Grid Cyber-physical System Research Articles

Simulation and modeling of deep adversarial probabilistic neural network-based intrusion prevention system in cloud computing for smart grid cyber physical systems

Smart Grid Cyber Physical Systems (SG-CPS) have a substantial impact on power grid infrastructure upgrading. Nonetheless, due to the sophisticated nature of the infrastructure and the critical demand for resilient intrusion prevention systems, the task of protecting its security against data integrity attacks is a significant challenge. The simulation and assessment of security performance in SG-CPS present substantial hurdles in real-world power grid systems, owing mostly to experimental constraints. This necessitates the development of novel ways to improve distribution chain security. This research introduces a novel approach, employing a Deep Adversarial Probabilistic Neural Network (DAPNN)-based Intrusion prevention system in a cloud environment. Combining Bayesian Probabilistic Neural Networks (BEPNNs) with adversarial training and rule-based decision-making enhances precision and resilience. The major goal of this research is to detect and counteract false data injection (FDI) attacks that have the potential to compromise the integrity of power grid data. This paper proposes a novel methodology for intrusion detection in SG-CPS that combines BEPNNs with adversarial training. The addition of rule-based decision-making improves the system’s precision and resilience. The IEEE 24-bus system provides the foundation for providing data points relevant to normal operating conditions, contingency scenarios, and intentional attacks. The training procedure includes the use of a BEPNN for feature extraction, as well as the use of adversarial training approaches. The intrusion detection system has decision-making logic based on rules. The cloud infrastructure solution used in the study is Microsoft Azure. The results show that the DAPNN-based Intrusion Prevention System is effective in detecting and mitigating FDI attacks in SG-CPS. The system outperforms in terms of accuracy, precision, recall and F-measure, hence improving the security of the power grid infrastructure.

Enhanced robust frequency stabilization of a microgrid against simultaneous cyber-attacks

A microgrid (MG) is a smart grid cyber-physical system, with component coordination relying on cyber resilience. Weak communications, protocols, and tools make the MG's secondary frequency control vulnerable to various cyber-attacks, posing new challenges and stability risks. In response to this challenge, this paper introduces the enhanced robust H∞ technique considering the dynamic impacts of cyber-attacks on secondary frequency control to develop a secondary frequency control loop, improving the regulation performance and cyber resiliency of the MG frequency. The secondary control cyber-attack strategies mainly rely on false data injection (FDI), denial of service (DoS), and controller hijacking. These attack techniques are simultaneously considered in formulating the H∞ problem and control synthesis as unstructured parametric uncertainty, attenuating the concurrent cyber impacts. The study extends a load frequency control model to illustrate how cyber-attacks can be represented mathematically and physically in the MG. The results reveal that cyber-attacks affect secondary frequency control elements differently depending on the type of cyber threats used. By implementing an enhanced H∞ controller, the MG can effectively maintain stable frequency levels even when faced with malicious attacks and disruptions caused by renewable energy sources and loads.

BSCSML: Design of an Efficient Bioinspired Security &Privacy Model for Cyber Physical System using Machine Learning

With the increasing prevalence of Smart Grid Cyber Physical Systems with Advanced Metering Infrastructure (SG CPS AMI), securing their internal components has become one of the paramount concerns. Traditional security mechanisms have proven to be insufficient in defending against sophisticated attacks. Bioinspired security and privacy models have emerged as promising solutions due to their stochastic solutions. This paper proposes a novel bio-inspired security and privacy model for SG CPS AMI that utilizes machine learning to strengthen their security levels. The proposed model is inspired by the hybrid Grey Wolf Teacher Learner based Optimizer (GWTLbO) Method’s ability to detect and respond to threats in real-time deployments. The GWTLbO Model also ensures higher privacy by selecting optimal methods between k-privacy, t-closeness & l-diversity depending upon contextual requirements. This study improves system accuracy and efficiency under diverse attacks using machine learning techniques. The method uses supervised learning to teach the model to recognize known attack trends and uncontrolled learning to spot unknown attacks. Our model was tested using real-time IoT device data samples. The model identified Zero-Day Attacks, Meter Bypass, Flash Image Manipulation, and Buffer-level attacks. The proposed model detects and responds to attacks with high accuracy and low false-positive rates. In real-time operations, the proposed model can handle huge volumes of data efficiently. The bioinspired security and privacy model secures CPS efficiently and is scalable for various cases. Machine learning techniques can improve the security and secrecy of these systems and revolutionize defense against different attacks.

Research on improving robustness strategy of grid cyber physical system considering spatial information

Adding additional links can be recognized as an effective way to improve the robustness of grid cyber physical system (GCPS) against cascading failures. However, for a typical large-scale and across-regional GCPS, previous studies mainly focus on the network topology without considering the components/nodes spatial information. Therefore, to improve the robustness of GCPS without changing the existing connections, five different link addition strategies, considering spatial information and construction cost constraints, are proposed. Firstly, a spatial GCPS model is established, which consists of the spatial power system and the spatial information system with hierarchical structures. Among them, the spatial information system consists of the access layer (AL), the backbone layer (BL) and the core layer (CL) and its network nodes are divided into different layers according to their functions. Secondly, based on the spatial information system layer nodes functions and the spatial power system nodes attributions, five link addition strategies, AL-AL, AL-BL, AL-CL, Generators-CL (G-CL) and Loads-CL (L-CL), are investigated under two constraint scenarios. Thirdly, to verify our proposed five link addition strategies have better improvement effects, we compare them with the two existing excellent strategies, including low degree link addition strategy (LD) and random addition link addition strategy (Random). Finally, the statistical test results on the real 39-node power system and the IEEE 118 bus system show that our proposed link addition strategies not only effectively improve the robustness of GCPS, but also have better effects than LD and Random. More specifically, no matter whether GCPS suffered severe or slight attacks, the AL-BL always performs better when either fixed number constraint scenario or fixed length constraint scenario, and the improvement effects of AL-AL, AL-CL, G-CL and L-CL are exhibited as descending orders, respectively. These findings can help maintainers to improve the robustness of GCPS before attacks.

Review on the key technologies of power grid cyber‐physical systems simulation

AbstractA Cyber‐Physical System (CPS) is a spatiotemporal multi‐dimensional and heterogeneous hybrid autonomous system composed of deep integration of information resources and physical systems. With the development of digitisation and digitalisation, a large number of data acquisition equipment, computing equipment, and electrical equipment are interconnected between the power grid and the information communication network. The power grid has thus been restructured as a mature and highly complex CPS. In order to promote the development of power grid CPS technologies and provide a reference for relevant researchers in the field, the origin and concept of CPS and features in power grid CPS are introduced firstly. Then the key technologies of power grid CPS simulation are discussed and further analysed from three perspectives, including modelling theory, simulation methods, and system‐level simulation. On this basis, the application of CPS simulation technology in future power grids has been prospected.

Research on Data Poisoning Attack against Smart Grid Cyber-Physical System Based on Edge Computing.

Data poisoning attack is a well-known attack against machine learning models, where malicious attackers contaminate the training data to manipulate critical models and predictive outcomes by masquerading as terminal devices. As this type of attack can be fatal to the operation of a smart grid, addressing data poisoning is of utmost importance. However, this attack requires solving an expensive two-level optimization problem, which can be challenging to implement in resource-constrained edge environments of the smart grid. To mitigate this issue, it is crucial to enhance efficiency and reduce the costs of the attack. This paper proposes an online data poisoning attack framework based on the online regression task model. The framework achieves the goal of manipulating the model by polluting the sample data stream that arrives at the cache incrementally. Furthermore, a point selection strategy based on sample loss is proposed in this framework. Compared to the traditional random point selection strategy, this strategy makes the attack more targeted, thereby enhancing the attack's efficiency. Additionally, a batch-polluting strategy is proposed in this paper, which synchronously updates the poisoning points based on the direction of gradient ascent. This strategy reduces the number of iterations required for inner optimization and thus reduces the time overhead. Finally, multiple experiments are conducted to compare the proposed method with the baseline method, and the evaluation index of loss over time is proposed to demonstrate the effectiveness of the method. The results show that the proposed method outperforms the existing baseline method in both attack effectiveness and overhead.

False data injection attack in smart grid cyber physical system: Issues, challenges, and future direction

Smart grid integrates the physical power system infrastructure with internet-of-things-based digital communication networks that work together for grid stability, sustainability, and reliability. A significant number of smart devices converge in cyber-physical systems to make the smart grid more competitive and efficient in addressing the energy challenges and vulnerabilities in power system confidentiality, integrity, and availability in smart grid cyber-physical security systems. False data injection attacks are the most malicious threats in the smart grid paradigm and have been widely applied recently. Last few years, several detection algorithms for identifying the false data injection attack have been developed. Addressing these issues, this paper reports a false data injection attack and threat mathematical model, impacting the on-grid system, economy, and society. The classification of false data injection attack detection algorithms and mathematical models are mainly presented. Finally, issues and challenges are identified from existing research and recommended for future research direction.

A Low-Latency RDP-CORDIC Algorithm for Real-Time Signal Processing of Edge Computing Devices in Smart Grid Cyber-Physical Systems.

Smart grids are being expanded in scale with the increasing complexity of the equipment. Edge computing is gradually replacing conventional cloud computing due to its low latency, low power consumption, and high reliability. The CORDIC algorithm has the characteristics of high-speed real-time processing and is very suitable for hardware accelerators in edge computing devices. The iterative calculation method of the CORDIC algorithm yet leads to problems such as complex structure and high consumption of hardware resource. In this paper, we propose an RDP-CORDIC algorithm which pre-computes all micro-rotation directions and transforms the conventional single-stage iterative structure into a three-stage and multi-stage combined iterative structure, thereby enabling it to solve the problems of the conventional CORDIC algorithm with many iterations and high consumption. An accuracy compensation algorithm for the direction prediction constant is also proposed to solve the problem of high ROM consumption in the high precision implementation of the RDP-CORDIC algorithm. The experimental results showed that the RDP-CORDIC algorithm had faster computation speed and lower resource consumption with higher guaranteed accuracy than other CORDIC algorithms. Therefore, the RDP-CORDIC algorithm proposed in this paper may effectively increase computation performance while reducing the power and resource consumption of edge computing devices in smart grid systems.

Resiliency assessment methodology for synchrophasor communication networks in a smart grid cyber–physical system

The smart grid cyber–physical system (SGCPS) is the latest evolution of the traditional power system. Synchrophasor application in the SGCPS is responsible for wide area monitoring and control of the grid. Its communication network referred to as the synchrophasor communication network (SCN) has to be resilient. The resiliency measures the system’s ability to bounce back to the operational state from the failed state. Despite the maturity of the research on resiliency, it is still sparsely explored for the SCNs in a SGCPS. There is no comprehensive resiliency metric for the resiliency analysis of the SCN. Thus, a comprehensive resiliency metric in the context of the SCNs is presented in this paper. Further, a methodology is also presented for evaluating the resiliency of the SCN. The SCNs are designed for the practical power grid of West Bengal State, India, which have been analyzed for their resiliency using the proposed methodology.

A Comprehensive Framework for the Assessment of Synchrophasor Communication Networks from the Perspective of Situational Awareness in a Smart Grid Cyber Physical System

The conventional power grid is modernizing into a cyber-physical smart grid. In this system, there are several applications, an important being the synchrophasor measurement system. Sensors called the phasor measurement units, measure the synchrophasors and communicate them to the phasor data concentrator using the synchrophasor communication networks. Situational awareness helps in the quick perception of the environment and in taking commensurate actions by the power system operator. Thus, the synchrophasor communication networks play an important role and must be assessed from the perspective of the operator’s situational awareness. In this context, a comprehensive situational awareness analysis metric is proposed. The proposed metric is based on hardware reliability, data reliability, and network delay. As a case study, a practical power grid of India (West Bengal Power Grid) is considered for assessing its situational awareness. The synchrophasor communication networks of the West Bengal power grid are simulated in the QualNet network simulator to validate the proposed situational awareness framework.

A Comprehensive Risk Assessment Framework for Synchrophasor Communication Networks in a Smart Grid Cyber Physical System with a Case Study

The smart grid (SG), which has revolutionized the power grid, is being further improved by using the burgeoning cyber physical system (CPS) technology. The conceptualization of SG using CPS, which is referred to as the smart grid cyber physical system (SGCPS), has gained a momentum with the synchrophasor measurements. The edifice of the synchrophasor system is its communication network referred to as a synchrophasor communication network (SCN), which is used to communicate the synchrophasor data from the sensors known as phasor measurement units (PMUs) to the control center known as the phasor data concentrator (PDC). However, the SCN is vulnerable to hardware and software failures that introduce risk. Thus, an appropriate risk assessment framework for the SCN is needed to alleviate the risk in the protection and control of the SGCPS. In this direction, a comprehensive risk assessment framework has been proposed in this article for three types of SCNs, namely: dedicated SCN, shared SCN and hybrid SCN in an SGCPS. The proposed framework uses hardware reliability as well as data reliability to evaluate the associated risk. A simplified hardware reliability model has been proposed for each of these networks, based on failure probability to assess risk associated with hardware failures. Furthermore, the packet delivery ratio (PDR) metric is considered for measuring risk associated with data reliability. To mimic practical shared and hybrid SCNs, the risk associated with data reliability is evaluated for different background traffics of 70%, 80% and 95% using 64 Kbps and 300 Kbps PMU data rates. The analytical results are meticulously validated by considering a case study of West Bengal’s (a state in India) power grid. With respect to the case study, different SCNs are designed and simulated using the QualNet network simulator. The simulations are performed for dedicated SCN, shared SCN and hybrid SCN with 64 Kbps and 300 Kbps PMU data rates. The simulation results are comprehensively analyzed for risk hedging of the proposed SCNs with data reliability and hardware reliability. To summarize, the mean risk with data reliability (RwDR) as compared to the mean risk with hardware reliability (RwHR) increases in shared SCN and hybrid SCN by a factor of 17.108 and 23.278, respectively. However, minimum RwDR increases in shared and hybrid SCN by a factor of 16.005 and 17.717, respectively, as compared to the corresponding minimum RwHR. The overall analysis reveals that the RwDR is minimum for dedicated SCN, moderate for shared SCN, and highest for hybrid SCN.

Smart grid cyber-physical systems: communication technologies, standards and challenges

The recent developments in embedded system design and communication technologies popularized the adaption of the cyber-physical system (CPS) for practical applications. A CPS is an amalgamation of a physical system, a cyber system, and their communication network. The cyber system performs extensive computational operations on the data received from the physical devices, interprets the data, and initiates effective control actions in real-time. One such CPS is the smart grid CPS (SG-CPS) consisting of physical devices with diverse communication requirements, and intermediate communication networks. Thus, reliable communication networks are paramount for the effective operation of the SG-CPS. This paper is an elaborate survey on the communication networks from the perspective of the SG-CPS. This paper presents the state-of-art communication technologies that can meet the communication requirements of the various SG-CPS applications. The communications standards and communication protocols are also comprehensively discussed. A systematic mapping among communication technologies, standards, and protocols for various SG-CPS applications has been presented based on an extensive literature survey in this paper. Furthermore, several challenges, such as security, safety, reliability and resilience, etc., have been addressed from SG-CPS’s perspective. This work also identifies the research gaps in the various domains of the SG-CPS that can be of immense benefit to the research community.

Vulnerability Assessment of 6G-Enabled Smart Grid Cyber–Physical Systems

Next-generation wireless communication and networking technologies, such as sixth-generation (6G) networks and software-defined Internet of Things (SDIoT), make cyber-physical systems (CPSs) more vulnerable to cyberattacks. In such massively connected CPSs, an intruder can trigger a cyberattack in the form of false data injection, which can lead to system instability. To address this issue, we propose a graphics-processing-unit-enabled adaptive robust state estimator. It comprises a deep learning algorithm, long short-term memory, and a nonlinear extended Kalman filter, and is called LSTMKF. Through an SDIoT controller, it provides an online parametric state estimate. The reliability is improved by performing two levels of online parametric state estimation for secure communication and load management. The CPS under study is a 6G and SDIoT-enabled smart grid, which is tested on IEEE 14, 30, and 118 bus systems. Compared to existing techniques, the proposed algorithm is able to estimate the state variables of the system even during or after a cyberattack, with lower time complexity and high accuracy.

TOTAL: Optimal Protection Strategy Against Perfect and Imperfect False Data Injection Attacks on Power Grid Cyber–Physical Systems

This article explores the problem of protection against false data injection attacks (FDIAs) on the power system state estimation. Although many research works have been reported previously to solve the same problem, yet most of them are only for perfect FDIAs. To address the problem reasonably, all related factors influencing the success probability and corresponding attack impact of imperfect FDIAs should also be considered. Based on such considerations, a topology, parameter, accuracy, level (TOTAL) protection strategy considering all corresponding factors is proposed. The TOTAL protection strategy minimizes the attack impact of typical imperfect FDIAs (single measurement attacks) while defending against typical perfect FDIAs (single-state variable attacks). Depending on whether the protection scheme contains phasor measurement units (PMUs), we formulate the meter selection as a linear binary programming or integer programming problem, which can be solved by suitable solvers. The proposed strategy is compared with the existing methods in the literature and evaluated using the standard IEEE test cases.

Classical Failure Modes and Effects Analysis in the Context of Smart Grid Cyber-Physical Systems

Reliability assessment in traditional power distribution systems has played a key role in power system planning, design, and operation. Recently, new information and communication technologies have been introduced in power systems automation and asset management, making the distribution network even more complex. In order to achieve efficient energy management, the distribution grid has to adopt a new configuration and operational conditions that are changing the paradigm of the actual electrical system. Therefore, the emergence of the cyber-physical systems concept to face future energetic needs requires alternative approaches for evaluating the reliability of modern distribution systems, especially in the smart grids environment. In this paper, a reliability approach that makes use of failure modes of power and cyber network main components is proposed to evaluate risk analysis in smart electrical distribution systems. We introduce the application of Failure Modes and Effects Analysis (FMEA) method in future smart grid systems in order to establish the impact of different failure modes on their performance. A smart grid test system is defined and failure modes and their effects for both power and the cyber components are presented. Preventive maintenance tasks are proposed and systematized to minimize the impact of high-risk failures and increase reliability.

Security Assumptions for Ubiquitous Secure Smart Grid Infrastructure using 2 Way Peg Blockchain and Fuzzy Specifications

The smart grid cyber-physical system and advanced metering infrastructure is an immensely growing term in industrial research based on the electrical power grid system and has uncovered an efficient paradigm for the power generation, transmission, and for the expenditure management as well. These systems allow home area networks and advanced metering infrastructure to communicate bi-directionally. False data injection is a new and climacteric security threat to the power grid to tamper such important data. However, these communication channels that the energy service interfaces provide two-way communication according to the central control unit. Thus, for the security and robustness of the grid, it is critical to separate the false or infected data. The existing protocol on this problem is based on state estimation which is less secure and computationally expensive. In this paper, we propose a 2-way peg blockchain security system by providing a digital signature scheme between home area network device (Smart Meter) and AMI server to get authorization, providing an infected data detection algorithm based on fuzzy rule specification and 2WP based blockchain protocol and finally infected performance reputation updating algorithm by computing the probability distribution of uncouth level to detect the infected or compromised the. We get more efficiency and security by performing a detailed analysis.

Grid Cyber-Physical System Risk Management Model Based on Cooperative Game Theory

AbstractAdvanced grid technology represented by smart grid and energy internet is the core feature of the next-generation power grid. The next-generation power grid will be a large-scale cyber-physical system (CPS), which will have a higher level of risk management due to its flexibility in sensing and control. This paper explains the methods and results of a study on grid CPS’s behavior after risk. Firstly, a behavior model based on hybrid automata is built to simulate grid CPS’s risk decisions. Then, a GCPS risk transfer model based on cooperative game theory is built. The model allows decisions to ignore complex network structures. On this basis, a modified applicant-proposing algorithm to achieve risk optimum is proposed. The risk management model proposed in this paper can provide references for power generation and transmission decision after risk as well as risk aversion, an empirical study in north China verifies its validity.

Interactive visual analysis method for attack and defense drill of grid cyber-physical system

The open and distributed connection of the power system makes it vulnerable to various potential cyber-attacks, which may lead to power outages and even casualties. Therefore, the construction of attack and defense drill (ADD) platforms for attack mechanism investigation and protection strategy evaluation has become a research hotspot. However, for the massive and heterogeneous security analysis data generated during the drill, it is rare to have a comprehensive and intuitive method to visually and efficiently display the perspective of the attacker and defender. In order to solve this problem, this paper proposes a visual analysis scheme of an ADD framework for a grid cyber-physical system (GCPS) based on the interactive visual analysis method. Specifically, it realizes system weakness discovery based on knowledge visualization, optimization of the detection model and visualization interaction. Finally, the case study on the simulation platform of ADD proves the effectiveness of the proposed method.

A quantitative analysis model of grid cyber physical systems

Conventional power systems are being developed into grid cyber physical systems (GCPS) with widespread application of communication, computer, and control technologies. In this article, we propose a quantitative analysis method for a GCPS. Based on this, we discuss the relationship between cyberspace and physical space, especially the computational similarity within the GCPS both in undirected and directed bipartite networks. We then propose a model for evaluating the fusion of the three most important factors: information, communication, and security. We then present the concept of the fusion evaluation cubic for the GCPS quantitative analysis model. Through these models, we can determine whether a more realistic state of the GCPS can be found by enhancing the fusion between cyberspace and physical space. Finally, we conclude that the degree of fusion between the two spaces is very important, not only considering the performance of the whole business process, but also considering security.

Safety Analysis of AADL Models for Grid Cyber-Physical Systems via Model Checking of Stochastic Games

As safety-critical systems, grid cyber-physical systems (GCPSs) are required to ensure the safety of power-related systems. However, in many cases, GCPSs may be subject to uncertain and nondeterministic environmental hazards, as well as the variable quality of devices. They can cause failures and hazards in the whole system and may jeopardize system safety. Thus, it necessitates safety analysis for system safety assurance. This paper proposes an architecture-level safety analysis approach for GCPSs applying the probabilistic model-checking of stochastic games. GCPSs are modeled using Architecture Analysis and Design Language (AADL). Random errors and failures of a GCPS and nondeterministic environment behaviors are explicitly described with AADL annexes. A GCPS AADL model including the environment can be regarded as a game. To transform AADL models to stochastic multi-player games (SMGs) models, model transformation rules are proposed and the completeness and consistency of rules are proved. Property formulae are formulated for formal verification of GCPS SMG models, so that occurrence probabilities of failed states and hazards can be obtained for system-level safety analysis. Finally, a modified IEEE 9-bus system with grid elements that are power management systems is modeled and analyzed using the proposed approach.