Grid Communication Research Articles

Anomaly Detection of Cyber Attacks in Smart Grid Communications Based on Residual Recurrent Neural Networks

ABSTRACTThe smart grid consists of widely distributed devices, and the complex, multitiered communication network structure increases its vulnerability to attacks. Attackers can exploit communication vulnerabilities between layers to launch network attacks. To optimize communication among smart grid devices and improve the accuracy of detecting network anomalies, this article proposes a deep learning‐based method for detecting communication network anomalies in power grid devices. Data from standard sources, such as smart meters, are collected, and the modified flow direction algorithm (MFDA) is employed for optimal weighted feature selection. The selected features are then input into an adaptive residual recurrent neural network (ARRNN) with dilated gated recurrent units (DGRUs) for anomaly detection, improving the accuracy of abnormal traffic monitoring in the smart grid. Simulation results show significant improvements in key performance indicators, with the false discovery rate (FDR) reduced to 0.060, the Matthews correlation coefficient (MCC) reaching 0.881, and specificity, recall, and precision all at 0.940. Large‐scale data experiments demonstrate outstanding performance in terms of memory usage, computational speed, and latency. This method proves to be effective and robust for communication anomaly detection in power grid networks.

Enhancing unmanned aerial vehicle and smart grid communication security using a ConvLSTM model for intrusion detection

The emergence of small-drone technology has revolutionized the way we use drones. Small drones leverage the Internet of Things (IoT) to deliver location-based navigation services, making them versatile tools for various applications. Unmanned aerial vehicle (UAV) communication networks and smart grid communication protocols share several similarities, particularly in terms of their architecture, the nature of the data they handle, and the security challenges they face. To ensure the safe, secure, and reliable operation of both, it is imperative to establish a secure and dependable network infrastructure and to develop and implement robust security and privacy mechanisms tailored to the specific needs of this domain. The research evaluates the performance of deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), CNN-LSTM, and convolutional long short-term memory (ConvLSTM), in detecting intrusions within UAV communication networks. The study utilizes five diverse and realistic datasets, namely, KDD Cup-99, NSL-KDD, WSN-DS, CICIDS 2017, and Drone, to simulate real-world intrusion scenarios. Notably, the ConvLSTM model consistently achieves an accuracy of 99.99%, showcasing its potential in securing UAVs from cyber threats. By demonstrating its superior performance, this work highlights the importance of tailored security mechanisms in safeguarding UAV technology against evolving cyber threats. Ultimately, this research contributes to the growing body of knowledge on UAV security, emphasizing the necessity of high-quality datasets and advanced models in ensuring the safe, secure, and reliable operation of UAV systems across various industries.

Fog Cloud Computing Based on Lightweight Scheme for Secured Smart Grids

The integration of fog and cloud computing in smart grids presents a promising approach to enhance the security and efficiency of energy management systems. This paper proposes a lightweight cryptographic scheme tailored for fogcloud architectures to secure smart grid communications. The scheme leverages the distributed nature of fog computing to reduce latency while maintaining robust security measures against cyber threats. By incorporating quantum-resistant cryptographic techniques, the proposed solution addresses the unique challenges of data confidentiality, integrity and privacy in smart grids. Our approach is designed to be resource-efficient, making it suitable for deployment in environments with limited computational power, such as smart meters and Internet of Things (IoT) devices. Comprehensive performance evaluations demonstrate that the proposed scheme not only enhances security but also improves the overall efficiency and reliability of smart grid operations. The findings suggest that this lightweight scheme is a viable solution for securing next-generation smart grids, providing a balance between security and operational demands. Performance evaluation demonstrates that the scheme provides secure and efficient authentication while minimising computational and communication overheads.

Communication Solutions for the Last Mile of Smart Grid: Neighborhood Area Networks in Smart Grid Communications: Standards and Challenges

Communication Solutions for the Last Mile of Smart Grid: Neighborhood Area Networks in Smart Grid Communications: Standards and Challenges

Data protection enhancement in smart grid communication: An efficient multi-layer encrypting approach based on chaotic techniques and steganography

In this study, a multi-layer encryption system is proposed through which sensitive and important data in smart grids can be secured from unauthorized persons as well as cyber threats. The suggested system is analyzed using fundamental performance assessment metrics say as average processing times across ten runs, peak signal-to-noise ratio (PSNR), and root mean square error (RMSE). The encryption process involves hiding the data encrypted by a digital dictionary and the advanced encryption standard (AES) algorithm in an image. In this way, AES encryption suggests rapid processing with a short average time per each block; similarly, the decryption technique, which includes the dictionary and AES decryption also renders the results in a justified computational time nominating it as a practical approach. It is worth noting that the small key era and dictionary construction instances provoke innovations in the general efficiency of the encryption mechanism. The conducted study, based on recorded values for PSNR, RMSE, UACI, and NPCR, demonstrates that the proposed multi-layer encryption system, which incorporates dictionary encryption and AES, is a robust and a fast method to shield important information and attaining a balance between protection and computational time.

Two-stage optimal scheduling for flexibility and resilience tradeoff of PV-battery building via smart grid communication

Energy flexibility and energy resilience are now becoming new key features of building energy systems under the context of climate change and energy transition. During the system operation phase, these two performance indexes might be contradictory and require tradeoff. The main contribution of this study is to propose a two-stage mixed-integer linear programming (MILP) model to optimally tradeoff between flexibility and resilience. Its main idea is to improve the resilience of building energy system with minimum constraints on system flexibility using the outage risk information provided by smart grid. Two new concepts are considered in the proposed method, including self-sufficient requirement and continuous outage probability. The insight is to add additional penalty for the time step in which its battery state of charge (SOC) is far from self-sufficient requirement while the corresponding continuous outage probability is high. To validate our proposed method, a probabilistic outage simulation model is developed using sigmoid function and Markov Chain. Comprehensive numerical studies are conducted to compare the proposed method with traditional economic mode and backup mode under two outage patterns. The results demonstrate that the proposed method only uses 6.7 % additional operation cost such that 78.3 % of baseload curtailment and 81.1 % of user discomfort are reduced. The proposed MILP model can provide practical guideline for the flexibility and resilience tradeoff of distributed energy resources.

Research on Digital Twin Technology for Enhancing Power Communication System Performance

Abstract Digital twin technology simulates physical systems through real-time data and virtual models, providing an innovative solution to challenges in power communication systems, such as multiple access interference (MAI) and the near-far effect. By constructing a digital twin model, we can accurately measure the non-orthogonality of each user’s spread spectrum code, enabling the elimination of mutual interference through matrix inversion or iterative methods. Our simulations show that the application of digital twin technology improves system performance by reducing MAI and stabilizing communication quality under complex conditions. When the number of stages increases, the system performance improves, but when it is greater than level 5, the system performance does not improve significantly, so the level 5 is selected in the practical application. In this case, the interference factor of the grid communication system is equal to 0.55, and the resulting system gain is 2.

Joint Cooperative Computation and Communication for Demand-Side NOMA-MEC Systems With Relay Assistance in Smart Grid Communications

Joint Cooperative Computation and Communication for Demand-Side NOMA-MEC Systems With Relay Assistance in Smart Grid Communications

Mitigating adversarial cascades in large graph environments

A significant amount of society’s infrastructure can be modeled using graph structures, from electric and communication grids, to traffic networks, to social networks. Each of these domains are also susceptible to unwanted cascades, whether this be overloaded devices in the power grid or the reach of a social media post containing misinformation. The potential harm of a cascade is compounded when considering a malicious attack by an adversary that is intended to maximize the cascade impact. However, by exploiting knowledge of the cascading dynamics, targets with the largest cascading impact can be preemptively prioritized for defense, and the damage an adversary can inflict can be mitigated. While game theory provides tools for finding an optimal preemptive defense strategy, existing methods struggle to scale to the context of large graph environments because of the combinatorial explosion of possible actions with the size of the graph. Data-driven deep learning approaches can fill this gap, but they demand a large amount of data that is often costly to obtain. We propose a novel data generation approach for the cascading failure security problem that uses counterfactual data augmentation and a sub-game data querying strategy to generate both high quality and a high quantity of training data. We demonstrate that our data generation approach creates up to an order of magnitude more data than naïve querying with double the efficiency. We also introduce a deep learning model based embedding node pairs, and train it on our generated data, showing that it effectively defends from cascades on graphs as large as 1000 nodes. Our deep learning approach achieves a closer approximation to the Nash Equilibrium than existing heuristic methods, for graphs where the ground-truth equilibrium can be calculated.

Application and Optimization of Multi-Factor Authentication and Biometric Technology in Power Grid Energy Network Access Control

With the sudden development in electronic and network technology, the power grid has emerged in several evolved nations and other areas with faster development. Energy storage can enhance the stability, resiliency and efficiency of the electric smart grid. The power grid transfers energy via bidirectional control of energy and information flow. In the power grid, a smart meter is an important component that is deployed on the user side to determine the consumption of energy regularly. However, the data about the usage of electricity leads to the leakage of the private information of the user, which threatens the privacy of the user. The attackers can change the in-transit message induce wrong information and sometimes cause disastrous action. Hence, it is important to assure data security and privacy of users by conducting authentication in power grid communication. An optimization technique named Puzzle Optimization Algorithm (POA) is designed for the efficient privacy-preserving aggregation approach with multi-factor authentication and a biometric scheme. Also, optimal key-based Elliptic Curve Cryptography (ECC) is used to encrypt the sensitive data that are securely stored. Authentication offers high security and texture analysis. In the registration phase, the privacy information of the person with their biometrics is the important feature point of the individual unique identification. Additionally, the designed approach is compared with other existing optimizations to analyse the functionality of the implemented technique. The simulation outcome defined that the suggested technique enhances user privacy and information security than other standard approaches.

Smart grid data compression and reconstruction by wavelet packet transform

A smart grid is a power network from generation to consumers and provides beneficial, steady, and safe electricity. It utilizes smart meters for billing, phasor measurement units to check the system's health., etc. As a result, it contains enormous volumes of real-time data that may be shared and stored by users, control centers, and services in a smart grid. It weakens the smart grid's communication networks. The size of the data in a smart grid will grow extremely in the future. As a result, it must reduce distortion in data compression and denoise while minimizing the demand on storage and communication networks. The goal of data compression and denoising should be to maximally conserve the useful data while accurately reflecting the state of the system and providing sufficient data regeneration at the receiving end. This paper has used lower-order different wavelets to represent a design to compress and reconstruct data at level three using wavelet Packet Transform. It works on the phasor measurement unit's current magnitude and voltage sag signals.•The proposed design has a better compression ratio.•Low reconstruction error.•This design is easy to access, systematic, profitable, and not time-consuming.

A blockchain‐based resilient and secure framework for events monitoring and control in distributed renewable energy systems

AbstractThe rapid and green energy transition is essential to deal with the fast‐growing energy needs in both public and industrial sectors. This has paved the way to integrate distributed renewable energy resources () such as solar, hydro, wind, and geothermal into the power grid (). Wind and solar are free, zero‐carbon emission, and everlasting power sources that contribute 5% and 7% of global electricity generation, respectively. Therefore, the fast, secure, and reliable integration of these green is critical to achieve the instant energy demands. Smart grid due to inherited characteristics such as intelligent sensing, computing, and communication technologies can effectively integrate the . However, the existing smart grid communication architecture faces various cyberattacks, resulting in poor integration, monitoring, and control of . In this respect, blockchain technology can provide fast, secure, and efficient end‐to‐end communication between in the smart grid. In this study, the authors propose a blockchain‐based resilient and secure scheme called for wireless sensor networks ‐based events monitoring and control in . Experimental studies and performance analyses are carried out to predict the efficiency of the proposed scheme by considering numerous standard metrics. The extensive numerical results demonstrated that the proposed scheme is significant in terms of secure, resilient, and reliable information transmission for in .

An Enhanced Authentication and Key Agreement Protocol for Smart Grid Communication

An Enhanced Authentication and Key Agreement Protocol for Smart Grid Communication

An efficient neighbor nodes trust tagging model for optimized route detection in smart grids with secure data transmission

Smart Grids are the control and transmission networks of the future; they allow suppliers and customers to interact in real time to optimize and intelligently manage resources. Although wireless mesh network technology can facilitate these smart functionalities, it is important to address the vulnerabilities and cyber-attack risks that are inherent to it. Smart Grid's reliance on the Internet amplifies security concerns. A number of methods have been proposed to address this issue; while some have made promises, they all need substantial amounts of computational resources. With this technology, channels based on trust can be set up. The security of the system is built utilizing a family relationship-based method, which makes use of measures that may be used to gauge a node's originality. Additionally, the power consumption and signal strength of the node are taken into account. The complexity and extent of the network need the development of new types of smart grid communication. Finding a way to detect and thwart major assaults on routing protocols is another design challenge. Smart grids rely on these protocols in its data system for efficient interchange of renewable energy. A unique secure energy routing mechanism is developed in this proposed model for secure data communication. In the proposed model, a new method for Neighbor Nodes Trust Tagging Model for Optimized Route Detection (NNTT-ORD) is proposed for establishing secure route for data communication in smart grids. The proposed model is compared with the existing model and the results represent that the proposed model route provides a secure environment for data transmission.

An improved lightweight and privacy preserving authentication scheme for smart grid communication

Smart grid is an efficient and reliable technical framework for controlling computers and automation equipment, and how to ensure the communication security in smart grid is an important issue. Cryptographic authentication scheme is a feasible solution, and the existing authentication schemes for smart grid seek to ensure better performance. Some existing authentication schemes lack comprehensive security considerations and have security or privacy vulnerabilities, which makes them vulnerable to specific attacks. The paper reviews a recent scheme ISG-SLAS (Yu and Park, 2022) and analyzes its potential insecure aspects in detail, including unable to resist ESL attack, cannot provide un-traceability and etc. To this end, the paper designs an improved authentication scheme for smart grid based on symmetric cryptography. Through informal security analyses and formal security analyses with real-or-random (ROR) model and Scyther platform, the security of the proposed scheme is demonstrated. From the perspective of performance, the proposed scheme is compared with ten advanced authentication schemes for smart grid, and the results show that the proposed scheme excels other recent schemes in computational overhead, communication overhead and storage overhead, reduced by 10.1%, 30.8% and 36.1% of ISG-SLAS and 58.9%, 64.1% and 24.3% of the average value of all alternatives respectively.

Leveraging 5G Network Capabilities for Smart Grid Communication

This paper presents a comprehensive investigation into the architecture and components of 5G networks, focusing on their suitability for smart grid applications. With the increasing complexity and demands of modern energy infrastructure, there is a growing need for reliable and low-latency communication systems to support critical smart grid operations. This paper explores key features of 5G networks, including network slicing, massive MIMO (Multiple Input Multiple Output), and low-latency communication protocols like URLLC (Ultra-Reliable Low Latency Communication), and examines how these features can be harnessed to address the unique requirements of smart grid environments. Through a detailed analysis of each component, this paper sheds light on the design considerations, deployment strategies, and performance implications of leveraging 5G technology for smart grid communication. Furthermore, practical insights, challenges, and future research areas are discussed to guide the development of next-generation smart grid solutions.

CBGA: A deep learning method for power grid communication networks service activity prediction

CBGA: A deep learning method for power grid communication networks service activity prediction

Metabolites: a converging node of host and microbe to explain meta-organism.

Meta-organisms encompassing the host and resident microbiota play a significant role in combatting diseases and responding to stress. Hence, there is growing traction to build a knowledge base about this ecosystem, particularly to characterize the bidirectional relationship between the host and microbiota. In this context, metabolomics has emerged as the major converging node of this entire ecosystem. Systematic comprehension of this resourceful omics component can elucidate the organism-specific response trajectory and the communication grid across the ecosystem embodying meta-organisms. Translating this knowledge into designing nutraceuticals and next-generation therapy are ongoing. Its major hindrance is a significant knowledge gap about the underlying mechanisms maintaining a delicate balance within this ecosystem. To bridge this knowledge gap, a holistic picture of the available information has been presented with a primary focus on the microbiota-metabolite relationship dynamics. The central theme of this article is the gut-brain axis and the participating microbial metabolites that impact cerebral functions.

A Literature Review on Access Control in Networking Employing Blockchain

Access Control (AC) in networking attempts to make sure that only authorized devices perform actions formed upon privileges defined for them with a view to prevent malicious users' entry and interaction in the communication grid. Blockchain solutions contain an arrangement of related blocks that naturally safeguards the trustworthiness, defending the incontestability, defend masked-identity of its transactions/transfers due to scattered consensus strategies and cryptographic solutions. Our survey comprehensively reviews BC-formed AC in broad scope of networking considering AC techniques while breaking down into 4 propositions and assessing them in terms of blockchain roles, AC technique and approach, network elements, and rest. We stockpiled a primary sample of 79 bibliographic references by weeding out them for screening criteria sought from scientific information reservoirs exploiting a qualitative and extensive strategy. Formed upon this survey, in blockchain-formed AC, blockchain can be exploited as an AC manager to administrate network devices and access information, implement automatic AC by means of smart contracts, secure storage of AC related data to reinforce overall AC security, and for safe data exchanging in the operation of AC. Minute assessment highlights that from blockchain-formed AC, 52.5% provide AC using blockchain itself or using smart contracts, 92.5% exploit sequential blockchain, 35% exploit PBFT consensus, provide 100% fine-grained and host-formed AC, 85% decentralized AC, 87.5% have single-factor authentication, 92.5% provide dynamic AC, and 45% have opted for IoT. Finally, we evaluate the chances and difficulties of the principle of blockchain-formed AC and then giving recommended actions to beat them.

Detection of False-Reading in Smart Grid Net-Metering System

Malicious consumers may hack their smart meters (SMs) in the smart grid to report fake readings in order to make unlawful financial profits. Since the reported readings are used for energy management, the utility suffers significant financial losses as a result, and grid performance may suffer as a result. The net metering system, in which one SM is used to report the difference between the power consumed and the power generated, is the subject of this research, which is the first study to look into the issue. First, we process real power consumption and generation information to create a benign dataset for the net-metering system. The data was then examined, and time correlations between the net meter readings and correlations between the measurements and pertinent information from reliable sources, such as temperature, were discovered. Based on the data analysis, we suggest a single-pole double-throw detector to detect erroneous readings. In addition to data from reliable sources, our detector is trained on net meter readings from every customer in order to improve its performance by discovering correlations between them. We also create a relationship between the customer and the grid. Since the only billing system available nowadays is online, this project includes an app that may be used to solve a variety of problems, including electrical outages. Overall, with this, we improve customer and grid communication and simplify their lives.