (TOU), Real time pricing (RTP), Critical Peak Pricing (CPP). In adding to, additional benefit of SMs is which are capable to report back to the service control center in near real time nontechnical losses (for instance, tampering with meters, bypassing meters, and illicit tapping into distribution systems). SMI supports two-way transmission meters reading electrical utilization at superior frequency. This data is treated in real time and signals send to manage demand. This paper expresses a transitory impression of cyberattack instances in customary energy networks and SMI. This paper presents cyber security attacks and countermeasures in Smart Grid Metering Network (SGMN). Based on the existing survey threat models, a number of proposed ways have been planned to deal with all threats in the formulation of the secrecy and privacy necessities of SG measurement network.
Shukla, Saurabh, Thakur, Subhasis, Breslin, John G..
2021.
Secure Communication in Smart Meters using Elliptic Curve Cryptography and Digital Signature Algorithm. 2021 IEEE International Conference on Cyber Security and Resilience (CSR). :261—266.
With the advancement in the growth of Internet-of-Things (IoT), its number of applications has also increased such as in healthcare, smart cities, vehicles, industries, household appliances, and Smart Grids (SG). One of the major applications of IoT is the SG and smart meter which consists of a large number of internet-connected sensors and can communicate bi-directionally in real-time. The SG network involves smart meters, data collectors, generators, and sensors connected with the internet. SG networks involve the generation, distribution, transmission, and consumption of electrical power supplies. It consists of Household Area Network (HAN), and Neighborhood Area Network (NAN) for communication. Smart meters can communicate bidirectionally with consumers and provide real-time information to utility offices. But this communication channel is a wide-open network for data transmission. Therefore, it makes the SG network and smart meter vulnerable to outside hacker and various Cyber-Physical System (CPS) attacks such as False Data Injection (FDI), inserting malicious data, erroneous data, manipulating the sensor reading values. Here cryptography techniques can play a major role along with the private blockchain model for secure data transmission in smart meters. Hence, to overcome these existing issues and challenges in smart meter communication we have proposed a blockchain-based system model for secure communication along with a novel Advanced Elliptic Curve Cryptography Digital Signature (AECCDS) algorithm in Fog Computing (FC) environment. Here FC nodes will work as miners at the edge of smart meters for secure and real-time communication. The algorithm is implemented using iFogSim, Geth version 1.9.25, Ganache, Truffle for compiling smart contracts, Anaconda (Python editor), and ATOM as language editor for the smart contracts.