Biblio

The analysis shows how important Power Network Measuring and Characterization (PSMC) is to the plan. Networks planning and oversight for the transmission of electrical energy is becoming increasingly frequent. In reaction to the current contest of assimilating trying to cut charging in the crate, estimation, information sharing, but rather govern into PSMC reasonable quantities, Electrical Transmit Monitoring and Management provides a thorough outline of founding principles together with smart sensors for domestic spying, security precautions, and control of developed broadening power systems.Electricity supply control must depend increasingly heavily on telecommunications infrastructure to manage and run their processes because of the fluctuation in transmission and distribution of electricity. A wider attack surface will also be available to threat hackers as a result of the more communications. Large-scale blackout have occurred in the past as a consequence of cyberattacks on electrical networks. In order to pinpoint the key issues influencing power grid computer networks, we looked at the network infrastructure supporting electricity grids in this research.

Industrial Control Systems (ICS) are not secure by design–with recent developments requiring them to connect to the Internet, they tend to be highly vulnerable. Additionally, attacks on critical infrastructures such as power grids and nuclear plants can cause significant damage and loss of lives. Since such attacks tend to generate anomalies in the systems, an efficient way of attack detection is to monitor the systems and identify anomalies in real-time. An automated anomaly detection tool is introduced in this paper. Additionally, the functioning of the systems is viewed as Finite State Automata. Specific sensor measurements are used to determine permissible transitions, and statistical measures such as the Interquartile Range are used to determine acceptable boundaries for the remaining sensor measurements provided by the system. Deviations from the boundaries or permissible transitions are considered as anomalies. An additional feature is the provision of a finite state automata diagram that provides the operational constraints of a system, given a set of regulated input. This tool showed a high anomaly detection rate when tested with three types of ICS. The concepts are also benchmarked against a state-of-the-art anomaly detection algorithm called Isolation Forest, and the results are provided.

With the development of the digital development transformation of the power grid, the classification of power unstructured encrypted data is an important basis for data security protection. However, most studies focus on exact match classification or single-keyword fuzzy match classification. This paper proposes a fuzzy matching classification method for power unstructured encrypted data. The data owner generates an index vector based on the power unstructured file, and the data user generates a query vector by querying the file through the same process. The index and query vector are uploaded to the cloud server in encrypted form, and the cloud server calculates the relevance score and sorts it, and returns the classification result with the highest score to the user. This method realizes the multi-keyword fuzzy matching classification of unstructured encrypted data of electric power, and through the experimental simulation of a large number of data sets, the effect and feasibility of the method are proved.

Supply chain cyberattacks that exploit insecure third-party software are a growing concern for the security of the electric power grid. These attacks seek to deploy malicious software in grid control devices during the fabrication, shipment, installation, and maintenance stages, or as part of routine software updates. Malicious software on grid control devices may inject bad data or execute bad commands, which can cause blackouts and damage power equipment. This paper describes an experimental setup to simulate the software update process of a commercial power relay as part of a hardware-in-the-loop simulation for grid supply chain cyber-security assessment. The laboratory setup was successfully utilized to study three supply chain cyber-security use cases.

This paper addresses the allocation method of offensive resources for man-made attacks on power systems considering extreme weather conditions, which can help the defender identify the most vulnerable components to protect in this adverse situation. The problem is formulated as an attacker-defender model. The attacker at the upper level intends to maximize the expected damage considering all possible line failure scenarios. These scenarios are characterized by the combinations of failed transmission lines under extreme weather. Once the disruption is detected, the defender at the lower level alters the generation and consumption in the power grid using DC optimal power flow technique to minimize the damage. Then the original bi-level problem is transformed into an equivalent single-level mixed-integer linear program through strong duality theorem and Big-M method. The proposed attack resource allocation method is applied on IEEE 39-bus system and its effectiveness is demonstrated by the comparative case studies.

One major tool of Energy Management Systems for monitoring the status of the power grid is State Estimation (SE). Since the results of state estimation are used within the energy management system, the security of the power system state estimation tool is most important. The research in this area is targeting detection of False Data Injection attacks on measurements. Though this aspect is crucial, SE also depends on database that are used to describe the relationship between measurements and systems' states. This paper presents a two-stage optimization framework to not only detect, but also correct cyber-attacks pertaining the measurements' model parameters used by the SE routine. In the first stage, an estimate of the line parameters ratios are obtained. In the second stage, the estimated ratios from stage I are used in a Bi-Level model for obtaining a final estimate of the measurements' model parameters. Hence, the presented framework does not only unify the detection and correction in a single optimization run, but also provide a monitoring scheme for the SE database that is typically considered static. In addition, in the two stages, linear programming framework is preserved. For validation, the IEEE 118 bus system is used for implementation. The results illustrate the effectiveness of the proposed model for detecting attacks in the database used in the state estimation process.

The security and reliability of power grid dispatching system is the basis of the stable development of the whole social economy. With the development of information, computer science and technology, communication technology, and network technology, using more advanced intelligent technology to improve the performance of security and reliability of power grid dispatching system has important research value and practical significance. In order to provide valuable references for relevant researchers and for the construction of future power system related applications. This paper summarizes the latest technical status of attribute encryption and hierarchical identity encryption methods, and introduces the access control method based on attribute and hierarchical identity encryption, the construction method of attribute encryption scheme, revocable CP-ABE scheme and its application in power grid data security access control. Combined with multi authorization center encryption, third-party trusted entity and optimized encryption algorithm, the parallel access control algorithm of hierarchical identity and attribute encryption and its application in power grid data security access control are introduced.

With the high-speed development of UHV power grid, the characteristics of power grid changed significantly, which puts forward new requirements for the safe operation of power grid and depend on Security and Stability Control System (SSCS) greatly. Based on the practical cases, this paper analyzes the principle of the abnormal criteria of the SSCS and its influence on the strategy of the SSCS, points out the necessity of the research on the locking strategy of the SSCS under the abnormal state. Taking the large-scale SSCS for an example, this paper analysis different control strategies of the stations in the different layered, and puts forward effective solutions to adapt different system functions. It greatly improved the effectiveness and reliability of the strategy of SSCS, and ensure the integrity of the system function. Comparing the different schemes, the principles of making the lock-strategy are proposed. It has reference significance for the design, development and implementation of large-scale SSCS.

The modernization of legacy power grids relies on the prevalence of information technology (IT). While the benefits are multi-fold and include increased reliability, more accurate monitoring, etc., the reliance on IT increases the attack surface of power grids by making them vulnerable to cyber-attacks. One of the modernization paths is the emergence of multi-terminal dc systems that offer numerous advantages over traditional ac systems. Therefore, cyber-security issues surrounding dc networks need to be investigated. Contributing to this effort, a class of false data injection attacks, called load redistribution (LR) attacks, that targets dc grids is proposed. These attacks aim to compromise the system load data and lead the system operator to dispatch incorrect power flow commands that lead to adverse consequences. Although similar attacks have been recently studied for ac systems, their feasibility in the converter-based dc grids has yet to be demonstrated. Such an attack assessment is necessary because the dc grids have a much smaller control timescale and are more dependent on IT than their traditional ac counterparts. Hence, this work formulates and evaluates dc grid LR attacks by incorporating voltage-sourced converter (VSC) control strategies that appropriately delineate dc system operations. The proposed attack strategy is solved with Gurobi, and the results show that both control and system conditions can affect the success of an LR attack.

In the process of compiling the power-cut window period of the power grid equipment maintenance plan, problems such as omission of constraints are prone to occur due to excessive reliance on manual experience. In response to these problems, this paper proposes a method for mining key features of the power-cut window based on grey relational analysis. Through mining and analysis of the historical operation data of the power grid, the operation data of new energy, and the historical power-cut information of equipment, the indicators that play a key role in the arrangement of the outage window period of the equipment maintenance plan are found. Then use the key indicator information to formulate the window period. By mining the relationship between power grid operation data and equipment power outages, this paper can give full play to the big data advantages of the power grid, improve the accuracy and efficiency of the power-cut window period.

Supervisory Control and Data Acquisition (SCADA) systems are utilized extensively in critical power grid infrastructures. Modern SCADA systems have been proven to be susceptible to cyber-security attacks and require improved security primitives in order to prevent unwanted influence from an adversarial party. One section of weakness in the SCADA system is the integrity of field level sensors providing essential data for control decisions at a master station. In this paper we propose a lightweight hardware scheme providing inferred authentication for SCADA sensors by combining an analog to digital converter and a permutation generator as a single integrated circuit. Through this method we encode critical sensor data at the time of sensing, so that unencoded data is never stored in memory, increasing the difficulty of software attacks. We show through experimentation how our design stops both software and hardware false data injection attacks occurring at the field level of SCADA systems.

For the huge charging demands of numerous electric vehicles (EVs), coordinated charging is increasing in power grid. However, since connected with public networks, the coordinated charging control system is in a low-level cyber security and greatly vulnerable to malicious attacks. This paper investigates the malicious mode attack (MMA), which is a new cyber-attack pattern that simultaneously attacks massive EV charging piles to generate continuous sinusoidal power disturbance with the same frequency as the poorly-damped wide-area electromechanical mode. Thereby, high amplitude forced oscillations are stimulated by MMA, which seriously threats the stability of power systems and the power supply of charging stations. The potential threat of MMA is clarified by investigating the vulnerability of the IoT-based coordinated charging load control system, and an MMA process like Mirai is pointed out as an example. An MMA model is established for impact analysis. A hardware test platform is built for the verification of the MMA model. Test result verified the existence of MMA and the accuracy of the MMA model.

Despite the fact that the power grid is typically regarded as a relatively stable system, outages and electricity shortages are common occurrences. Grid security is mainly dependent on accurate vulnerability assessment. The vulnerability can be assessed in terms of topology-based metrics and flow-based metrics. In this work, power flow analysis is used to calculate the metrics under single line contingency (N-1) conditions. The effect of load uncertainty on system vulnerability is checked. The IEEE 30 bus power network has been used for the case study. It has been found that the variation in load demand affects the system vulnerability.

In order to solve the problem of untargeted data security grading methods in the process of power grid data governance, this paper analyzes the mainstream data security grading standards at home and abroad, investigates and sorts out the characteristics of power grid data security grading requirements, and proposes a method that considers national, social, and A grid data security classification scheme for the security impact of four dimensions of individuals and enterprises. The plan determines the principle of power grid data security classification. Based on the basic idea of “who will be affected to what extent and to what extent when the power grid data security is damaged”, it defines three classification factors that need to be considered: the degree of impact, the scope of influence, and the objects of influence, and the power grid data is divided into five security levels. In the operation stage of power grid data security grading, this paper sorts out the experience and gives the recommended grading process. This scheme basically conforms to the status quo of power grid data classification, and lays the foundation for power grid data governance.

At present, the black-start mode of the large power grid is mostly limited to relying on the black-start power supply inside the system, or only to the recovery mode that regards the transmission power of tie lines between systems as the black-start power supply. The starting power supply involved in the situation of the large power outage is incomplete and it is difficult to give full play to the respective advantages of internal and external power sources. In this paper, a method of coordinated black-start of large power grid internal and external power sources is proposed by combining the two modes. Firstly, the black-start capability evaluation system is built to screen out the internal black-start power supply, and the external black-start power supply is determined by analyzing the connection relationship between the systems. Then, based on the specific implementation principles, the black-start power supply coordination strategy is formulated by using the Dijkstra shortest path algorithm. Based on the condensation idea, the black-start zoning and path optimization method applicable to this strategy is proposed. Finally, the black-start security verification and corresponding control measures are adopted to obtain a scheme of black-start cooperation between internal and external power sources in the large power grid. The above method is applied in a real large power grid and compared with the conventional restoration strategy to verify the feasibility and efficiency of this method.

We present a scalable optimization algorithm and its parallel implementation for two-stage stochastic programming problems of large-scale, particularly the security constrained optimal power flow models routinely used in electrical power grid operations. Such problems can be prohibitively expensive to solve on industrial scale with the traditional methods or in serial. The algorithm decomposes the problem into first-stage and second-stage optimization subproblems which are then scheduled asynchronously for efficient evaluation in parallel. Asynchronous evaluations are crucial in achieving good balancing and parallel efficiency because the second-stage optimization subproblems have highly varying execution times. The algorithm employs simple local second-order approximations of the second-stage optimal value functions together with exact first- and second-order derivatives for the first-stage subproblems to accelerate convergence. To reduce the number of the evaluations of computationally expensive second-stage subproblems required by line search, we devised a flexible mechanism for controlling the step size that can be tuned to improve performance for individual class of problems. The algorithm is implemented in C++ using MPI non-blocking calls to overlap computations with communication and boost parallel efficiency. Numerical experiments of the algorithm are conducted on Summit and Lassen supercomputers at Oak Ridge and Lawrence Livermore National Laboratories and scaling results show good parallel efficiency.

With the construction of power information network, the power grid has built a security protection system based on boundary protection. However, with the continuous advancement of the construction of the power Internet of Things, a large number of power Internet of Things terminals need to connect to the power information network through the public network, which have an impact on the existing security protection system of the power grid. This article analyzes the characteristics of the border protection model commonly used in network security protection. Aiming at the lack of security protection capabilities of this model, a zero-trust security architecture-based power Internet of Things network security protection model is proposed. Finally, this article analyzes and studies the application of zero trust in the power Internet of Things.

At present, the security protection of distribution automation system is faced with complex and diverse operating environment, and the main use of public network may bring greater security risks, there are still some deficiencies. According to the actual situation of distribution automation of China Southern Power Grid, this paper designs multiple protection technology, carries out encryption distribution terminal research, and realizes end-to-end longitudinal security protection of distribution automation system, which is effectively improving the anti-attack ability of distribution terminal.

The security of the power grid is the first element of its operation. This paper aims at finding the vulnerability nodes in the power grid to prevent it from being destroyed. A novel comprehensive vulnerability index is proposed to the singleness of evaluation indicators for existing literature by integrating the power grid's topology information and operating state. Taking IEEE-118 as an example, the simulation analysis proves that the proposed vulnerability index has certain discriminative advantages and the best weighting factor is obtained through correlation analysis.

As an important part of the intelligent measurement system, IOT terminal is in the “edge” layer of the intelligent measurement system architecture. It is the key node of power grid management and cloud fog integration. Its information security is the key to the construction of the security system of intelligent measurement, and the security link between the cloud and sensor measurement. With the in-depth integration of energy flow, information flow and business flow, and the in-depth application of digital technologies such as cloud computing, big data, internet of things, mobile Internet and artificial intelligence, the transformation and development of power system to digital and high-quality digital power grid has been accelerated. As a typical multi-dimensional complex system combining physical space and information space, the security threats and risks faced by the digital grid are more complex. The security risks in the information space will transfer the hazards to the power system and physical space. The Internet of things terminal is facing a more complex situation in the security field than before. This paper studies the feasibility of the security monitoring technology of the Internet of things terminal, in order to reduce the potential risks, improve the safe operation environment of the Internet of things terminal and improve the level of the security protection of the Internet of things terminal. One is to study the potential security problems of Internet of things terminal, and put forward the technical specification of security protection of Internet of things terminal. The second is to study the Internet of things terminal security detection technology, research and develop terminal security detection platform, and realize the unified detection of terminal security protection. The third is to study the security monitoring technology of the Internet of things terminal, develop the security monitoring system of the Internet of things terminal, realize the terminal security situation awareness and threat identification, timely discover the terminal security vulnerabilities, and ensure the stable and safe operation of the terminal and related business master station.

The power cyber-physical system combines the cyber network with the traditional electrical power network, which can monitor and control the operation of the power grid stably and efficiently. Since the system's structure and function is complicated and large, it becomes fragile as a result. Therefore, establishing a reasonable and effective CPS model and discussing its vulnerability performance under external attacks is essential and vital for power grid operation. This paper uses the theory of complex networks to establish a independent system model by IEEE-118-node power network and 200-node scale-free information network, introducing information index to identify and sort important nodes in the network, and then cascade model of the power cyber-physical system based on the node load capacity is constructed and the vulnerability assessment analysis is carried out. The simulation shows that the disintegration speed of the system structure under deliberate attacks is faster than random attacks; And increasing the node threshold can effectively inhibit the propagation of failure.

As a large amount of renewable energy is injected into the power grid, the source side of the power grid becomes extremely uncertain. Traditional static safety analysis methods based on pure physical models can no longer quickly and reliably give analysis results. Therefore, this paper proposes a deep learning-based static security analytical method. First, the static security assessment index of the power grid under the N-1 principle is proposed. Secondly, a neural network model and its input and output data for static safety analysis problems are designed. Finally, the validity of the proposed method was verified by IEEE grid data. Experiments show that the proposed method can quickly and accurately give the static security analysis results of the source-side high uncertainty grid.

Recent work on intrusion-tolerance has shown that resilience to sophisticated network attacks requires system replicas to be deployed across at least three geographically distributed sites. While commodity data centers offer an attractive solution for hosting these sites due to low cost and management overhead, their use raises significant confidentiality concerns: system operators may not want private data or proprietary algorithms exposed to servers outside their direct control. We present a new model for Byzantine Fault Tolerant replicated systems that moves toward “intrusion tolerance as a service”. Under this model, application logic and data are only exposed to servers hosted on the system operator's premises. Additional offsite servers hosted in data centers can support the needed resilience without executing application logic or accessing unencrypted state. We have implemented this approach in the open-source Spire system, and our evaluation shows that the performance overhead of providing confidentiality can be less than 4% in terms of latency.

Affected by the inherent ideas like "Focus on Function Realization, Despise Security Protection", there are lots of hidden threats in the industrial control system of wind farms (ICS-WF), such as unreasonable IP configuration, failure in virus detection and killing, which are prone to illegal invasion and attack from the cyberspace. Those unexpected unauthorized accesses are quite harmful for the stable operation of the wind farms and regional power grid. Therefore, by investigating the current security situation and needs of ICS-WF, analyzing the characteristics of ICS-WF’s architecture and internal communication, and integrating the ideas of the classified protection of cybersecurity, this paper proposes a new customized cybersecurity strategy for ICS-WF based on the barrel theory. We also introduce an new anomalous intrusion detection technology for ICS-WF, which is developed based on statistical models of wind farm network characteristics. Finally, combined all these work with the network security offense and defense drill in the industrial control safety simulation laboratory of wind farms, this research formulates a three-dimensional comprehensive protection solution for ICS-WF, which significantly improves the cybersecurity level of ICS-WF.

The power grid has high requirements for data security, and data security audit technology is facing challenges. Because the server in the power grid operating environment is considered untrustworthy and does not have the authority to obtain the secret key, the encrypted data cannot be parsed and the data processing ability of the data center is restricted. In response to the above problems, the power grid database encryption system was designed, and the access control module and the encryption module that should be written based on SQL statements were explained. The database encryption system was developed using the Java language and deployed in the cloud environment. Finally, the method was proved by experiments. feasibility.