Biblio

The electrical grid connects all the generating stations to supply uninterruptible power to the consumers. With the advent of technology, smart sensors and communication are integrated with the existing grid to behave like a smart system. This smart grid is a two-way communication that connects the consumers and producers. It is a connected smart network that integrates electricity generation, transmission, substation, distribution, etc. In this smart grid, clean, reliable power with a high-efficiency rate of transmission is available. In this paper, a highly efficient smart management system of a smart grid with overall protection is proposed. This management system checks and monitors the parameters periodically. This future technology also develops a smart transformer with ac and dc compatibility, for self-protection and for the healing process.

The most vital requirement for the electric power system as a critical infrastructure is its security of supply. In course of the transition of the electric energy system, however, the security provided by the N-1 principle increasingly reaches its limits. The IT/OT convergence changes the threat structure significantly. New risk factors, that can lead to major blackouts, are added to the existing ones. The problem, however, the cost of security optimizations are not always in proportion to their value. Not every component is equally critical to the energy system, so the question arises, "How secure does my system need to be?". To adress the security-by-design principle, this contribution introduces a Security Metric (SecMet) that can be applied to Smart Grid architectures and its components and deliver an indicator for the "Securitisation Need" based on an individual risk assessment.

The security of Energy Data collection is the basis of achieving reliability and security intelligent of smart grid. The newest security communication of Data collection is Zero Trust communication; The Strategy of Zero Trust communication is that don’t trust any device of outside or inside. Only that device authenticate is successful and software and hardware is more security, the Energy intelligent power system allow the device enroll into network system, otherwise deny these devices. When the device has been communicating with the Energy system, the Zero Trust still need to detect its security and vulnerability, if device have any security issue or vulnerability issue, the Zero Trust deny from network system, it ensures that Energy power system absolute security, which lays a foundation for the security analysis of intelligent power unit.

Electrical load forecasting is an essential part of the smart grid to maintain a stable and reliable grid along with helping decisions for economic planning. With the integration of more renewable energy resources, especially solar photovoltaic (PV), and transitioning into a prosumer-based grid, electrical load forecasting is deemed to play a crucial role on both regional and household levels. However, most of the existing forecasting methods can be considered black-box models due to deep digitalization enablers, such as Deep Neural Networks (DNN), where human interpretation remains limited. Additionally, the black box character of many models limits insights and applicability. In order to mitigate this shortcoming, eXplainable Artificial Intelligence (XAI) is introduced as a measure to get transparency into the model’s behavior and human interpretation. By utilizing XAI, experienced power market and system professionals can be integrated into developing the data-driven approach, even without knowing the data science domain. In this study, an electrical load forecasting model utilizing an XAI tool for a Norwegian residential building was developed and presented.

This paper proposes an intelligent line congestion prognosis scheme based on wide-area measurements, which accurately identifies an impending congestion and the problem causing the congestion. Due to the increasing penetration of renewable energy resources and uncertainty of load/generation patterns in the Active Distribution Networks (ADNs), power line congestion is one of the issues that could happen during peak load conditions or high-power injection by renewable energy resources. Congestion would have devastating effects on both the economical and technical operation of the grid. Hence, it is crucial to accurately predict congestions to alleviate the problem in-time and command proper control actions; such as, power redispatch, incorporating ancillary services and energy storage systems, and load curtailment. We use neural network methods in this work due to their outstanding performance in predicting the nonlinear behavior of the power system. Bayesian Regularization, along with Levenberg-Marquardt algorithm, is used to train the proposed neural networks to predict an impending congestion and its cause. The proposed method is validated using the IEEE 13-bus test system. Utilizing the proposed method, extreme control actions (i.e., protection actions and load curtailment) can be avoided. This method will improve the distribution grid resiliency and ensure the continuous supply of power to the loads.

Conventional systems has to be updated as the technology advances at quick pace. A smart grid is a renovated and digitalized version of a standard electrical infrastructure that allows two-way communication between customers and the utility, which overcomes huge manual hustle. Advanced Metering Infrastructure plays a major role in a smart grid by automatically reporting the power consumption readings to the utility through communication networks. However, there is always a trade-off. Security of AMI communication is a major problem that must be constantly monitored if this technology is to be fully utilized. This paper mainly focuses on developing a virtual setup of fully functional smart meter and a web application for generating electricity bill which allows consumer to obtain demand response, where the data is managed at server side. It also focuses on analyzing the potential security concerns posed by MITM-Arp-spoofing attacks on AMI systems and session hijacking attacks on web interfaces. This work also focusses on mitigating the vulnerabilities of session hijacking on web interface by restricting the cookies so that the attacker is unable to acquire any confidential data.

The critical nature of smart grids (SGs) attracts various network attacks and malicious manipulations. Existent SG solutions are less capable of ensuring secure and trustworthy operation. This is due to the large-scale nature of SGs and reliance on network protocols for trust management. A particular example of such severe attacks is the false data injection (FDI). FDI refers to a network attack, where meters' measurements are manipulated before being reported in such a way that the energy system takes flawed decisions. In this paper, we exploit the secure nature of blockchains to construct a data management framework based on public blockchain. Our framework enables trustworthy data storage, verification, and exchange between SG components and decision-makers. Our proposed system enables miners to invest their computational power to verify blockchain transactions in a fully distributed manner. The mining logic employs machine learning (ML) techniques to identify the locations of compromised meters in the network, which are responsible for generating FDI attacks. In return, miners receive virtual credit, which may be used to pay their electric bills. Our design circumvents single points of failure and intentional FDI attempts. Our numerical results compare the accuracy of three different ML-based mining logic techniques in two scenarios: focused and distributed FDI attacks for different attack levels. Finally, we proposed a majority-decision mining technique for the practical case of an unknown FDI attack level.

We use discrete-time adaptive control theory to design a novel false data injection (FDI) attack against automatic generation control (AGC), a critical system that maintains a power grid at its requisite frequency. FDI attacks can cause equipment damage or blackouts by falsifying measurements in the streaming sensor data used to monitor the grid's operation. Compared to prior work, the proposed attack (i) requires less knowledge on the part of the attacker, such as correctly forecasting the future demand for power; (ii) is stealthier in its ability to bypass standard methods for detecting bad sensor data and to keep the false sensor readings near historical norms until the attack is well underway; and (iii) can sustain the frequency excursion as long as needed to cause real-world damage, in spite of AGC countermeasures. We validate the performance of the proposed attack on realistic 37-bus and 118-bus setups in PowerWorld, an industry-strength power system simulator trusted by real-world operators. The results demonstrate the attack's improved stealthiness and effectiveness compared to prior work.

The increasing penetration of cyber systems into smart grids has resulted in these grids being more vulnerable to cyber physical attacks. The central challenge of higher order cyber-physical contingency analysis is the exponential blow-up of the attack surface due to a large number of attack vectors. This gives rise to computational challenges in devising efficient attack mitigation strategies. However, a system operator can leverage private information about the underlying network to maintain a strategic advantage over an adversary equipped with superior computational capability and situational awareness. In this work, we examine the following scenario: A malicious entity intrudes the cyber-layer of a power network and trips the transmission lines. The objective of the system operator is to deploy security measures in the cyber-layer to minimize the impact of such attacks. Due to budget constraints, the attacker and the system operator have limits on the maximum number of transmission lines they can attack or defend. We model this adversarial interaction as a resource-constrained attacker-defender game. The computational intractability of solving large security games is well known. However, we exploit the approximately modular behaviour of an impact metric known as the disturbance value to arrive at a linear-time algorithm for computing an optimal defense strategy. We validate the efficacy of the proposed strategy against attackers of various capabilities and provide an algorithm for a real-time implementation.

In order to solve the problem of data analysis and application caused by the inefficient integration of hardware and software compatibility of hardware in the Internet of things, this paper proposes and designs a C/S framework communication system based on task driven and multi-user authority. By redefining the relationship between users and hardware and adopting the matching framework for different modules, the system realizes the high concurrent and complex data efficient collaborative processing between software and hardware. Finally, by testing and verifying the functions of the system, the communication system effectively realizes the functions of data processing between software and hardware, and achieves the expected results.

In modern power grids (PGs), load frequency control (LFC) is effectively employed to preserve the frequency within the allowable ranges. However, LFC dependence on information and communication technologies (ICTs) makes PGs vulnerable to cyber attacks. Manipulation of measured data and control commands known as false data injection attacks (FDIAs) can negatively affect grid frequency performance and destabilize PG. This paper investigates the frequency performance of an isolated PG under coordinated FDIAs. A control scheme based on the combination of a Kalman filter, a chi-square detector, and a linear quadratic Gaussian controller is proposed to detect and mitigate the coordinated FDIAs. The efficiency of the proposed control scheme is evaluated under two types of scaling and exogenous FDIAs. The simulation results demonstrate that the proposed control scheme has significant capabilities to detect and mitigate the designed FDIAs.

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.

Power grids are the most extensive man-made systems that are difficult to control and monitor. With the development of conventional power grids and moving toward smart grids, power systems have undergone vast changes since they use the Internet to transmit information and control commands to different parts of the power system. Due to the use of the Internet as a basic infrastructure for smart grids, attackers can sabotage the communication networks and alter the measurements. Due to the complexity of the smart grids, it is difficult for the network operator to detect such cyber-attacks. The attackers can implement the attack in a manner that conventional Bad Data detection (BDD) systems cannot detect since it may not violate the physical laws of the power system. This paper uses the cross wavelet transform (XWT) to detect stealth false data injections attacks (FDIAs) against state estimation (SE) systems. XWT can capture the coherency between measurements of adjacent buses and represent it in time and frequency space. Then, we train a machine learning classification algorithm to distinguish attacked measurements from normal measurements by applying a feature extraction technique.

With the increasing use of information and communication technology in electrical power grids, the security of energy supply is increasingly threatened by cyber-attacks. Traditional cyber-security measures, such as firewalls or intrusion detection/prevention systems, can be used as mitigation and prevention measures, but their effective use requires a deep understanding of the potential threat landscape and complex attack processes in energy information systems. Given the complexity and lack of detailed knowledge of coordinated, timed attacks in smart grid applications, we need information and insight into realistic attack scenarios in an appropriate and practical setting. In this paper, we present a man-in-the-middle-based attack scenario that intercepts process communication between control systems and field devices, employs false data injection techniques, and performs data corruption such as sending false commands to field devices. We demonstrate the applicability of the presented attack scenario in a physical smart grid laboratory environment and analyze the generated data under normal and attack conditions to extract domain-specific knowledge for detection mechanisms.

As a modern power transmission network, smart grid connects abundant terminal devices and plays an important role in our daily life. However, along with its growth are the security threats. Different from the separated environment previously, an adversary nowadays can destroy the power system by attacking its terminal devices. As a result, it's critical to ensure the security and safety of terminal devices. To achieve it, detecting the pre-existing vulnerabilities in the terminal program and enhancing its security, are of great importance and necessity. In this paper, we introduce Cker, a novel vulnerability detection tool for smart grid devices, which generates an program model based on device sources and sets rules to perform model checking. We utilize the static analysis to extract necessary information and build corresponding program models. By further checking the model with pre-defined vulnerability patterns, we achieve security detection and error reporting. The evaluation results demonstrate that our method can effectively detect vulnerabilities in smart devices with an acceptable accuracy and false positive rate. In addition, as Cker is realized by pure python, it can be easily scaled to other platforms.

Smart grids can be exposed to relay attacks (or wormhole attacks) resulting from weaknesses in cryptographic operations such as authentication and key derivation associated with process automation protocols. Relay attacks refer to attacks in which authentication is evaded without needing to attack the smart grid itself. By using a universal composability model that provides a strong security notion for designing cryptographic operations, we formulate the necessary relay resilience settings for strengthening authentication and key derivation and enhancing relay security in process automation protocols in this paper. We introduce R-Chain, a universally composable relay resilience framework that prevents bypass of cryptographic operations. Our framework provides an ideal chaining functionality that integrates all cryptographic operations such that all outputs from a preceding operation are used as input to the subsequent operation to support relay resilience. We apply R-Chain to provide relay resilience in a practical smart grid process automation protocol, namely WirelessHART.

Smart grids are one of the most important applications of cyber-physical systems. They intelligently transmit energy to customers by information technology, and have replaced the traditional power grid and are widely used. However, smart grids are vulnerable to cyber-attacks. Once attacked, it will cause great losses and lose the trust of customers. Therefore, it is important to evaluate the trustworthiness of smart grids. In order to evaluate the trustworthiness of smart grids, this paper uses a generalized stochastic Petri net (GSPN) to model smart grids. Considering various security threats that smart grids may face, we propose a general GSPN model for smart grids, which evaluates trustworthiness from three metrics of reliability, availability, and integrity by analyzing steady-state and transient probabilities. Finally, we obtain the value of system trustworthiness and simulation results show that the feasibility and effectiveness of our model for smart grids trustworthiness.

Rapid development of wireless technologies has driven the evolution of smart grid application. In smart grid, authentication plays an important role for secure communication between smart meter and service provider. Hence, the design of secure authenticated key agreement schemes has received significant attention from researchers. In these schemes, a trusted third party directly participates in key agreement process. Although, this third party is assumed as trusted, however we cannot reject the possibility that being a third party, it can also be malicious. In the existing works, either the established session key is revealed to the agents of a trusted third party, or a trusted third party agent can impersonate the smart meter and establish a valid session key with the service provider, which is likely to cause security vulnerabilities. Therefore, there is a need to design a secure authentication scheme so that only the deserving entities involved in the communication can establish and know the session key. This paper proposes a new secure authenticated key agreement scheme for smart grid considering the fact that the third party can also be malicious. The security of the proposed scheme has been thoroughly evaluated using an adversary model. Correctness of the scheme has been analyzed using the broadly accepted Burrows-Abadi-Needham (BAN) Logic. In addition, the formal security verification of the proposed scheme has been performed using the widely accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool. Results of this simulation confirm that the proposed scheme is safe. Detailed security analysis shows the robustness of the scheme against various known attacks. Moreover, the comparative performance study of the proposed scheme with other relevant schemes is presented to demonstrate its practicality.

5G smart grid applications rely on its high-performance transmission and bearer network. With the help of complex network theory, this paper first analyzes the complex network characteristic parameters of 5G smart grid, and explains the necessity and supporting significance of network vulnerability analysis for efficient transmission of 5G network. Then the node importance analysis algorithm based on node degree and clustering coefficient (NIDCC) is proposed. According to the results of simulation analysis, the power network has smaller path length and higher clustering coefficient in terms of static parameters, which indicates that the speed and breadth of fault propagation are significantly higher than that of random network. It further shows the necessity of network vulnerability analysis. By comparing with the other two commonly used algorithms, we can see that NIDCC algorithm can more accurately estimate and analyze the weak links of the network. It is convenient to carry out the targeted transformation of the power grid and the prevention of blackout accidents.

Modern smart grid systems are heavily dependent on Information and Communication Technology, and this dependency makes them prone to cyber-attacks. The occurrence of a cyber-attack has increased in recent years resulting in substantial damage to power systems. For a reliable and stable operation, cyber protection, control, and detection techniques are becoming essential. Automated detection of cyberattacks with high accuracy is a challenge. To address this, we propose a two-layer hierarchical machine learning model having an accuracy of 95.44 % to improve the detection of cyberattacks. The first layer of the model is used to distinguish between the two modes of operation - normal state or cyberattack. The second layer is used to classify the state into different types of cyberattacks. The layered approach provides an opportunity for the model to focus its training on the targeted task of the layer, resulting in improvement in model accuracy. To validate the effectiveness of the proposed model, we compared its performance against other recent cyber attack detection models proposed in the literature.

Modern electric power systems are complex cyber-physical systems. The integration of traditional power and digital technologies result in interdependencies that need to be considered in risk analysis. In this paper we argue the need for analysis methods that can combine the competencies of various experts in a common analysis focusing on the overall system perspective. We report on our experiences on using the Vulnerability Analysis Framework (VAF) and bow-tie diagrams in a combined analysis of the power and cyber security aspects in a realistic case. Our experiences show that an extended version of VAF with increased support for interdependencies is promising for this type of analysis.

The false data injection attacks (FDIAs) in smart grids can offset the power measurement data and it can bypass the traditional bad data detection mechanism. To solve this problem, a new detection mechanism called cosine similarity ratio which is based on the dynamic estimation algorithm of square root cubature Kalman filter (SRCKF) is proposed in this paper. That is, the detection basis is the change of the cosine similarity between the actual measurement and the predictive measurement before and after the attack. When the system is suddenly attacked, the actual measurement will have an abrupt change. However, the predictive measurement will not vary promptly with it owing to the delay of Kalman filter estimation. Consequently, the cosine similarity between the two at this moment has undergone a change. This causes the ratio of the cosine similarity at this moment and that at the initial moment to fluctuate considerably compared to safe operation. If the detection threshold is triggered, the system will be judged to be under attack. Finally, the standard IEEE-14bus test system is used for simulation experiments to verify the effectiveness of the proposed detection method.

State estimation algorithm ensures an effective realtime monitoring of the modern smart grid leading to an accurate determination of the current operating states. Recently, a new genre of data integrity attacks namely false data injection attack (FDIA) has shown its deleterious effects by bypassing the traditional bad data detection technique. Modern grid operators must detect the presence of such attacks in the raw field measurements to guarantee a safe and reliable operation of the grid. State forecasting based FDIA identification schemes have recently shown its efficacy by determining the deviation of the estimated states due to an attack. This work emphasizes on a scalable deep learning state forecasting model which can accurately determine the presence of FDIA in real-time. An optimal set of hyper-parameters of the proposed architecture leads to an effective forecasting of the operating states with minimal error. A diligent comparison between other state of the art forecasting strategies have promoted the effectiveness of the proposed neural network. A comprehensive analysis on the IEEE 14 bus test bench effectively promotes the proposed real-time attack identification strategy.

The Internet of Things (IoT) paradigm, with its highly distributed and interconnected architecture, is gaining ground in Industry 4.0 and in critical infrastructures like the eHealth sector, the Smart Grid, Intelligent Power Plants and Smart Mobility. In these critical sectors, the preservation of metrological characteristics and their traceability is a strong legal requirement, just like cyber-security, since it offers the ground for liability. Any vulnerability in the system in which the metrological network is embedded can endanger human lives, the environment or entire economies. This paper presents a framework comprised of a methodology and some tools for the governance of the metrological chain. The proposed methodology combines the RAMI 4.0 model, which is a Reference Architecture used in the field of Industrial Internet of Things (IIoT), with the the Reference Model for Information Assurance & Security (RMIAS), a framework employed to guarantee information assurance and security, merging them with the well established paradigms to preserve calibration and referability of metrological instruments. Thus, metrological traceability and cyber-security are taken into account straight from design time, providing a conceptual space to achieve security by design and to support the maintenance of the metrological chain over the entire system lifecycle. The framework lends itself to be completely automatized with Model Checking to support automatic detection of non conformity and anomalies at run time.

We propose a localized oscillation amplitude monitoring (OAM) method for the mitigation of cyber threats directed at the wide area control (WAC) system used to coordinate control of Flexible AC Transmission Systems (FACTS) for power oscillation damping (POD) of active power flow on inter-area tie lines. The method involves monitoring the inter-area tie line active power oscillation amplitude over a sliding window. We use system instability - inferred from oscillation amplitudes growing instead of damping - as evidence of an indication of a malfunction in the WAC of FACTS, possibly indicative of a cyber attack. Monitoring the presence of such a growth allows us to determine whether any destabilizing behaviors appear after the WAC system engages to control the POD. If the WAC signal increases the oscillation amplitude over time, thereby diminishing the POD performance, the FACTS falls back to POD using local measurements. The proposed method does not require an expansive system-wide view of the network. We simulate replay, control integrity, and timing attacks for a test system and present results that demonstrate the performance of the OAM method for mitigation.