Biblio

In today's power market, it's vital to keep electrical energy affordable to the vast majority of people while maintaining the highest degree of dependability. Due to which, the transmission network must operate beyond transfer limitations, generating congestion on transmission lines. These transmission line difficulties can be alleviated with the use of reactive power adjustment based on FACTS devices. Using a fuzzy tuned Static Synchronous Series Compensator [SSSC], this research proposes a novel method for calculating the effective damping oscillation control signals. The performance of the SSSC is compared to that of fuzzy logic-based controllers using PI controllers. According to the simulation results, the SSSC with fuzzy logic control effectively improves power flow under disrupted conditions

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.

In this paper, a novel method is proposed to assess the power system resilience considering the impacts of hurricanes. Firstly, the transmission line outage model correlated to wind speed is developed. Then, Probability Load Flow (PLF) considering the random outage of lines and the variation of loads is designed, and Latin Hypercube Sampling (LHS) is used to improve the efficiency of Monte Carlo Simulation (MCS) in solving PLF. Moreover, risk indices, including line overloading, node voltage exceeding limit, load shedding and system collapse, are established to assess the resilience of power systems during hurricanes. The method is tested with a modified IEEE 14-bus system, and simulation results indicate the effectiveness of the proposed approach.

Critical infrastructures such as the electricity grid can be severely impacted by cyber-attacks on its supply chain. Hence, having a robust cybersecurity infrastructure and management system for the electricity grid is a high priority. This paper proposes a cyber-security protocol for defense against man-in-the-middle (MiTM) attacks to the supply chain, which uses encryption and cryptographic multi-party authentication. A cyber-physical simulator is utilized to simulate the power system, control system, and security layers. The correctness of the attack modeling and the cryptographic security protocol against this MiTM attack is demonstrated in four different attack scenarios.

The outages and power shortages are common occurrences in today's world and they have a significant economic impact. These failures can be minimized by making the power grid topologically robust. Therefore, the vulnerability assessment in power systems has become a major concern. This paper considers both pure and extended topological method to analyse the vulnerability of the power system to single line failures. The lines are ranked based on four spectral graph metrics: spectral radius, algebraic connectivity, natural connectivity, and effective graph resistance. A correlation is established between all the four metrics. The impact of load uncertainty on the component ranking has been investigated. The vulnerability assessment has been done on IEEE 9-bus system. It is observed that load variation has minor impact on the ranking.

This paper conducts simulation analysis on power transmission lines and availability of power communication link based on Latin hypercube sampling. It proposes a new method of vulnerability communication link assessment for electric cyber physical system. Wind power output, transmission line failure and communication link failure of electric cyber physical system are sampled to obtain different operating states of electric cyber physical system. The connectivity of communication links under different operating states of electric cyber physical system is calculated to judge whether the communication nodes of the links are connected with the control master station. According to the connection between the link communication node and the control master station, the switching load and switching load of the electric cyber physical system in different operating states are calculated, and the optimal switching load of the electric cyber physical system in different operating states is obtained. This method can clearly identify the vulnerable link in the electric cyber physical system, so as to monitor the vulnerable link and strengthen the link strength.

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 parameters of transmission line vary with environmental and operating conditions, thus the paper proposes a digital twin-based transmission line model. Based on synchrophasor measurements from phasor measurement units, the proposed model can use the maximum likelihood estimation (MLE) to reduce uncertainty between the digital twin and its physical counterpart. A case study has been conducted in the paper to present the influence of the uncertainty in the measurements on the digital twin for the transmission line and analyze the effectiveness of the MLE method. The results show that the proposed digital twin-based model is effective in reducing the influence of the uncertainty in the measurements and improving the fault location accuracy.

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.

The widely adopted dynamic line rating (DLR) mechanism can improve the operation efficiency for industrial and commercial power systems. However, the predicted environmental parameters used in DLR bring great uncertainty to transmission line capacity estimation and may introduce system security risk if over-optimistic estimation is adopted in the operation process, which could affect the electrical safety of industrial and commercial power systems in multiple cases. Therefore, it becomes necessary to establish a system operation security assessment model to reduce the risk and provide operational guidance to enhance electrical safety. This paper aims to solve the electrical safety problems caused by the transmission line under DLR mechanism. An operation security assessment method of transmission lines considering DLR uncertainty is proposed to visualize the safety margin under the given operation strategy and optimally setting transmission line capacity while taking system safety into account. With the help of robust optimization (RO) techniques, the uncertainty is characterized and a risk-averse transmission line rating guidance can be established to determine the safety margin of line capacity for system operation. In this way, the operational security for industrial and commercial power systems can be enhanced by reducing the unsafe conditions while the operational efficiency benefit provided by DLR mechanism still exist.

Renewable energy resource plays an important role due to increasing energy claim. Power generation by PV technology is one of the fastest growing renewable energy sources due to its clean, economical and sustainable property. Grid integrated PV systems plays an important role in power generation sector. As the energy demand is increasing day by day, the power transfer capability of transmission line is increasing which leads various problems like stability, increase in fault current, congestion etc. To overcome the problem, we can use either FACTS device or battery storage or construct additional lines which is cost effective. This paper deals with grid connected PV system, which functions as PV-STATCOM. Voltage and damping control are used to elevate the power transfer capacity and to achieve regulated voltage within the limits at the point of common coupling (PCC). The studies are performed on SMIB and the simulation is carried out in MATLAB/SIMULINK environment.

In order to improve the accuracy and efficiency of transmission line safety early warning, a transmission line safety early warning system based on big data variable analysis is proposed. Firstly, the overall architecture of the system is designed under the B / S architecture. Secondly, in the hardware part of the system, the security data real-time monitoring module, data transmission module and security warning module are designed to meet the functional requirements of the system. Finally, in the system software design part, the big data variable analysis method is used to calculate the hidden danger of transmission line safety, so as to improve the effectiveness of transmission safety early warning. The experimental results show that, compared with the traditional security early warning system, the early warning accuracy and efficiency of the designed system are significantly improved, which can ensure the safe operation of the transmission line.

Natural disasters and cyber intrusions threaten the normal operation of the critical electric grid infrastructure. There is still no widely accepted methodology to quantify the resilience in power systems. In this work, power system resiliency refers to the ability of the system to keep provide energy to the critical load even with adverse events. A significant amount of work has been done to quantify the resilience for distribution systems. Even though critical loads are located in distribution system, transmission system play a critical role in supplying energy to distribution feeder in addition to the Distributed Energy Resources (DERs). This work focuses on developing a framework to quantify the resiliency of cyber-physical transmission systems. Quantifying the resiliency of the transmission network, is important to determine and devise suitable control mechanisms to minimize the effects of undesirable events in the power grid. The proposed metric is based on both system infrastructure and with changing operating conditions. A graphical analysis along with measure of critical parameters of the network is performed to quantify the redundancy and vulnerabilities in the physical network of the system. A similar approach is used to quantify the cyber-resiliency. The results indicate the capability of the proposed framework to quantify cyber-physical resilience of the transmission systems.

The power communication network generates a large amount of data. The existing security monitoring method needs to use a large transmission bandwidth in the process of data processing, which leads to the decrease of real-time response. Therefore, an automatic monitoring method of power communication network security based on edge computing is proposed. The paper establishes the power communication monitoring network architecture by combining RFID identification sensor network and wireless communication network. The edge calculation is embedded to the edge side of the power communication network, and the data processing model of power communication is established. Based on linear discriminant analysis, the paper designs a network security situation awareness assessment model, and uses this model to evaluate the real-time data collected by the power communication network. According to the evaluation results, the probability of success of intrusion attack is calculated and the security risk monitoring is carried out for the intrusion attack. The experimental results show that compared with the existing monitoring methods, the edge based security monitoring method can effectively reduce communication delay, improve the real-time response, and then improve the intelligent level of power communication network.

This paper shows the benefits of synchrophasor technology for islanding detection and resynchronization in the control room at Empresa de Transmisión Eléctrica Dominicana (ETED) in the Dominican Republic. EPG's Real Time Dynamics Monitoring System (RTDMS®) deployed at ETED was tested during operator training with the event data after an islanding event occurred on October 26, 2019, which caused the ETED System to split into two islands. RTDMS's islanding detection algorithm quickly detected and identified the event. The islanding situation was not clear for operators during the time of the event with the use of traditional SCADA tools. The use of synchophasor technology also provides valuable information for a quick and safe resynchronization. By monitoring the system frequency in each island and voltage angle differences between islands, operators can know the exact time of circuit breaker closure for a successful resynchronization. Synchrophasors allow the resynchronization in a relatively short time, avoiding the risk of additional load loss, generator outages or even a wider system blackout.

Most of today's infrastructure systems can be efficiently operated thanks to the intelligent power supply of the smart grids. However, smart grids are highly vulnerable to malicious attacks, that is, because of the interplay between the components in the smart grids, the failure of some critical components may result in the cascading failure and breakdown of the whole system. Therefore, the question of how to identify the most critical components to protect the smart grid system is the first challenge to operators. To enable the system's robustness, there has been a lot of effort aimed at the system analysis, designing new architectures, and proposing new algorithms. However, these works mainly introduce different ranking methods for link (transmission line) or node (station) identification and directly select most the highest degree nodes or common links as the critical ones. These methods fail to address the problem of interdependencies between components nor consider the role of users that is one of critical factors impacting on the smart grid vulnerability assessment. This motivates us to study a more general and practical problem in terms of smart grid vulnerability assessment, namely the Maximum-Impact through Critical-Line with Limited Budget (MICLLB) problem. The objective of this research is to provide an efficient method to identify critical components in the system by considering a realistic attack scenario.

It is important to address the physical and cyber security needs of critical infrastructures, including systems, facilities, and assets. Security requirements may include capabilities to prevent and protect against both physical and digital intrusion, hazards, threats, and incidents, and to expeditiously recover and reconstitute critical services. Energy security has serious repercussions for mission critical facilities. Mission critical facilities do not have the luxury of being able to shut down or run at a reduced capacity during outages, whether they last minutes, hours, or days. Disaster recovery plans are a necessity for mission critical facilities, involving the proper training of business continuity personnel to enact enterprise-level plans for business resiliency. Steps need to be taken to improve information security and mitigate the threat of cyber-attacks. The Smart Grid is the convergence of electric distribution systems and modern digital information technology.

The progressive expansion of renewable energies, especially wind power plants being promoted in Germany as part of the energy transition, places new demands on the transmission grid. As an alternative to grid expansion, sector coupling of the gas and electricity sector through Power-to-Gas (PtG) technology is seen as a great opportunity to make the energy transmission more flexible and reliable in the future as well as make use of already existing gas infrastructure. In this paper, PtG plants are dimensioned and placed in a model of the German transmission grid. Time series based load flow calculations are performed allowing conclusions about the line loading for the exemplary year 2016.

The transmission line tower is affected by the surface subsidence in the mined out area of coal mine, which will appear the phenomenon of subsidence, inclination and even tower collapse, threatening the operation safety of the transmission line tower in the mined out area. Therefore, a Safety and Damage Assessment Method of Transmission Line Tower in Goaf Based on Artificial Intelligence is proposed. Firstly, the geometric model of the coal seam in the goaf and the structural reliability model of the transmission line tower are constructed to evaluate the safety. Then, the random forest algorithm in artificial intelligence is used to evaluate the damage of the tower, so as to take protective measures in time. Finally, a finite element simulation model of tower foundation interaction is built, and its safety (force) and damage identification are experimentally analyzed. The results show that the proposed method can ensure high accuracy of damage assessment and reliable judgment of transmission line tower safety within the allowable error.

Cyber attacks on transmission lines are one of the main challenges in security of smart grids. These targeted attacks, if not detected, might cause cascading problems in power systems. This paper proposes a bi-level mixed integer linear programming (MILP) optimization model for false data injection on targeted buses in a power system to overflow targeted transmission lines. The upper level optimization problem outputs the optimized false data injections on targeted load buses to overflow a targeted transmission line without violating bad data detection constraints. The lower level problem integrates the false data injections into the optimal power flow problem without violating the optimal power flow constraints. A few case studies are designed to validate the proposed attack model on IEEE 118-bus power system.

The application of line current differential relays (LCDRs) to protect transmission lines has recently proliferated. However, the reliance of LCDRs on digital communication channels has raised growing cyber-security concerns. This paper investigates the impacts of false data injection attacks (FDIAs) on the performance of LCDRs. It also develops coordinated attacks that involve multiple components, including LCDRs, and can cause false line tripping. Additionally, this paper proposes a technique for detecting FDIAs against LCDRs and differentiating them from actual faults in two-terminal lines. In this method, when an LCDR detects a fault, instead of immediately tripping the line, it calculates and measures the superimposed voltage at its local terminal, using the proposed positive-sequence (PS) and negative-sequence (NS) submodules. To calculate this voltage, the LCDR models the protected line in detail and replaces the rest of the system with a Thevenin equivalent that produces accurate responses at the line terminals. Afterwards, remote current measurement is utilized by the PS and NS submodules to compute each sequence's superimposed voltage. A difference between the calculated and the measured superimposed voltages in any sequence reveals that the remote current measurements are not authentic. Thus, the LCDR's trip command is blocked. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 39-bus test system. The performance of the proposed method is also tested using an OPAL real-time simulator.

The presence of security vulnerabilities in automotive networks has already been shown by various publications in recent years. Due to the specification of the Controller Area Network (CAN) as a broadcast medium without security mechanisms, attackers are able to read transmitted messages without being noticed and to inject malicious messages. In order to detect potential attackers within a network or software system as early as possible, Intrusion Detection Systems (IDSs) are prevalent. Many approaches for vehicles are based on techniques which are able to detect deviations from specified CAN network behaviour regarding protocol or payload properties. However, it is challenging to detect attackers who secretly connect to CAN networks and do not actively participate in bus traffic. In this paper, we present an approach that is capable of successfully detecting unknown CAN devices and determining the distance (cable length) between the attacker device and our sensing unit based on Time Domain Reflectometry (TDR) technique. We evaluated our approach on a real vehicle network.

Smart power grid is a perfect model of Cyber Physical System (CPS) which is an important component for a comfortable life. The major concern of the electrical network is safety and reliable operation. A cyber attacker in the operation of power system would create a major damage to the entire power system structure and affect the continuity of the power supply by adversely changing its parameters. A risk assessment method is presented for evaluating the cyber security assessment of power systems taking into consideration the need for protection systems. The paper considers the impact of bus and transmission line protection systems located in substations on the cyber physical performance of power systems. The proposed method is to simulate the response of power systems to sudden attacks on various power system preset value and parameters. This paper focuses on the cyber attacks which occur in a co-ordinated way so that many power system components will be in risk. The risk can be modelled as the combined probability of power system impact due to attacks and of successful interruption into the system. Stochastic Petri Nets is employed for assessing the risks. The effectiveness of the proposed cyber security risk assessment method is simulated for a IEEE39 bus system.

Analyzing performance of security and stability control system is of great importance for the safe and stable operation of the power grid. Digital dynamic experimental model is built by real time digital simulation (RTDS) in order to research security and stability system of Inner Mongolia in northern 500kV transmission channel. The whole process is closed-loop dynamic real-time simulation. According to power grid network testing technology standard, all kinds of stability control devices need to be tested in a comprehensive system. Focus on the following items: security and stability control strategy, tripping criterion as well as power system low frequency oscillations. Results of the trial indicated that the simulation test platform based on RTDS have the ability of detecting the safe and stable device. It can reflect the action behavior and control characteristics of the safe and stable device accurately. The device can be used in the case of low frequency oscillation of the system.

Although there has been notable progress in modeling cascading failures in power grids, few works included using machine learning algorithms. In this paper, cascading failures that lead to massive blackouts in power grids are predicted and classified into no, small, and large cascades using machine learning algorithms. Cascading-failure data is generated using a cascading failure simulator framework developed earlier. The data set includes the power grid operating parameters such as loading level, level of load shedding, the capacity of the failed lines, and the topological parameters such as edge betweenness centrality and the average shortest distance for numerous combinations of two transmission line failures as features. Then several machine learning algorithms are used to classify cascading failures. Further, linear regression is used to predict the number of failed transmission lines and the amount of load shedding during a cascade based on initial feature values. This data-driven technique can be used to generate cascading failure data set for any real-world power grids and hence, power-grid engineers can use this approach for cascade data generation and hence predicting vulnerabilities and enhancing robustness of the grid.