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.

Cyberattack on power system brings new challenges on the development of modern power system. Hackers may implement false data injection attack (FDIA) to cause unstable operating conditions of the power system. However, data from different power internet of things usually contains a lot of redundancy, making it difficult for current efficient discriminant model to precisely identify FDIA. To address this problem, we propose a deep learning network-based data fusion model to handle features from measurement data in power system. Proposed model includes a data enrichment module and a data fusion module. We firstly employ feature engineering technique to enrich features from power system operation in time dimension. Subsequently, a long short-term memory based autoencoder (LSTM-AE) is designed to efficiently avoid feature space explosion problem during data enriching process. Extensive experiments are performed on several classical attack detection models over the load data set from IEEE 14-bus system and simulation results demonstrate that fused data from proposed model shows higher detection accuracy with respect to the raw data.

Cyber-Physical Power System (CPPS) is one of the most critical infrastructure systems due to deep integration between power grids and communication networks. In the power system, cascading failure is spreading more readily in CPPS, even leading to blackouts as well as there are new difficulties with the power system security simulation and faults brought by physical harm or network intrusions. The current study summarized the cross- integration of several fields such as computer and cyberspace security in terms of the robustness of Cyber-Physical Systems, viewed as Interconnected and secure network systems. Therefore, the security events that significantly influenced the power system were evaluated in this study, besides the challenges and future directions of power system security simulation technologies were investigated for posing both challenges and opportunities for simulation techniques of power system security like building a new power system to accelerate the transformation of the existing energy system to a clean, low-carbon, safe, and efficient energy system which is used to assure power system stability through fusion systems that combine the cyber-physical to integrate the battery power station, power generation and renewable energy resources through the internet with the cyber system that contains Smart energy system control and attacks.

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.

Modern day grid operation requires multiple interlinked applications and many automated processes at control center for monitoring and operation of grid. Information technology integrated with operational technology plays a critical role in grid operation. Computing resource requirements of these software applications varies widely and includes high processing applications, high Input/Output (I/O) sensitive applications and applications with low resource requirements. Present day grid operation control center uses various applications for load despatch schedule management, various real-time analytics & optimization applications, post despatch analysis and reporting applications etc. These applications are integrated with Operational Technology (OT) like Data acquisition system / Energy management system (SCADA/EMS), Wide Area Measurement System (WAMS) etc. This paper discusses various design considerations and implementation of converged infrastructure through virtualization technology by consolidation of servers and storages using multi-cluster approach to meet high availability requirement of the applications and achieve desired objectives of grid control center of north eastern region in India. The process involves weighing benefits of different architecture solution, grouping of application hosts, making multiple clusters with reliability and security considerations, and designing suitable infrastructure to meet all end objectives. Reliability, enhanced resource utilization, economic factors, storage and physical node selection, integration issues with OT systems and optimization of cost are the prime design considerations. Modalities adopted to minimize downtime of critical systems for grid operation during migration from the existing infrastructure and integration with OT systems of North Eastern Regional Load Despatch Center are also elaborated in this paper.

With the reform of the power market(PM), various power applications based on computer networks have also developed. As a network application system supporting the operation of the PM, the technical support system(TSS) of the PM has become increasingly important for its network information security(NIS). The purpose of this article is to study the security protection of computer network information in power systems. This paper proposes an identity authentication algorithm based on digital signatures to verify the legitimacy of system user identities; on the basis of PMI, according to the characteristics of PM access control, a role-based access control model with time and space constraints is proposed, and a role-based access control model is designed. The access control algorithm based on the attribute certificate is used to manage the user's authority. Finally, according to the characteristics of the electricity market data, the data security transmission algorithm is designed and the feasibility is verified. This paper presents the supporting platform for the security test and evaluation of the network information system, and designs the subsystem and its architecture of the security situation assessment (TSSA) and prediction, and then designs the key technologies in this process in detail. This paper implements the subsystem of security situation assessment and prediction, and uses this subsystem to combine with other subsystems in the support platform to perform experiments, and finally adopts multiple manifestations, and the trend of the system's security status the graph is presented to users intuitively. Experimental studies have shown that the residual risks in the power system after implementing risk measures in virtual mode can reduce the risk value of the power system to a fairly low level by implementing only three reinforcement schemes.

Aiming at the problem of low detection accuracy in traditional power system network intrusion detection methods, in order to improve the performance of power system network intrusion detection, a power system network intrusion detection method based on random forest algorithm is proposed. Firstly, the power system network intrusion sub sample is selected to construct the random forest decision tree. The random forest model is optimized by using the edge function. The accuracy of the vector is judged by the minimum state vector of the power system network, and the measurement residual of the power system network attack is calculated. Finally, the power system network intrusion data set is clustered by Gaussian mixture clustering Through the design of power system network intrusion detection process, the power system network intrusion detection is realized. The experimental results show that the power system network intrusion detection method based on random forest algorithm has high network intrusion detection performance.

To ensure the safe operation of power system, this paper studies two technologies of data encryption and digital signature, and designs a real-time data encryption system based on DES algorithm, which improves the security of data network communication. The real-time data encryption system of power system is optimized by the hybrid encryption system based on DES algorithm. The real-time data encryption of power system adopts triple DES algorithm, and double DES encryption algorithm of RSA algorithm to ensure the security of triple DES encryption key, which solves the problem of real-time data encryption management of power system. Java security packages are used to implement digital signatures that guarantee data integrity and non-repudiation. Experimental results show that the data encryption system is safe and effective.

With the construction of State Grid informatization, professional data such as operation inspection, marketing, and regulation have gradually shifted from offline to online. In recent years, cyberspace security incidents have occurred frequently, and national and group cybersecurity threats have emerged. As the next-generation communication system, quantum security has to satisfy the security requirements. Also, it is especially important to build the fusion application of energy network quantum private communication technology and conventional network, and to form a safe and reliable quantum-level communication technology solution suitable for the power grid. In this paper, from the perspective of the multi-terminal quantum key application, combined with a mature electricity consumption information collection system, a handheld meter reading solution based on quantum private communication technology is proposed to effectively integrate the two and achieve technological upgrading. First, from the technical theory and application fields, the current situation of quantum private communication technology and its feasibility of combining with classical facilities are introduced and analyzed. Then, the hardware security module and handheld meter reading terminal equipment are taken as typical examples to design and realize quantum key shared storage, business security process application model; finally, based on the overall environment of quantum key distribution, the architecture design of multi-terminal quantum key application verification is implemented to verify the quantum key business application process.

The chances of cyber-attacks have been increased because of incorporation of communication networks and information technology in power system. Main objective of the paper is to prove that attacker can launch the attack vector without the knowledge of complete network information and the injected false data can't be detected by power system operator. This paper also deals with analyzing the impact of multi-attacking strategy on the power system. This false data attacks incurs lot of damage to power system, as it misguides the power system operator. Here, we demonstrate the construction of attack vector and later we have demonstrated multiple attacking regions in IEEE 14 bus system. Impact of attack vector on the power system can be observed and it is proved that the attack cannot be detected by power system operator with the help of residue check method.

By studying the problems of network information security in power system, this paper proposes a self-defense research and solution for power information network based on artificial intelligence. At the same time, it proposes active defense new technologies such as vulnerability scanning, baseline scanning, network security attack and defense drills in power information network security, aiming at improving the security level of network information and ensuring the security of the information network in the power system.

Modern cyber-physical systems are increasingly complex and vulnerable to attacks like false data injection aimed at destabilizing and confusing the systems. We develop and evaluate an attack-detection framework aimed at learning a dynamic invariant network, data-driven temporal causal relationships between components of cyber-physical systems. We evaluate the relative performance in attack detection of the proposed model relative to traditional anomaly detection approaches. In this paper, we introduce Granger Causality based Kalman Filter with Adaptive Robust Thresholding (G-KART) as a framework for anomaly detection based on data-driven functional relationships between components in cyber-physical systems. In particular, we select power systems as a critical infrastructure with complex cyber-physical systems whose protection is an essential facet of national security. The system presented is capable of learning with or without network topology the task of detection of false data injection attacks in power systems. Kalman filters are used to learn and update the dynamic state of each component in the power system and in-turn monitor the component for malicious activity. The ego network for each node in the invariant graph is treated as an ensemble model of Kalman filters, each of which captures a subset of the node's interactions with other parts of the network. We finally also introduce an alerting mechanism to surface alerts about compromised nodes.

This paper presents a methodology for utilizing Phasor Measurement units (PMUs) for procuring real time synchronized measurements for assessing the security of the power system dynamically. The concept of wide-area dynamic security assessment considers transient instability in the proposed methodology. Intelligent framework based approach for online dynamic security assessment has been suggested wherein the database consisting of critical features associated with the system is generated for a wide range of contingencies, which is utilized to build the data mining model. This data mining model along with the synchronized phasor measurements is expected to assist the system operator in assessing the security of the system pertaining to a particular contingency, thereby also creating possibility of incorporating control and preventive measures in order to avoid any unforeseen instability in the system. The proposed technique has been implemented on IEEE 39 bus system for accurately indicating the security of the system and is found to be quite robust in the case of noise in the measurement data obtained from the PMUs.

Harmonic distortions come into existence in the power system not only due to nonlinear loads of consumers but also due to custom power devices used by power utilities. These distortions are harmful to the power networks as these produce over heating of appliances, reduction in their life expectancy, increment in electricity bill, false tripping, etc. This paper presents an effective, simple and direct approach to identify the problematic cause either consumer load or utility source or both responsible for harmonics injection in the power system. This technique does not require mathematical model, historical data and expert knowledge. The online methodology is developed in the laboratory and tested for different polluted loads and source conditions. Experimental results are found satisfactory. This proposed technique has substantial potential to determine the problematic cause without any power interruption by plug and play operation just like CCTV.

The traditional electricity distribution system is rapidly shifting from the passive infrastructure to a more active infrastructure, giving rise to a smart grid. In this project an active electricity distribution network and its components have been studied. A 14-node SCADA-based active distribution network model has been proposed for managing this emerging network infrastructure to ensure reliability and protection of the network The proposed model was developed using matlab /simulink software and the fuzzy logic toolbox. Surge arresters and circuit breakers were modelled and deployed in the network at different locations for protection and isolation of fault conditions. From the reliability analysis of the proposed model, the failure rate and outage hours were reduced due to better response of the system to power fluctuations and fault conditions.

The intelligent power communication network is closely connected with the power system, and carries the data transmission and intelligent decision in a series of key services in the power system, which is an important guarantee for the smart power service. The self-healing control (SHC) of the distribution network monitors the data of each device and node in the distribution network in real time, simulates and analyzes the data, and predicts the hidden dangers in the normal operation of the distribution network. Control, control strategies such as correcting recovery and troubleshooting when abnormal or fault conditions occur, reducing human intervention, enabling the distribution network to change from abnormal operating state to normal operating state in time, preventing event expansion and reducing the impact of faults on the grid and users.

As the next generation of the power system, smart grid develops towards automated and intellectualized. Along with the benefits brought by smart grids, e.g., improved energy conversion rate, power utilization rate, and power supply quality, are the security challenges. One of the most important issues in smart grids is to ensure reliable communication between the secondary equipment. The state-of-art method to ensure smart grid security is to detect cyber attacks by deep learning. However, due to the small number of negative samples, the performance of the detection system is limited. In this paper, we propose a novel approach that utilizes the Generative Adversarial Network (GAN) to generate abundant negative samples, which helps to improve the performance of the state-of-art detection system. The evaluation results demonstrate that the proposed method can effectively improve the performance of the detection system by 4%.

Traditional power grid security schemes are being replaced by highly advanced and efficient smart security schemes due to the advancement in grid structure and inclusion of cyber control and monitoring tools. Smart attackers create physical, cyber, or cyber-physical attacks to gain the access of the power system and manipulate/override system status, measurements and commands. In this paper, we formulate the environment for the attacker-defender interaction in the smart power grid. We provide a strategic analysis of the attacker-defender strategic interaction using a game theoretic approach. We apply repeated game to formulate the problem, implement it in the power system, and investigate for optimal strategic behavior in terms of mixed strategies of the players. In order to define the utility or cost function for the game payoffs calculation, generation power is used. Attack-defense budget is also incorporated with the attacker-defender repeated game to reflect a more realistic scenario. The proposed game model is validated using IEEE 39 bus benchmark system. A comparison between the proposed game model and the all monitoring model is provided to validate the observations.

Modern advances in sensor, computing, and communication technologies enable various smart grid applications. The heavy dependence on communication technology has highlighted the vulnerability of the electricity grid to false data injection (FDI) attacks that can bypass bad data detection mechanisms. Existing mitigation in the power system either focus on redundant measurements or protect a set of basic measurements. These methods make specific assumptions about FDI attacks, which are often restrictive and inadequate to deal with modern cyber threats. In the proposed approach, a deep learning based framework is used to detect injected data measurement. Our time-series anomaly detector adopts a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM) network. To effectively estimate system variables, our approach observes both data measurements and network level features to jointly learn system states. The proposed system is tested on IEEE 39-bus system. Experimental analysis shows that the deep learning algorithm can identify anomalies which cannot be detected by traditional state estimation bad data detection.

Aiming at the realization of power system visualization plane topology modeling, a development method of Microsoft Foundation Classes application framework based on Microsoft Visual Studio is proposed. The overall platform development is mainly composed of five modules: the primitive library module, the platform interface module, the model array file module, the topology array file module, and the algorithm module. The software developed by this method can realize the user-defined power system modeling, and can realize power system operation analysis by combining with algorithm. The proposed method has a short development cycle, compatibility and expandability. This method is applied to the development of a plane topology modeling platform for the distribution network system, which further demonstrates the feasibility of this method.

Recently, the security of state estimation has been attracting significant research attention due to the need for trustworthy situation awareness in emerging (e.g., industrial) cyber-physical systems. In this paper, we investigate secure estimation based on Kalman filtering (SEKF) using partially homomorphically encrypted data. The encryption will enhance the confidentiality not only of data transmitted in the communication network, but also key system information required by the estimator. We use a multiplicative homomorphic encryption scheme, but with a modified decryption algorithm. SEKF is able to conceal comprehensive information (i.e., system parameters, measurements, and state estimates) aggregated at the sink node of the estimator, while retaining the effectiveness of normal Kalman filtering. Therefore, even if an attacker has gained unauthorized access to the estimator and associated communication channels, he will not be able to obtain sufficient knowledge of the system state to guide the attack, e.g., ensure its stealthiness. We present an implementation structure of the SEKF to reduce the communication overhead compared with traditional secure multiparty computation (SMC) methods. Finally, we demonstrate the effectiveness of the SEKF on an IEEE 9-bus power system.

Traditionally, power grid vulnerability assessment methods are separated to the study of nodes vulnerability and edges vulnerability, resulting in the evaluation results are not accurate. A framework for vulnerability assessment is still required for power grid. Thus, this paper proposes a universal method for vulnerability assessment of power grid by establishing a complex network model with uniform weight of nodes and edges. The concept of virtual edge is introduced into the distinct weighted complex network model of power system, and the selection function of edge weight and virtual edge weight are constructed based on electrical and physical parameters. In addition, in order to reflect the electrical characteristics of power grids more accurately, a weighted betweenness evaluation index with transmission efficiency is defined. Finally, the method has been demonstrated on the IEEE 39 buses system, and the results prove the effectiveness of the proposed method.

As a modern power transmission network, smart grid connects plenty of terminal devices. However, along with the growth of devices are the security threats. Different from the previous separated environment, an adversary nowadays can destroy the power system by attacking these devices. Therefore, it's critical to ensure the security and safety of terminal devices. To achieve this goal, detecting the pre-existing vulnerabilities of the device program and enhance the terminal security, are of great importance and necessity. In this paper, we propose a novel approach that detects existing buffer-overflow vulnerabilities of terminal devices via automatic static analysis (ASA). We utilize the static analysis to extract the device program information and build corresponding program models. By further matching the generated program model with pre-defined vulnerability patterns, we achieve vulnerability detection and error reporting. The evaluation results demonstrate that our method can effectively detect buffer-overflow vulnerabilities of smart terminals with a high accuracy and a low false positive rate.

In recent years, there has been a significant increase in wind power penetration into the power system. As a result, the behavior of the power system has become more dependent on wind power behavior. Supervisory control and data acquisition (SCADA) systems responsible for monitoring and controlling wind farms often have vulnerabilities that make them susceptible to cyberattacks. These vulnerabilities allow attackers to exploit and intrude in the wind farm SCADA system. In this paper, a cyber-physical system (CPS) model for the information and communication technology (ICT) model of the wind farm SCADA system integrated with SCADA of the power system is proposed. Cybersecurity of this wind farm SCADA system is discussed. Proposed cyberattack scenarios on the system are modeled and the impact of these cyberattacks on the behavior of the power systems on the IEEE 9-bus modified system is investigated. Finally, an anomaly attack detection algorithm is proposed to stop the attack of tripping of all wind farms. Case studies validate the performance of the proposed CPS model of the test system and the attack detection algorithm.

Technological developments in the energy sector while offering new business insights, also produces complex data. In this study, the relationship between smart grid and big data approaches have been investigated. After analyzing where the big data techniques and technologies are used in which areas of smart grid systems, the big data technologies used to detect attacks on smart grids have been focused on. Big data analytics produces efficient solutions, but it is more critical to choose which algorithm and metric. For this reason, an application prototype has been proposed using big data approaches to detect attacks on smart grids. The algorithms with high accuracy were determined as 92% with Random Forest and 87% with Decision Tree.